IoT and Cloud Computing in Biological Water Monitoring: A Systematic Review of Challenges, Architectures, and Emerging Trends

IoT and Cloud Computing in Biological Water Monitoring: A Systematic Review of Challenges, Architectures, and Emerging Trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review IoT and Cloud Computing in Biological Water Monitoring: A Systematic Review of Challenges, Architectures, and Emerging Trends Ronewa Nethanani, Yoren Ndou, Wilson Nchabeleng, Sifundo Ndlovu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6848919/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of Internet of Things (IoT) and cloud computing technologies has revolutionized biological water quality monitoring by enabling high-resolution, real-time sensing and data analytics. Despite the potential, various challenges persist in deployment, data management, scalability, and security. This systematic review investigates the current landscape of IoT and cloud-enabled biological water monitoring, identifying commonly used technologies, architectural models, and recurring limitations while highlighting opportunities for advancement. A total of 17,872 records were screened from Google Scholar, Web of Science, and Scopus, of which 80 studies met inclusion criteria. The review adhered to PRISMA guidelines, and data were extracted and categorized across themes including cloud platforms, microcontrollers, communication protocols, system architectures, and security mechanisms. Most studies were published between 2020 and 2023, with Google Scholar contributing 60% of the included records. Custom/private cloud servers (45%) were the most used backend platforms, while ThingSpeak (13.75%) and AWS IoT (8.75%) were notable open/cloud-based solutions. Hardware trends favored Arduino-based (16.25%) and general microcontroller-based systems (15%), with ESP32-based and Raspberry Pi platforms also widely adopted. Major implementation barriers included connectivity issues (15.05%), GSM/Zigbee congestion (11.83%), and deployment cost (8.6%). HTTP (28.75%) and API (22.5%) were dominant communication methods, with MQTT used in 17.5% of cases. Architecture-wise, over half of the systems followed a cloud-only model, while hybrid and embedded systems remained underutilized. Alarmingly, 55% of studies did not report security mechanisms, and 50% lacked explicit privacy measures; when reported, encryption (17.5%) and data anonymization (30%) were the most common. The integration of IoT and cloud technologies in biological water monitoring is maturing, yet significant challenges remain—particularly in standardization, energy efficiency, and security. The review underscores the urgent need for context-aware architectures, transparent reporting, and stronger emphasis on privacy-by-design. Future work should leverage edge computing, AI integration, and standardized frameworks to enhance scalability, accuracy, and sustainability in aquatic monitoring systems. Marine and Freshwater Biology Chemical Engineering Internet of Things (IoT) Cloud Computing Water Quality Monitoring Environmental Sensors Data Security Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Early observation is critical for understanding and responding to the biological impacts triggered by eutrophication, such as nutrient-driven phytoplankton development and aquatic biodiversity alterations. Static bounded data models cannot monitor evolving dynamics caused by eutrophication. Owing to excess nutrients' more destructive impact on biological balance, continuous monitoring acquires supreme priority for early diagnosis and countermeasures (Chang, Imen and Vannah, 2015 ; Kandasamy et al., 2025 ). The emergence of IoT and cloud technologies has significantly transformed environmental monitoring by enabling real-time, high-resolution data collection and processing. IoT platforms, such as water-based sensors, provide continuous environmental data (Sujatha, Reza and Ranganathan, 2024 ), which are immediately transmitted to cloud servers via wireless networks like 2G (Mishra et al., 2020 ). These data are then integrated and processed using cloud-based infrastructures—ranging from social and sensor clouds to computation clouds—for enhanced analysis and decision-making (Mishra et al., 2020 ). The integration of satellite, drone, and in-situ sensor data further supports the use of microservices architectures for flexible and scalable processing • (Skoufias, 2022 ), while platforms like ArcGIS and web-based servers facilitate centralized data storage and access [4]. Environmental and socioeconomic conditions play a significant role in the intensification and expansion of toxic algal blooms (HABs). Global climate change, through warming temperatures, increased stratification, and intense precipitation, promotes CyanoHABs and is accountable for their rising frequency and extension of their ranges (Gobler, 2020 ; Chapra et al., 2017 ). Eutrophication, driven mainly by anthropogenic nutrient loads from agriculture, aquaculture, and industry, further promotes bloom events, as evidenced in cases like the ZJB (Zhang et al., 2022 ; Chapra et al., 2017 )These flowers have serious effects on water bodies, public health, and the regional economy through polluting the drinking water supply, disrupting fisheries, and causing huge economic losses to aquaculture (Gobler, 2020 ; Chapra et al., 2017 ). Biological water monitoring using algae is a classical method of water quality assessment, as algae respond rapidly to environmental changes and to pollution levels. As bioindicators, different groups of algae point to definite trophic states—green algae and diatoms are characteristic of oligotrophic (nutrient-poor) conditions, and blue-green algae (Cyanophyta) point to eutrophication (Khalil et al., Rocha, 1992 ). Phytoplankton and zooplankton are sentinels of aquatic health, and their changes in abundance and community composition signal changes in water chemistry and the occurrence of pollutants (Chandel et al., 2024 ; Rocha, 1992 ). The predominance of some groups, including diatoms and cyanobacteria, has rendered them important tools in assessing aquatic ecosystem status and identifying nutrient enrichment (Khalil et al., 2021 ; Rocha, 1992 ). The Internet of Things (IoT) plays a transformative role in algae monitoring by enabling real-time, high-frequency data collection through interconnected sensors and devices. These systems can continuously track critical water quality parameters such as turbidity, pH, temperature, dissolved oxygen, and water level using sensors connected to Wi-Fi-enabled microcontrollers like the ESP32, which transmit updates every few seconds to databases and mobile apps (Bodaragama et al., 2024 ). This rapid data acquisition supports the development of machine learning models for predicting harmful algal bloom (HAB) events, overcoming the limitations of traditional, labor-intensive, and less responsive monitoring methods (Kwon et al., 2023 ; Busari et al., 2023 ). As a result, IoT-based monitoring systems offer a cost-effective and scalable solution for timely detection and management of HABs (Kwon et al., 2023 ; Busari et al., 2023 ). Cloud computing plays a vital role in algae monitoring by enabling the remote storage, access, and analysis of water quality data collected from IoT-enabled devices. Measurements taken by sensors submerged in lakes or rivers are transmitted to cloud platforms like Wia, allowing users to view real-time data on smartphones or computers from any location. This seamless integration supports efficient data management and facilitates timely analysis and response to changing water conditions (Tziortzioti et al., 2019 ). Modern IoT-based and AI-integrated algae monitoring systems significantly outperform traditional methods in both accuracy and efficiency. Advanced machine learning models such as SVM, random forest, and decision trees, when paired with sensor data and techniques like PCA, have achieved accuracy rates of up to 100%, surpassing conventional nonlinear models and manual monitoring approaches (Lee, 2025 ). These systems also enable real-time analysis, low-power operation (e.g., 29 W), and edge computing capabilities, reducing reliance on constant cloud connectivity and lowering operational costs. As a result, they present a cost-effective, scalable alternative to labor-intensive traditional methods with minimal error margins and rapid processing (Lee, 2025 ; Esty et al., 2025 ). Interoperability and standardization are essential for ensuring seamless communication and integration among the diverse components of IoT-based algae monitoring systems. While specific references to standardization in algae-focused IoT were not found, broader technological enablers like cloud computing and advanced communication networks support interoperability by enabling fast, secure, and reliable data transmission between users, sensors, and cloud systems. Cloud platforms also enhance scalability, resource integration, and secure data access—critical for managing large and varied datasets from distributed sources. Technologies like Docker further ensure consistent deployment environments, contributing to standardized and secure system operations (Esty et al., 2025 ). Most IoT studies emphasize physicochemical parameters (pH, turbidity, DO) with comparatively few incorporating biological assays or in situ algal community profiling (Forhad et al., 2024 ). Technical challenges (connectivity, data security) and ecological complexities (species interactions, trophic dynamics) are often addressed in isolation rather than via integrated frameworks (Ubina et al., 2023 ). Effective algae monitoring underpins SDG 6 (Clean Water and Sanitation) by enabling early detection of water quality threats and guiding remediation efforts. Rapid adoption of IoT and cloud platforms in environmental monitoring has outpaced the development of best-practice guidelines, risking fragmented implementations and data silos. Bridging engineering innovations with ecological understanding is essential to design robust, adaptive monitoring systems that address both technical feasibility and environmental variability. Systematically map existing IoT and cloud-based algae monitoring solutions, highlighting their strengths and limitations. Integrate findings from sensor technology, data analytics, and aquatic ecology to propose holistic monitoring frameworks. Offer practical recommendations for scalable architecture designs, interoperability standards, and context-aware deployment strategies to advance the field (Strigaro, Capelli and Cannata, 2024 ). 1.1 Research questions With the growing use of IoT and cloud technology in environmental monitoring systems, various issues related to data management, access in real-time, and long-term dependability have been raised. In biological water monitoring, there are certain issues that must be discussed at the research level to provide assurance that the systems that are installed are not only operational, but also sustainable and versatile in various use scenarios and environments What are the most critical infrastructural and technical issues of the integration of IoT and cloud computing technologies with biological water monitoring systems? In what ways do geographical, economic, and environmental factors influence the scalability and efficiency of cloud-integrated IoT systems in actual deployment? What are technologies, approaches, and architecture patterns that have been proposed to address concerns such as energy usage, data protection, and system interoperability in cloud-IoT technology? Analysis of existing technological solutions can caution best practices and can assist in charting solutions based on a weighing of cost, complexity, and efficiency (Sujan et al., 2021 ). What are some of the new trends or opportunities in smart environmental monitoring that can be harnessed to enhance biological water quality monitoring, especially in automation and data analysis? Against the backdrop of advancements in edge computing, machine learning and decentralized networks, the question looks at new possibilities for enhancing accuracy, responsiveness, and maintainability (Sharma et al., 2023). 1.2. Rationale The reason to conduct this literature systematic review stems from the growing need for effective, real-time water quality monitoring systems capable of operating in changing conditions and generating actionable data for researchers, policymakers, and stakeholders. IoT platforms, if integrated with cloud infrastructures, deliver unparalleled levels of access to real-time analysis and historical archiving of data. Yet, notwithstanding such promise, real-world implementations often fall short because integration difficulties or technological limitations are readily forgotten (Mandal et al., 2024; Bera et al., 2024). Lots of studies have pointed out that even though IoT-enabled systems can be deployed at relatively modest cost, most of them cannot be run continuously due to battery constraints, unreliable cloud connectivity or poor sensor calibration (Chandrashekar et al., 2023; Adhikari & Saikia, 2022). On top of that, data security concerns and the lack of standard protocol for device-to-device communication add layers of complexity, especially in multi-site or collaborative research setups. This review responds to the need for a full synthesis of the issues and opportunities within this domain, providing insight not only into technical limitations but also into contextual limitations such as geographic unreachability and cost. It is intended to guide future deployments and research efforts by de-mystifying what has been studied, what has been tried in terms of solutions, and where the most critical gaps remain (Singh & Kumar, 2025; Sharma et al., 2025). 1.3. Objectives The general purpose of the current systematic literature review is to critically analyze the application of Internet of Things (IoT) and cloud computing technologies in biological water monitoring. With the environmental conditions in the entire world deteriorating due to climate change, eutrophication, industrial pollution, and agricultural runoff, the need for smart, adaptive, and dynamic systems for water quality monitoring has become increasingly important. The review aims to explore how Internet of Things (IoT) devices, in this case, low-power sensors such as those utilizing the ESP32 microcontroller, combined with cloud computing infrastructure can enable real-time monitoring of biological water quality parameters such as turbidity, temperature, pH, and total dissolved solids (TDS). These technologies can make a revolution by offering high-definition continuous data that is remotely viewable and can be analyzed, eliminating the limitation of the traditional manual sampling methods. The second primary objective is to research and examine the challenges of using IoT and cloud technology in water systems. Among those are sensor accuracy and calibration issues, power in remote locations, data security and privacy concerns, communication latency, and the lack of a unified protocol for interoperability of data. Integrating evidence from over 80 peer-reviewed articles found between 2015 and 2025, this review endeavors to learn about how such challenges impact the validity and credibility of such technologies once deployed in real-world applications. In addition to defining these constraints, the review discusses new possibilities facilitated by emerging technologies such as edge computing, artificial intelligence, low-power wide-area networks, and hybrid cloud-edge architectures. These can potentially enhance data analytics ability, reduce energy requirements, and make system response time faster, all of which are essential for the detection and management of biological threats such as toxic algal blooms. Finally, this review seeks to present actionable, evidence-based suggestions to system designers, policymakers, and researchers. By presenting an overview of the state of the art and highlighting the opportunity and limitation in current solutions, the review contributes towards the making of more adaptive, sustainable, and intelligent water quality monitoring systems to meet international aspirations like SDG 6: Clean Water and Sanitation. 1.4. Research Contribution By filling most of the gaps in the body of literature of studies, this systematic review makes a multidimensional contribution to the research of IoT and cloud-integrated biological water quality monitoring: Many of the researched studies employ literature analysis alone and are not tested empirically, though several of them propose promising designs and integrated solutions for water monitoring using IoT, AI, and cloud platforms. This study adds to the literature by making the identification of the actual-world impediments—in the sense of deployment expense, calibration problems, and environmental limitations—which bar the utilization or experiment test of these theoretical models in actual-world environments. It stresses that case studies and pilot runs should be utilized as a foundation for further research. The impacts of regional conditions on technological feasibility of IoT-cloud solutions such as weather exposure, rural availability, or local economic constraints are not considered in a number of different assessments. By contrasting facts with a sense of context-awareness and distinguishing which solutions will and will not be geographically transferable, this review improves on this point. Through contextualization, this gives an improved framework on which to better inform judgments around technological feasibility to researchers and policymakers. There are few studies examining system-level issues like energy consumption, interoperability, data privacy, and real-time synchronization despite several studies debating the feasibility of cloud and IoT integration. By categorizing and assessing these neglected hindrances, this study contributes by introducing a categorization of system-level faults preventing complete deployment. Relative maturity of various architectural elements is also discussed. In addition to recognizing roadblocks, this review charts new frontiers, specifically in edge computing, LPWANs, AI integration, and hybrid system architecture. This paper contributes by connecting mutually distinct technologies, such as the development of sensors or big data pipelines, and how a hybridized structure could increase sustainability, scalability, and responsiveness in real-time water quality monitoring systems. 1.5. Research Novelty Few papers have dealt with the synergistic issues and system-level issues that arise when integrating these technologies, although many papers have evaluated the advantage of integrating cloud computing or the Internet of Things in water monitoring systems. Focusing on practical deployment issues like energy limitations, data integrity, sensor deployment, and communication latency, this paper is the first to fully address this convergence. Moreover, this paper combines technical depth with contextual examination, considering how physical location, regulatory environments, and user ability affect system functionality and sustainability. This contrasts with previous reviews that either generalize IoT application scenarios or focus on each of the individual components like sensor calibration or data storage. Materials and Methods This section outlines the methodology used in the study. It begins with the Eligibility Criteria (Section 2.1), which define specific guidelines for including or excluding studies to ensure only relevant research is considered. Information Sources (Section 2.2 ) identifies the databases used to locate studies, emphasizing key academic repositories. The Search Strategy (Section 2.3 ) details the keywords and techniques applied to retrieve appropriate studies. In the Selection Process (Section 2.4 ), studies are initially screened to verify compliance with the eligibility criteria. Next, the Data Collection Process (Section 2.5 ) involves systematically extracting essential information from the selected studies. Data Items (Section 2.6 ) specify the types of data gathered for further evaluation. To ensure reliability, Study Risk of Bias (Section 2.7 ) assesses potential bias or limitations in the included studies. Effect Measures (Section 2.8 ) describe how the impact of various elements was evaluated. The Synthesis Methods (Section 2.9 ) explain how the results across studies were integrated. Reporting Bias (Section 2.10 ) investigates any selective reporting of outcomes, and Certainty Assessment (Section 2.11) evaluates the confidence level in the overall evidence. In the Results (Section 3), findings from the study are presented. Results of Study Selection (Section 3.1 ) outline how many studies were included and the reasons behind their selection. Study Characteristics (Section 3.2 ) highlight key attributes of these studies. Risk of Bias in Studies (Section 3.3 ) notes any methodological flaws. Results of Individual Studies (Section 3.4) present the outcomes of each study, while Results of Syntheses (Section 3.5) compile the findings into a broader perspective. Reporting Biases (Section 3.6) reassess possible selective reporting, and Certainty of Evidence (Section 3.7) indicates the overall reliability of the combined results. The Discussion (Section 4) critically interprets the findings in relation to IoT and cloud computing applications in biological water monitoring, highlighting key insights, challenges, and emerging trends identified across the reviewed studies. It explores the practical significance of these findings within the context of real-time water quality monitoring and environmental management. Section 5, Practical Recommendations, offers targeted suggestions for researchers and practitioners, focusing on system scalability, data security, and integration strategies for IoT-cloud architectures. The review concludes with a final summary, emphasizing the study's overall contribution to advancing knowledge and guiding future research in sustainable water monitoring technologies. 2.1. Eligibility Criteria To make sure that only relevant and high-quality studies were included in this review, clear eligibility rules were used to select the materials. The focus of this research is on how the Internet of Things (IoT) and Cloud Computing are used in biological water monitoring, especially the issues, challenges, and opportunities. Only studies that clearly discuss these technologies within the context of biological water monitoring were included. In addition, the selected studies needed to have a clear research framework or methodology, particularly one that shows how technology affects biological systems or water quality analysis (Khan et al., 2021). Only articles written in English were used, as this is the main language of scientific communication and it avoids issues with translation that may lead to misinterpretation (Khanyi et al., 2024 ; Thobejane et al., 2024; Skosana et al, 2024 ; Mkhize et al., 2025 ). Also, the review only considered studies published between 2015 and 2025. This time range was chosen to reflect the most recent advancements in IoT and Cloud Computing, which have rapidly evolved in the past decade (Khanyi et al., 2024 ; Thobejane et al., 2024; Skosana et al, 2024 ; Mkhize et al., 2025 ). Studies that did not talk about IoT or Cloud Computing in relation to biological water monitoring, that lacked a clear methodology, were not in English, or were published outside the chosen time frame were excluded from the review. Table 2 Proposed Inclusion and Exclusion Criteria. Criteria Inclusion Criteria Exclusion Criteria Topic Focuses on IoT and Cloud Computing Issues Challenges and Opportunities in Biological Water Monitoring Studies not IoT and Cloud Computing Issues Challenges and Opportunities in Biological Water Monitoring Research Framework Must include a clear research framework or methodology Lacks a framework or methodology relevant to biological impacts Language Must be written in English Published in other languages Publication Period Published between 2015 and 2025 Outside the specified period 2.2 Information Source The data for this study was collected from a diverse array of peer-reviewed articles, conference papers, and technical studies published between 2015 and 2025. These sources were obtained from established academic databases such as Web of Science, SCOPUS, and Google Scholar, ensuring scholarly rigor and relevance. The selection included research focusing on IoT and Cloud Computing applications in water quality monitoring. Table 2 Summarizes the online research databases consulted, emphasizing the role of each source and how it aligns with the study’s inclusion and exclusion criteria to ensure methodological rigor. Table 3 Summary of Online Research Repositories Used. Database Access Platform Inclusion/Exclusion Criteria Applied Purpose of Use Google Scholar Browser Yes Ensure broad coverage across multidisciplinary sources Scopus Open Athens (UJ Oline Library) Yes Accesses high-quality, peer-reviewed journal articles Web of Science Open Athens (UJ Oline Library) Yes Provides publications with strong research impact and citations 2.3. Search Strategy To ensure a comprehensive collection of literature related to the topic " IoT and Cloud Computing Issues, Challenges, and Opportunities in Biological Water Monitoring", a systematic search was conducted using three primary academic databases: Scopus, Web of Science, and Google Scholar. These databases were selected due to their extensive coverage of peer-reviewed scientific and technical publications relevant to the domains of Internet of Things (IoT), cloud computing, and environmental monitoring. The search strategy employed a carefully constructed combination of keywords and Boolean operators designed to retrieve relevant studies. The main search terms included:("IoT" OR "Internet of Things") AND ("cloud computing") AND ("biological water monitoring" OR "water quality" OR "biological contamination" OR "bacteria" OR "microbial") AND ("issues" OR "challenges" OR "opportunities").The search was limited to publications that met the inclusion criteria, namely studies published between 2015 and 2025, written in English, and explicitly addressing the issues, challenges, or opportunities associated with IoT and cloud computing in the context of biological water monitoring. Studies were excluded if they did not focus on this intersection, lacked a clear research framework or methodology, were outside the specified date range, or were published in languages other than English. The retrieved studies were exported to Microsoft Excel for duplication and screening, after which they proceeded to the eligibility and full-text review stages as described in Section 2.4 . The process is summarized in Fig. 1 . Additionally, a PRISMA flow diagram was suggested to visually document the study selection process, providing a clear and transparent overview of the search and screening process. 2.4. Selection Process The screening and selection of relevant literature followed a structured approach to ensure the quality and relevance of the studies included in this review. Articles were initially retrieved from SCOPUS, Web of Science, and Google Scholar, and all records were exported to Microsoft Excel for organization, duplication, and documentation. The inclusion criteria specified that articles must focus on IoT and cloud computing issues, challenges, and opportunities in biological water monitoring. Only studies published between 2015 and 2025 were considered, and eligible works were required to be written in English. Furthermore, each selected article had to include a clear research framework or methodology relevant to the integration of IoT and cloud computing technologies in monitoring biological parameters such as microbial activity, bacterial content, or other biological indicators in water systems. Studies were excluded if they did not specifically address the intersection of IoT and cloud computing in biological water monitoring, or if they were focused on unrelated technological or environmental domains. Articles that lacked a relevant methodological framework, were published outside the 2015–2025-time range or were written in languages other than English were also omitted. The screening process began with a title and abstract review to eliminate clearly irrelevant records, followed by a full-text analysis to assess alignment with the inclusion criteria. This rigorous filtering ensured that only studies with strong methodological grounding and direct relevance to the review topic were selected for further analysis. To enhance the reliability of the findings, a team of four reviewers independently conducted the screening process. Each study was assessed by at least two reviewers. To further minimize potential bias, a fifth independent reviewer was designated to resolve any disagreements or inconsistencies through consensus-building. This structured and collaborative approach ensured a transparent, rigorous, and unbiased selection process. The diagram below visually summarizes the selection process (Khanyi et al., 2024 ; Thobejane et al., 2024; Skosana et al, 2024 ; Mkhize et al., 2025 ). 2.5 Data Collection Process Data for this review was gathered through Google Scholar, Web of Science, and Scopus, with three independent reviewers participating to guarantee accuracy, reliability, and reduce possible biases. Figure 4 depicts the data collection process, outlining each stage to improve clarity and replicability. At first, every reviewer independently gathered data from the chosen studies, concentrating on key aspects like study features, results, and particular IoT-cloud computing metrics pertinent to biological water monitoring. This deliberate extraction method, intentionally free from automation tools, facilitated a thorough and detailed approach. After individual extraction, the reviewers performed cross-checks to ensure consistency and precision. Any differences found during this phase were addressed through direct conversations, guaranteeing a cohesive dataset. In cases where several reports related to one study, particular decision criteria were used to choose the most thorough and current information. After verifying accuracy and completeness, the gathered data was compiled into an Excel database for final validation and further analysis. 2.6 Data Items In line with the research focus on IoT and cloud computing issues, challenges, and opportunities in biological water monitoring, a structured approach was adopted for extracting key data items from the selected literature. Each study was assessed based on specific technical and contextual parameters critical to the implementation of smart water monitoring systems. The data items extracted included: study title, year of publication, and the source database (Google Scholar, Scopus, or Web of Science), as well as the type of cloud platform used (e.g., AWS IoT, Azure, ThingSpeak, Blynk, or custom servers). Additional attributes involved integration with microcontrollers, data upload methods (such as MQTT, HTTP, or API), and data storage mechanisms (e.g., SQL, JSON, or real-time databases).To evaluate architectural efficiency, the studies were categorized based on whether they used edge processing, cloud-only, or hybrid architectures. Security mechanisms were also documented, including encryption standards, authentication protocols, and access control measures. Privacy concerns, such as data anonymization and mitigation techniques, were considered, along with latency handling and the system’s real-time responsiveness. Special attention was given to scalability challenges (e.g., network constraints, energy limitations, and hardware issues) and opportunities for future development, including the integration of AI, system expansion, or predictive analytics capabilities.This multidimensional data extraction framework enabled a consistent and comparative evaluation across the literature, as seen in prior systematic technology reviews (Myataza et al, 2024 ; Gumede et al, 2024 ; Mudau et al., 2024 ; Mtjilibe et al, 2024 ). Such detailed categorization helped identify trends, limitations, and future potential within IoT-cloud systems tailored for biological water quality monitoring.In total, 80 studies were initially retrieved from three major academic repositories 48 from Google Scholar, 24 from Web of Science, and 8 from Scopus (Table 4 ). Following the application of inclusion and exclusion criteria, 4 studies were selected for detailed analysis and scoring based on the established evaluation matrix. 2.6.1. Data Collection Method The data collection process in this study followed a systematic and structured methodology to ensure that relevant literature was accurately identified, filtered, and analyzed in alignment with the research objectives. The process was divided into five key phases (Myataza et al, 2024 ; Gumede et al, 2024 ; Mudau et al., 2024 ; Mtjilibe et al, 2024 ). Following the selection of relevant studies, data were extracted using a standardized template. This template captured both bibliographic information (including Study ID, Title, Year, and Repository) and technical parameters such as the cloud platform used (e.g., AWS IoT, Azure, ThingSpeak), microcontroller integration, system architecture (cloud, edge, or hybrid), communication protocols (e.g., MQTT, HTTP, API), and data storage methods (SQL, JSON, or real-time databases). In addition, each study was analyzed for key technological factors, including scalability challenges (e.g., network and hardware limitations), opportunities for future expansion (e.g., AI integration or system scalability), and system capabilities in terms of latency, real-time monitoring, and responsiveness. Security and privacy features were also evaluated, with attention to encryption protocols, authentication mechanisms, data anonymization, and compliance with regulatory standards.To enable comparative analysis, a scoring matrix method was used to rate each study across a set of defined technical and functional criteria. This scoring facilitated the classification of literature into three analytical themes issues, challenges, and opportunities highlighting current trends and identifying technological gaps in the use of IoT and cloud computing in biological water quality monitoring. This structured method ensured the reliability, depth, and reproducibility of the data collection process. 2.6.2. Collected Data Variables Definition In this study, several key variables were identified and extracted from the selected literature to evaluate the integration of IoT and cloud computing in biological water monitoring. These variables were chosen based on their relevance to system performance, scalability, data handling, and the overall effectiveness of the monitoring architecture. Paper ID: A unique identifier assigned to each reviewed study to facilitate organization and reference during the analysis. Title: The full title of the research paper as published. Year of Publication: The year in which the study was officially published, providing temporal context for the technology used. Research Database: Indicates the database or repository (e.g., Google Scholar, Scopus, Web of Science) where the study was retrieved, reflecting the study's accessibility and indexing. Cloud Platform Used: Specifies the cloud service (e.g., ThingSpeak, Blynk, AWS IoT, Azure, or custom solutions) used for data storage, analysis, and visualization. Integration with Microcontroller: Details the type of hardware (e.g., Arduino, ESP32, Raspberry Pi) used for sensing and processing at the edge. Scalability Challenges: Identifies any technical or infrastructural issues that hinder system scalability, such as limited network coverage or energy constraints. Opportunities for Future Expansion: Outlines prospects for system enhancement, such as AI integration, predictive analytics, or broader geographic deployment. Data Upload Method: Describes the protocol used to transmit sensor data to the cloud (e.g., MQTT, HTTP, API), which impacts latency and efficiency. Data Storage Mechanism: Indicates the format or system used to store collected data (e.g., SQL, JSON, real-time databases), influencing retrieval and analysis capabilities. Edge vs. Cloud Processing Architecture: Specifies whether processing occurs primarily at the edge, in the cloud, or in a hybrid model. Security Mechanisms: Details the presence of encryption, authentication methods, and other security protocols to protect transmitted and stored data. Privacy Concerns and Mitigations: Notes any attention given to user or environmental data privacy, including anonymization techniques or compliance with regulatory standards. Latency and Real-Time Response Metrics: Reports on the system’s responsiveness, such as data refresh rate or delay in alerts, which is crucial for real-time monitoring scenarios. Link to Full Text: Provides a URL or DOI for direct access to the full paper, ensuring transparency and verifiability. These variables were systematically extracted and tabulated to enable comparative scoring and synthesis, guiding the evaluation of current technological approaches and identifying areas for future research and innovation in IoT-based water quality monitoring systems. Table 4 Data Variables Collected. Field Description Study characteristics Deployment region, monitoring environment, sample size, IoT/cloud architecture, and communication protocol. Intervention characteristics Includes key performance metrics (sensor accuracy, data latency, uptime, energy use), their alignment with monitoring goals, and scalability impacts on system growth. Economic factors Includes the investment valuation of IoT/cloud deployments, the competitive advantages gained, and the overall ROI. External influences Includes the vendor ecosystem, adoption and demand trends, analytics-algorithm updates, emerging technological innovations, and environment‐specific monitoring drivers. 2.7. Effect Measures It is important to specify the effect measures used for each outcome when compiling results in IoT and cloud-based biological water monitoring to ensure accuracy and clarity in the results presentation. Setting criteria for study inclusion is an essential element of any statistical synthesis, as it involves making subjective decisions that may affect the results. These decisions need to be made clearly. For example, systematic methods like tabulating and coding important features like monitoring environments, system architectures, and performance metrics can help determine which studies are eligible for synthesis. For instance, studies assessing the efficacy of various IoT-cloud implementations may be coded based on standards like real-time data transmission, energy efficiency, or system scalability. For every synthesized outcome, effect measures (i.e., risk ratios for categorical outcomes, and mean differences for continuous outcomes) should be reported. This makes it as easy as possible to compare and comprehend different monitoring system configurations and how they influence the performance and reliability of biological water quality assessments. In that way, reviewers can ensure that the synthesis accurately reflects the data collected by providing a transparent basis for their conclusions and by revealing the techniques used to select and group studies for synthesis, including the criteria and coding methods. 2.8. Synthesis Methods . 2.8.1. Eligibility Assessment and Study Selection Criteria for Synthesis The synthesis process for evaluating the application of IoT and cloud computing in biological water monitoring involved systematically organizing studies into key thematic areas (Myataza et al, 2024 ; Gumede et al, 2024 ; Mudau et al., 2024 ; Mtjilibe et al, 2024 ). This was accomplished by careful screening and data extraction, in accordance with the specified inclusion and exclusion criteria detailed in Table 6 . The criteria guaranteed that every study concentrated on the significance of IoT and cloud computing in biological water monitoring, encompassed a research framework, was published in English, and was within the publication period of 2015 to 2025. Research that met these criteria was gathered in an Excel spreadsheet, where information was extracted and arranged based on the categories presented in Table 6 . Table 6 Synthesis-Specific Grouping. Category Data Extracted Study Details Paper ID, Research Title, Year of Publication, Online Database Contextual Information Cloud Platform Used, Integration with Microcontrollers, Scalability Challenges, Opportunities for Future Expansion Methods of Information Data Upload Method, Data Storage Mechanism, Edge vs. Computing Processing Architecture, Security Mechanisms, Privacy Concerns and Mitigations Outcomes and Impacts Latency and Real-time Response Metrics The eligibility process for synthesis included multiple organized steps to guarantee consistency and transparency. Initially, studies were evaluated based on the inclusion and exclusion criteria to ensure they match the review’s emphasis on IoT, and cloud computing uses in biological water monitoring. This evaluation examined relevance, the presence of a research framework, language, and adherence to publication date. After the primary screening, a thorough extraction of data was conducted, systematically documenting study specifics, contextual elements, methodologies, and results. The extracted features were subsequently compared to established synthesis categories, including system design, energy efficiency, data latency, and scalability, enabling precise classification. For studies that demonstrated significant results in various areas or seemed like borderline cases, subjective evaluations were conducted to categorize them into the most pertinent synthesis group according to their main contribution. This organized method guaranteed that the classification for synthesis was clear, uniform, and indicative of the studies' influence on IoT and cloud-connected water quality monitoring systems 2.8.2. Data Preparation and Processing Methods for Synthesis To maintain consistency and comparability among the data obtained from the analyzed studies, particular data preparation and transformation techniques were employed. These techniques tackled problems associated with absent data and inconsistencies in data formats, guaranteeing that all information was consistently arranged for presentation and synthesis. The methods for data preparation for review are listed in Table 7 . Table 7 Data Preparation Methods for Review. Criteria Methods Used Handling Missing Information Studies lacking essential information were excluded from the review. For studies providing data in ranges (e.g., survey responses between 90–120 participants), midpoint estimates were used to standardize the figures. Data Conversions Fractions and percentages were converted to decimals using Microsoft Excel, ensuring uniformity and facilitating direct comparisons across all data points. 2.8.3. Methods for Tabulating and Visualizing Study Results To successfully convey the findings of this systematic review, we utilized a mix of tabular and graphical approaches as outlined in Table 8 . These methods were chosen to guarantee a clear, understandable, and thorough presentation of the results concerning IoT and cloud computing applications in biological water monitoring. We arranged tables to systematically organize and compare essential findings from various studies. These tables contained information on study contributions, advantages, challenges of IoT-cloud integration, and effects on biological monitoring systems. To emphasize relevance, tables were arranged according to factors such as publication year and citation counts, showcasing the most impactful studies in the field. Microsoft Excel and Microsoft Word were utilized to produce visual displays, such as pie charts, graphs, and flow charts. Pie charts depict the distribution of studies focusing on different elements, including system architecture, performance indicators, or implementation issues. Graphs presented trends over time and distributions of study results, facilitating the recognition of patterns and relationships. Flowcharts illustrated the study selection and synthesis process, providing a clear visual overview of the methodological steps carried out. These visual tools were created to improve clarity, enabling readers to swiftly grasp essential insights and trends. By combining these visual tools with comprehensive tabulation, we sought to deliver a sophisticated and easily understandable summary of the compiled data. Table 8. Data Presentation and Synthesis Workflow. Steps Description 1. Data Collection Collect raw data from reviewed studies. 2. Data Preparation Addresses missing information and perform data conversions. 3.Tabulation Methods Structure tables to include study contributions, benefits, challenges, and impacts; order tables by publication year and citation count. 4.Graphical Methods Create pie charts, graphs, and flow charts to visually represent study selection and outcome distribution. 5.Presentation of Results Combine tabular and graphical methods to offer a comprehensive and transparent view of findings. 6.Review and finalize Review for completeness and accuracy; prepare results for inclusion in the review. 2.8.4. Methods for Data Synthesis and Meta-Analysis Due to the considerable variability among the studies included, a conventional meta-analysis was considered unfeasible. Rather, structured summaries and descriptive statistics were employed to consolidate the findings, as this method allowed for the differences in study designs and outcome measures. Microsoft Excel and Microsoft Word aided in the creation of tables, graphs, pie charts, and flow charts, which efficiently structured and showcased the varied data, enabling the recognition of patterns and trends. The organized summaries provided a thorough insight into the advantages and difficulties of integrating IoT and cloud computing in biological water monitoring, showcasing the variety of contexts and results in the research. This synthesis approach provided a clear and approachable display of results, considering the distinct features of every study. 2.8.5. Investigation of Heterogeneity Sources To investigate possible sources of heterogeneity in the study outcomes, we performed subgroup analyses that assessed differences according to factors like deployment context, monitoring goals, and particular IoT-cloud system designs. For example, we classified studies based on their monitoring settings (e.g., aquaculture, freshwater lakes, or industrial effluent monitoring) to examine whether the efficacy of IoT-cloud integration differed among applications. We furthermore examined how the system configuration (e.g., cloud-only versus hybrid architecture) affects monitoring results. These subgroup analyses facilitated the comparison of outcomes across varying levels of each factor, employing statistical interaction tests to assess whether the noted effects significantly varied among subgroups. Since a meta-analysis could not be conducted because of missing standardized effect estimates, results were compiled in tables to facilitate a visual evaluation of how these subgroup factors affected the effectiveness and challenges of IoT-cloud systems across various biological monitoring scenarios. Although these analyses offered important insights, they were exploratory instead of being pre-defined in our protocol. Thus, the results must be viewed carefully, considering the constraints of the existing data and the techniques used. 2.8.6. Sensitivity Analyses The review conducted a detailed examination of the main technology providers of the leading providers—AWS, Microsoft Azure, and Google Cloud—considering deployment modes such as on-premises, cloud-based, and hybrid environments relevant to biological water monitoring. It examined the methodological paradigms of the reviewed studies, such as study designs (quantitative, qualitative, or mixed methods), sample sizes, and participant characteristics, to provide an image of methodological diversity across the literature. Methods of data collection—such as interviews, questionnaires, direct observation, and document analysis—were evaluated, as were analytical methods used, ranging from statistical analysis to thematic interpretation. When synthesizing among studies, performance measures by IT parameters (such as data processing speed, scalability, and precision of the system), business parameters (such as operational efficiency, cost, and revenue effect), and organizational parameters (such as user satisfaction and stakeholder involvement) were looked at. Long-term effects like strategic advantage and sustained growth were also considered. By structuring the literature in a systematic manner along these axes, the research was able to discern emerging trends, overcome methodological heterogeneity, and lay a solid groundwork for future research agendas. 2.9. Reporting Bias Assessment To ensure the credibility and reliability of this systematic review on IoT and cloud computing challenges and opportunities in biological water monitoring, a careful assessment of potential reporting bias was conducted. Reporting bias refers to the selective revealing or suppression of research findings, which can lead to a skewed understanding of the topic (Higgins et al., 2019). In this study, efforts were made to minimize such bias by adhering to a comprehensive and transparent search strategy that included multiple reputable databases such as Google Scholar, Web of Science, and SCOPUS. To further address bias, inclusion and exclusion criteria were consistently applied across all records, and study selection was independently verified. The full texts were reviewed not only for relevance but also for the completeness of reported technical details such as cloud platform usage, data protocols, scalability challenges, and security mechanisms. Where potential publication bias was suspected such as the overrepresentation of studies with positive outcomes or commercial cloud integrations it was noted during data extraction. Any missing or unclear data was acknowledged rather than excluded to avoid selective reporting.By maintaining transparency in the study identification, screening, and synthesis process, this review aimed to mitigate the effects of reporting bias and provide a balanced overview of current trends and gaps in IoT-cloud integration for water quality monitoring. 2.10. Certainty Assessment To make sure the findings in this review are trustworthy, each study was carefully checked for how strong and reliable its evidence is. This was done by looking at several things: how clearly the study used cloud technology, what kind of communication protocols it used (like MQTT or HTTP), the system design (whether it used edge, cloud, or both), and whether it talked about security and privacy. We also looked at how well the system handled real-time data, if it could grow to handle more devices (scalability), and whether other people could repeat the work based on what was written. If a study explained all of these clearly and in detail, it was rated as high certainty. If the study left out important details or had unclear methods, it was rated as medium or low certainty. This way of checking evidence follows the updated guidelines suggested by Schünemann et al. (2022), which help make sure that technology reviews are fair and consistent. Results This section synthesizes findings from a diverse body of studies examining the integration of IoT and cloud computing technologies within biological water monitoring systems. The review highlights that IoT, and cloud platforms together create a transformative technological framework capable of enabling real-time, high-resolution monitoring of aquatic environments. These technologies enhance data acquisition, transmission, storage, and analysis, thereby addressing key limitations of traditional water quality monitoring methods. Research across the reviewed literature reveals that cloud integrated IoT systems significantly improve the scalability, responsiveness, and automation of biological monitoring networks. Such systems support the continuous tracking of vital biological indicators such as algal blooms, microbial content, and nutrient levels by linking remote sensing devices with cloud-based analytical platforms. Moreover, the adoption of cloud computing not only facilitates centralized data management and remote access but also strengthens decision-making through real-time visualization and predictive modeling. The results further underscore the importance of architectural choices, including edge-cloud hybrid systems, as well as considerations around security, data latency, energy efficiency, and interoperability. These technical and operational dimensions collectively influence the reliability and sustainability of IoT-enabled water monitoring solutions. Through a systematic literature synthesis, this review identifies best practices, recurring challenges, and emerging opportunities that can guide future deployment and research in the field of smart water quality monitoring. 3.1. Study Selection Results Figure 7 presents the PRISMA-compliant flow diagram outlining the systematic review’s screening and inclusion process. A total of 17,872 records were identified from three databases, with Google Scholar contributing the majority. After title and abstract screening, 17,792 records were excluded based on relevance and predefined exclusion criteria. All 80 remaining reports were retrieved and assessed for eligibility, ultimately being included in the final systematic literature review (SLR). This rigorous selection process ensured a focused, high-quality dataset for analysis of IoT and cloud computing applications in biological water monitoring. 3.2. Study Results As shown in Fig. 8 , scholarly interest in the integration of IoT and cloud computing for biological water monitoring has seen notable fluctuations over the past decade. The number of relevant studies peaked in 2020 with 21 publications, followed by a secondary peak in 2023 with 12 studies. This trend suggests a growing recognition of the critical role these technologies play in environmental monitoring, particularly in the context of increased global attention to water security and sustainable development goals. The decline in 2024 and 2025 may reflect either a saturation of foundational research or a shift toward more specialized, empirical implementation studies that fall outside broad review criteria. Figure 9 displays the distribution of academic sources used in this systematic review. The majority of studies were retrieved from Google Scholar (60%), followed by Scopus (30%) and Web of Science (10%). This reflects a broad, inclusive search strategy that prioritizes accessibility and multidisciplinary coverage in identifying relevant IoT and cloud computing studies for biological water monitoring. Figure 10 highlights the diversity of cloud platforms employed in the reviewed literature. A significant proportion of studies (45%) utilized custom or private cloud solutions, reflecting a preference for configurable and secure architectures tailored to specific monitoring requirements. Open platforms like ThingSpeak accounted for 13.75% of implementations, while commercial solutions such as AWS IoT, Blynk, and Azure collectively made up less than 15% of the total. Notably, over a quarter of the studies did not specify the backend used, indicating gaps in reporting and potential reproducibility concerns. Figure 11 illustrates the variety of microcontroller and sensor platforms employed in the reviewed studies. Arduino-based systems led in usage (16.25%), closely followed by general-purpose microcontroller setups (15%), often customized for specific environmental monitoring scenarios. ATmega and PIC microcontrollers represented a notable 11.25%, valued for their simplicity and low power demands. ESP-based systems (8.75%) and Raspberry Pi platforms (7.5%) were also popular for their processing power and wireless capabilities. The figure reflects a preference for accessible, low-cost, and modular solutions, which are essential for scalable IoT deployments in biological water monitoring. Figure 12 summarizes the range of technical challenges identified across the reviewed literature. General network and coverage issues were cited most frequently (15.05%), underscoring the persistent difficulty of ensuring reliable connectivity in remote or aquatic environments. GSM/Zigbee bandwidth constraints (11.83%) and LoRa limitations (9.67%) were also frequently mentioned, reflecting trade-offs between communication range, payload size, and energy consumption. Other barriers included infrastructure-related costs (8.6%), scalability issues, and inconsistent communication protocols—all of which hinder the broader adoption of IoT-cloud systems for real-time biological water monitoring. Figure 13 presents the distribution of communication technologies implemented in IoT-based water monitoring systems. Web protocols such as HTTP were the most utilized (28.75%), reflecting their ease of integration with cloud services and dashboards. API-based interfaces accounted for 22.5%, enabling modular data exchange across platforms. MQTT, a lightweight IoT-specific protocol, was used in 17.5% of cases due to its efficiency in bandwidth-limited environments. Surprisingly, nearly 14% of studies did not specify the communication method, underscoring the need for more transparent technical reporting in future research. As depicted in Fig. 14 , system architecture in IoT-enabled biological water monitoring varies considerably. Over half of the reviewed studies (52.94%) implemented cloud-centric designs, leveraging cloud platforms for real-time processing and storage. Hybrid edge-cloud solutions were observed in 23.53% of cases, supporting more responsive local analytics. A smaller portion of studies employed embedded-only architectures or unspecified configurations, indicating room for further development in adaptive and distributed processing frameworks tailored to energy-constrained or bandwidth-limited field environments. As shown in Fig. 15 , a significant proportion (55%) of the reviewed studies lacked explicit mention of security or privacy mechanisms, underscoring a major gap in reporting and system robustness. Where measures were reported, encryption (17.5%) and authentication (6.25%) were most common, followed by access control, secure communication, and token-based authorization. Advanced techniques like federated privacy and cryptographic privacy frameworks were rarely employed, reflecting the early stage of maturity in securing IoT-cloud biological monitoring infrastructures. As illustrated in Fig. 16 , data privacy remains an underreported aspect in IoT-cloud environmental monitoring systems. Half of the studies reviewed made no mention of privacy mechanisms, raising concerns about data sensitivity and regulatory compliance. Among those that did, data anonymization was the leading technique (30%), followed by general privacy awareness initiatives (11.25%). Minimal adoption of access control and other advanced privacy frameworks highlights the need for more rigorous privacy-by-design strategies in future deployments. 3.3. Study reearch questions results 1. What are the most critical infrastructural and technical issues of the integration of IoT and cloud computing technologies with biological water monitoring systems? The most critical technical issues identified include limited network coverage (15.05%), GSM and Zigbee bandwidth constraints (11.83%), and LoRa limitations (9.67%). These hinder reliable data transmission, especially in remote or aquatic environments. Deployment cost was also cited as a major challenge (8.6%). Furthermore, 55% of studies failed to specify any security protocols, and 50% omitted privacy mechanisms entirely, indicating a widespread lack of attention to data protection. Additionally, many systems lacked architectural clarity—26.25% did not specify which cloud platform was used, and 14% did not describe the communication protocols—affecting system reproducibility and standardization. 2. In what ways do geographical, economic, and environmental factors influence the scalability and efficiency of cloud-integrated IoT systems in actual deployment? Geographical and environmental factors significantly affect the scalability of IoT systems. Poor connectivity in rural or underwater locations limits the functionality of GSM, Wi-Fi, and even LoRa modules. Economically, regions with limited funding opt for open-source or low-cost platforms—reflected by the 45% use of custom/private clouds and 13.75% use of ThingSpeak. Environmentally, sensor durability is impacted by factors such as algae growth, water chemistry, and temperature fluctuations. These issues demand adaptive, context-specific systems that can perform reliably under varying local conditions. 3. What are technologies, approaches, and architecture patterns that have been proposed to address concerns such as energy usage, data protection, and system interoperability in cloud-IoT technology? To address energy and latency concerns, 23.53% of studies implemented hybrid edge-cloud architectures, reducing data transmission loads by enabling local processing. For communication, MQTT (17.5%) emerged as a preferred low-power, IoT-optimized protocol. In terms of security and privacy, encryption (17.5%) and authentication (6.25%) were the most common methods used, though underreported. Microcontrollers like ESP32 and low-power platforms such as Arduino and Raspberry Pi were frequently used (together over 30%), due to their balance between functionality and energy efficiency. Interoperability, however, remains a challenge due to the lack of standard communication frameworks across studies. 4. What are some of the new trends or opportunities in smart environmental monitoring that can be harnessed to enhance biological water quality monitoring, especially in automation and data analysis? Emerging trends include the use of AI and machine learning models—such as SVM, PCA, and random forest—to predict harmful algal blooms (HABs) with high accuracy. The increasing use of edge computing allows real-time, in-field analytics, reducing cloud dependency. Open cloud platforms like ThingSpeak and mobile-friendly dashboards improve user access and public engagement. However, most studies have yet to adopt advanced privacy-preserving analytics or federated learning, signaling future opportunities. These innovations offer promising avenues to enhance responsiveness, scalability, and intelligence in biological water monitoring systems. Conclusions This systematic review synthesized findings from 80 studies published between 2015 and 2025 on the integration of IoT and cloud computing in biological water monitoring. The review highlights both technological advancements and critical gaps in current implementations. Custom or private cloud platforms were the most frequently used (45%), while ThingSpeak (13.75%) and AWS IoT (8.75%) represented notable open-access and commercial alternatives. On the hardware side, Arduino-based (16.25%) and general-purpose microcontrollers (15%) dominated deployments. Communication protocols were highly varied, with HTTP (28.75%), API interfaces (22.5%), and MQTT (17.5%) being the most utilized. However, over 55% of the studies did not specify any security mechanisms, and 50% lacked any mention of privacy-preserving measures—despite the sensitive nature of environmental data. Encryption (17.5%) and data anonymization (30%) were the most common techniques where privacy and security were reported. In terms of architectural design, more than half (52.94%) of the systems relied solely on cloud-based processing, with limited uptake of edge or hybrid edge-cloud configurations that are essential for reducing latency and improving system resilience. Network limitations (15.05%), GSM/Zigbee bandwidth issues (11.83%), and high deployment costs (8.6%) were the top barriers reported. Moving forward, robust, context-aware architectures that integrate edge computing, AI-based analytics, and lightweight protocols are necessary. Future systems must prioritize not only functionality and cost-effectiveness but also data protection and scalability. To enable this, standardized reporting practices, open benchmarks, and cross-disciplinary collaboration will be essential in advancing smart water quality monitoring systems aligned with SDG 6: Clean Water and Sanitation. References Adebayo AI, Olubanjo KT, Fadeke AM, Uyanah JJ, Zirra AT, Igbaoreto WA, Fakoyede PD (2024) From static sampling to dynamic insights: The future of water quality monitoring with sensors, IOT, and drones. Sci World J 20(1):454–466 Adeleke IA, Nwulu NI, Ogbolumani OA (2023) A hybrid machine learning and embedded IoT-based water quality monitoring system, vol 22. Internet of things, p 100774 Agbehadji IE, Mabhaudhi T, Botai J, Masinde M (2023) A systematic review of existing early warning systems’ challenges and opportunities in cloud computing early warning systems. Climate, 11(9), p.188 Ajith JB, Manimegalai R, Ilayaraja V (2020) February. An IoT based smart water quality monitoring system using cloud. In 2020 International conference on emerging trends in information technology and engineering (ic-ETITE) (pp. 1–7). IEEE Alharbe N, Atkins AS, Champion J (2015) Use of cloud computing with wireless sensor networks in an Internet of Things environment for a smart hospital network. eTELEMED 2015, p.63 Alprol AE, Mansour AT, Ibrahim MEED, Ashour M (2024) Artificial intelligence technologies revolutionizing wastewater treatment: Current trends and future prospective. Water, 16(2), p.314 Amador-Castro F, González-López ME, Lopez-Gonzalez G, Garcia-Gonzalez A, Díaz-Torres O, Carbajal-Espinosa O, Gradilla-Hernández MS (2024) Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication. Journal of Environmental Management, 352, p.119959 Andrade GA, Esteban S, Risco-Martín JL, Chacón J, Besada-Portas E (2024) Modeling-and Simulation-Driven Methodology for the Deployment of an Inland Water Monitoring System. Information, 15(5), p.267 Arratia B, Rosas E, Calafate CT, Cano JC, Cecilia JM, Manzoni P (2024) AlLoRa: Empowering environmental intelligence through an advanced LoRa-based IoT solution. Comput Commun 218:44–58 Azhari M, Abarda A, Ettaki B, Zerouaoui J, Dakkon M (2020) Higgs boson discovery using machine learning methods with pyspark. Procedia Comput Sci 170:1141–1146 Bandara RMPNS, Jayasignhe AB, Retscher G (2025) The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review. Sensors, 25(6), p.1918 Barabde M, Danve S (2015) Real time water quality monitoring system. Int J Innovative Res Comput Communication Eng 3(6):5064–5069 Bodaragama BT, Miyurangana EM, Jayakod YTL, Vipulasiri DD, Rajapaksha US, Krishara J (2024) April. IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1–6). IEEE Bokingkito PB Jr, Llantos OE (2017) Design and implementation of real-time mobile-based water temperature monitoring system. Procedia Comput Sci 124:698–705 Busari I, Sahoo D, Harmel RD, Haggard BE (2023) A review of machine learning models for harmful algal bloom monitoring in freshwater systems. J Nat Resour Agricultural Ecosyst 1(2):63–76 Cabra J, Castro D, Colorado J, Mendez D, Trujillo L (2017) June. An IoT approach for wireless sensor networks applied to e-health environmental monitoring. In 2017 IEEE international conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 578–583). IEEE Capella JV, Bonastre A, Ors R, Peris M (2019) A new application of internet of things and cloud services in analytical chemistry: Determination of bicarbonate in water. Sensors, 19(24), p.5528 Chandel P, Mahajan D, Thakur K, Kumar R, Kumar S, Brar B, Sharma D, Sharma AK (2024) A review on plankton as a bioindicator: A promising tool for monitoring water quality. World Water Policy 10(1):213–232 Chang NB, Imen S, Vannah B (2015) Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Crit Rev Environ Sci Technol 45(2):101–166 Chapra SC, Boehlert B, Fant C, Bierman VJ Jr, Henderson J, Mills D, Mas DM, Rennels L, Jantarasami L, Martinich J, Strzepek KM (2017) Climate change impacts on harmful algal blooms in US freshwaters: a screening-level assessment. Environ Sci Technol 51(16):8933–8943 Chauhan AS, Panikulangara L, Jain R (2022) September. JAL MITRA water quality monitoring and reporting using IoT, cloud & analytics. In 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 376–381). IEEE Chavhan N, Bhattad R, Khot S, Patil S, Pawar A, Pawar T, Gawli P (2025) APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater. Digital Chemical Engineering, 14, p.100217 Chowdury MSU, Emran TB, Ghosh S, Pathak A, Alam MM, Absar N, Andersson K, Hossain MS (2019) IoT based real-time river water quality monitoring system. Procedia Comput Sci 155:161–168 Dalal S (2023) August. Machine learning model for water quality evaluation: Systematic review. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 1013–1018). IEEE Das B, Jain PC (2017) July. Real-time water quality monitoring system using Internet of Things. In 2017 International conference on computer, communications and electronics (Comptelix) (pp. 78–82). IEEE Deekshath R, Dharanya P, Kabadia MKD, Dinakaran MGD, Shanthini S (2018) IoT based environmental monitoring system using arduino UNO and thingspeak. Int J Sci Technol Eng 4(9):68–75 Skosana S, Mlambo S, Madiope T, Thango B (2024) Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review. Preprints 2024101331 Mkhize A, Mokhothu KD, Tshikhotho M, Thango BA (2025) Evaluating the Impact of Cloud Computing on SME Performance. Syst Rev Businesses 5(2):23. https://doi.org/10.3390/businesses5020023 Díaz M, Martín C, Rubio B (2016) State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J Netw Comput Appl 67:99–117 Ding S, Tukker A, Ward H (2023) Opportunities and risks of internet of things (IoT) technologies for circular business models: A literature review. J Environ Manage 336:117662 Esty JN, Mussa ASM, Fazle M, Rabbi MRH, Muhit MG, Shahriar N (2025) EcoGuard: Advancing IoT-based Aquaculture with Machine Learning for Enhanced Productivity and Automation. J IoT Social Mob Analytics Cloud 7(1):18–41 Flores-Iwasaki M, Guadalupe GA, Pachas-Caycho M, Chapa-Gonza S, Mori-Zabarburú RC, Guerrero-Abad JC (2025) Internet of Things (IoT) Sensors for Water Quality Monitoring in Aquaculture Systems: A Systematic Review and Bibliometric Analysis. AgriEngineering, 7(3), p.78 Forhad HM, Uddin MR, Chakrovorty RS, Ruhul AM, Faruk HM, Kamruzzaman S, Sharmin N, Jamal ASIM, Haque MMU, Morshed AM (2024) IoT based real-time water quality monitoring system in water treatment plants (WTPs). Heliyon, 10(23) Garrido-Momparler V, Peris M (2022) Smart sensors in environmental/water quality monitoring using IoT and cloud services. Trends Environ Anal Chem 35:e00173 Geetha S, Gouthami SJSW (2016) Internet of things enabled real time water quality monitoring system. Smart Water 2:1–19 Ghosh S, Ghosh K, Karamakar S, Prasad S, Debabhuti N, Sharma P, Tudu B, Bhattacharyya N, Bandyopadhyay R (2019) December. Development of an IOT based robust architecture for environmental monitoring using UAV. In 2019 IEEE 16th India Council International Conference (INDICON) (pp. 1–4). IEEE Giménez JG, Granero A, Senent-Aparicio J, Gómez-Jakobsen F, Mercado JM, Blanco-Gómez P, Ruiz JM, Cecilia JM (2024) Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study, vol 81. Ecological Informatics, p 102554 Gobler CJ (2020) Climate change and harmful algal blooms: insights and perspective, vol 91. Harmful algae, p 101731 Gopinath MP, Tamizharasi GS, Kavisankar L, Sathyaraj R, Karthi S, Aarthy SL, Balamurugan B (2019) A secure cloud-based solution for real-time monitoring and management of Internet of underwater things (IOUT). Neural Comput Appl 31:293–308 Khanyi MB, Xaba SN, Mlotshwa NA, Thango B, Matshaka L (2024) A Roadmap to Systematic Review: Evaluating the Role of Data Networks and Application Programming Interfaces in Enhancing Operational Efficiency in Small and Medium Enterprises. Sustainability 16(23):10192. https://doi.org/10.3390/su162310192 Thobejane LT, Thango BA (2024) Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review. Appl Sci 14(14):6097 Haque H, Labeeb K, Riha RB, Khan MNR (2021) March. IoT based water quality monitoring system by using Zigbee protocol. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 619–622). IEEE Hemdan EED, Essa YM, El-Sayed A, Shouman M, Moustafa AN (2021) July. Smart water quality analysis using IoT and big data analytics: a review. In 2021 International Conference on Electronic Engineering (ICEEM) (pp. 1–5). IEEE Hilal AM, Althobaiti MM, Eisa TAE, Alabdan R, Hamza MA, Motwakel A, Duhayyim A, Negm M (2022) N., An intelligent carbon-based prediction of wastewater treatment plants using machine learning algorithms. Adsorption Science & Technology, 2022, p.8448489 Huan J, Li H, Wu F, Cao W (2020) Design of water quality monitoring system for aquaculture ponds based on NB-IoT. Aquacultural Engineering, 90, p.102088 Huentenao H, Muñoz C, Otárola F, Huircan J, Somos M (2024) October. IoT System for Real-Time Monitoring of Turbidity in Lakes. In 2024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA) (pp. 1–6). IEEE Islam MM, Kashem MA, Alyami SA, Moni MA (2023) Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival. Microprocess Microsyst 102:104930 Jabbar WA, Ting TM, Hamidun MFI, Kamarudin AHC, Wu W, Sultan J, Alsewari AA, Ali MA (2024) Development of LoRaWAN-based IoT system for water quality monitoring in rural areas, vol 242. Expert systems with applications, p 122862 Mudau MC, Moshapo LW, Monyela TM, Thango BA (2024) The Role of Manufacturing Operations in SMEs Performance: A Systematic Review. Preprints 2024100539 Mtjilibe T, Rameetse E, Mgwenya N, Thango B (2024) Exploring the Challenges and Opportunities of Social Media for Organizational Engagement in SMEs: A Comprehensive Systematic Review. Available at SSRN 4998542 Jais NAM, Abdullah AF, Kassim MSM, Abd Karim MM, Muhadi NA (2024) Improved accuracy in IoT-Based water quality monitoring for aquaculture tanks using low-cost sensors: Asian seabass fish farming. Heliyon, 10(8) Jan F, Min-Allah N, Düştegör D (2021) Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications. Water, 13(13), p.1729 Jothimani A, Edward AS, Gowthem KM, Karthikeyan R (2017) Implementation of smart sensor interface network for water quality monitoring in industry using iot. Indian J Sci Technol 10(6):1–7 Júnior ACDS, Munoz R, Quezada MDLÁ, Neto AVL, Hassan MM, De Albuquerque VHC (2021) Internet of water things: A remote raw water monitoring and control system. IEEE Access 9:35790–35800 Kamaludin KH, Ismail W (2017) December. Water quality monitoring with internet of things (IoT). In 2017 IEEE Conference on Systems, Process and Control (ICSPC) (pp. 18–23). IEEE Kandasamy L, Mahendran A, Sangaraju SHV, Mathur P, Faldu SV, Mazzara M (2025) Enhanced remote sensing and deep learning aided water quality detection in the Ganges River, India supporting monitoring of aquatic environments. Results in Engineering, 25, p.103604 Khalil S, Mahnashi MH, Hussain M, Zafar N, Khan FS, Afzal U, Shah GM, Niazi UM, Awais M, Irfan M (2021) Exploration and determination of algal role as Bioindicator to evaluate water quality–Probing fresh water algae. Saudi J Biol Sci 28(10):5728–5737 Khan N, Khattak KS, Ullah S, Khan Z (2019) December. A low-cost IoT based system for environmental monitoring. In 2019 international conference on frontiers of information technology (FIT) (pp. 173–1735). IEEE Myataza A, Mafunga M, Mkhulisi NS, Thango BA (2024) A Systematic Review of ERP, CRM, and HRM Systems for SMEs: Managerial and Employee Support. Preprints 2024100384 Gumede TT, Chiworeka JM, Magoda AS, Thango B (2024) Building Effective Social Media Strategies for Business: A Systematic Review. Preprints 2024100379 Kim D, Lee K, Jeong S, Song M, Kim B, Park J, Heo TY (2024) Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data. Environmental Research, 262, p.119823 Koditala NK, Pandey PS (2018) August. Water quality monitoring system using IoT and machine learning. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) (pp. 1–5). IEEE Krishnan SR, Nallakaruppan MK, Chengoden R, Koppu S, Iyapparaja M, Sadhasivam J, Sethuraman S (2022) Smart water resource management using Artificial Intelligence—A review. Sustainability, 14(20), p.13384 Kukartsev V, Orlov V, Semenova E, Rozhkova A (2024) Optimizing water quality classification using random forest and machine learning. In BIO Web of Conferences (Vol. 130, p. 03007). EDP Sciences Kumar D (2022) May. IoT and GSM Based Real-Time Water Monitoring System with Low Power Consumption. In Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021 (pp. 165–172). Singapore: Springer Nature Singapore Kumar D, Sharma H, Prakash A, Husain M (2024) Cloud Computing for IoT Sensing Data. Technological Advancements in Data Processing for Next Generation Intelligent Systems. IGI Global, pp 1–23 Kumar J, Gupta R, Sharma S, Chakrabarti T, Chakrabarti P, Margala M (2024) IoT-Enabled Advanced Water Quality Monitoring System for Pond Management and Environmental Conservation. IEEE Access Kwon DH, Hong SM, Abbas A, Pyo J, Lee HK, Baek SS, Cho KH (2023) Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environ Eng Res, 28(1) Lai L, Liu Y, Zhang Y, Cao Z, Yin Y, Chen X, Jin J, Wu S (2024) Long-term spatiotemporal mapping in lacustrine environment by remote sensing: Review with case study, challenges, and future directions. Water Res, p.122457 Lee C, Wang YJ (2020) Development of a cloud-based IoT monitoring system for Fish metabolism and activity in aquaponics. Aquacult Eng 90:102067 Lee JY (2025) Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods. Environmental Monitoring and Assessment, 197(4), p.405 Liu X, Ho L, Bruneel S, Goethals P (2024) Applications of unmanned vehicle systems for multi-spatial scale monitoring and management of aquatic ecosystems: A review. Ecological Informatics, p 102926 Mahdzir MM, Yahaya SH, Fauadi MHFM, Yeow TT (2025) Integrating water quality model and aeration with IoT technology in water quality management: A conceptual framework. Multidisciplinary Sci J 7(3):2025139–2025139 Mallick A, Barnwal RP (2025) January. A Scalable Framework for Multi-cloud IoT Data Synchronization. In Proceedings of the 26th International Conference on Distributed Computing and Networking (pp. 364–369) Mallikarjun S, Mangalpady A (2025) A review on application of innovative techniques for sustainable environmental management in mines. Disaster Adv 18(5):191–195 Manoj M, Dhilip Kumar V, Arif M, Bulai ER, Bulai P, Geman O (2022) State of the art techniques for water quality monitoring systems for fish ponds using iot and underwater sensors: A review. Sensors, 22(6), p.2088 Mehta S, Singh A (2024) October. Integrating IoT and AI for Holistic Water Quality Monitoring and Management. In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT) (pp. 1–5). IEEE Mezni H, Driss M, Boulila W, Atitallah SB, Sellami M, Alharbi N (2022) Smartwater: A service-oriented and sensor cloud-based framework for smart monitoring of water environments. Remote Sensing, 14(4), p.922 Miasayedava L, McBride K, Tuhtan JA (2022) Automated environmental compliance monitoring of rivers with IoT and open government data. Journal of Environmental Management, 303, p.114283 Miller T, Durlik I, Kostecka E, Kozlovska P, Łobodzińska A, Sokołowska S, Nowy A (2025) Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data. Electronics, 14(4), p.696 Mishra DR, Kumar A, Ramaswamy L, Boddula VK, Das MC, Page BP, Weber SJ (2020) CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms, vol 96. Harmful algae, p 101828 Mounir M, Chkouri MY, El Khamlichi Y, Touhafi A (2024) November. Emerging Technologies for Water Quality Monitoring and Assessment: A Systematic Review. In 2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE) (pp. 1–8). IEEE Nandhakumar S, Vengat R, Ramkumar T, Rakesh R, K (2020) IoT Based Pollution Monitoring System for Effective Industrial Pollution Monitoring and Control. Biosc. Biotech Res Comm, 13(3) Nguyen CM, Mays J, Plesa D, Rao S, Nguyen M, Chiao JC (2015) May. Wireless sensor nodes for environmental monitoring in Internet of Things. In 2015 IEEE MTT-S International Microwave Symposium (pp. 1–4). IEEE Nishan RK, Akter S, Sony RI, Hoque MM, Anee MJ, Hossain A (2024) Development of an IoT-based multi-level system for real-time water quality monitoring in industrial wastewater. Discover Water, 4(1), p.43 Nyangaresi VO, AlRababah AA, Yenurkar GK, Chinthaginjala R, Yasir M (2025) Anonymous Authentication Scheme Based on Physically Unclonable Function and Biometrics for Smart Cities. Eng Rep 7(1):e13079 Okpara EC, Sehularo BE, Wojuola OB (2022) On-line water quality inspection system: the role of the wireless sensory network. Environmental Research Communications, 4(10), p.102001 Olanubi OO, Akano TT, Asaolu OS (2024) Design and development of an IoT-based intelligent water quality management system for aquaculture. Journal of Electrical Systems and Information Technology, 11(1), p.15 Olatomiwa L, Ambafi JG, Dauda US, Longe OM, Jack KE, Ayoade IA, Abubakar IN, Sanusi AK (2023) A review of Internet of Things-based visualisation platforms for tracking household carbon footprints. Sustainability, 15(20), p.15016 Pamula AS, Ravilla A, Madiraju SVH (2022) Applications of the internet of things (IoT) in real-time monitoring of contaminants in the air, water, and soil. Engineering Proceedings, 27(1), p.26 Pandey S, Mishra S, Jain RK (2023) May. IoT Interface Device for Sensing Arsenic in Contaminated Water. In 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON) (pp. 1–6). IEEE Park J, Kim KT, Lee WH (2020) Recent advances in information and communications technology (ICT) and sensor technology for monitoring water quality. Water, 12(2), p.510 Pasika S, Gandla ST (2020) Smart water quality monitoring system with cost-effective using IoT. Heliyon, 6(7) Perumal T, Sulaiman MN, Leong CY (2015) October. Internet of Things (IoT) enabled water monitoring system. In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) (pp. 86–87). IEEE Pranata AA, Lee JM, Kim DS (2017) June. Towards an IoT-based water quality monitoring system with brokerless pub/sub architecture. In 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) (pp. 1–6). IEEE Prasad AN, Mamun KA, Islam FR, Haqva H (2015) December. Smart water quality monitoring system. In 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (pp. 1–6). IEEE Pule M, Yahya A, Chuma J (2017) Wireless sensor networks: A survey on monitoring water quality. J Appl Res Technol 15(6):562–570 Qian P, Liu G (2024) Application of High-Frequency Intelligent Sensing Network in Monitoring and Early Warning of Water Quality Dynamic Change. International Journal of Computational Intelligence Systems, 17(1), p.195 Razman NA, Ismail W, Abd Razak WZ, Ismail MH, Jamaludin I, J (2023) Design and analysis of water quality monitoring and filtration system for different types of water in Malaysia. Int J Environ Sci Technol 20(4):3789–3800 Rocha AA (1992) Algae as biological indicators of water pollution. A general approach, Algae and environment Ruiz-Vanoye JA, Diaz-Parra O, Márquez Vera MA, Fuentes-Penna A, Barrera-Cámara RA, Ruiz-Jaimes MA, Toledo-Navarro Y, Bernábe-Loranca MB, Simancas-Acevedo E, Trejo-Macotela FR, Vera-Jiménez MA (2025) A Comprehensive Review of Quality of Aquaculture Services in Integrated Multi-Trophic Systems. Fishes, 10(2), p.54 Salah K, Alfalasi A, Alfalasi M, Alharmoudi M, Alzaabi M, Alzyeodi A, Ahmad RW (2020) January. IoT-enabled shipping container with environmental monitoring and location tracking. In 2020 IEEE 17th annual consumer communications & networking conference (CCNC) (pp. 1–6). IEEE Saravanan K, Anusuya E, Kumar R, Son LH (2018) Real-time water quality monitoring using Internet of Things in SCADA. Environmental monitoring and assessment, 190(9), p.556 Silva GME, Campos DF, Brasil JAT, Tremblay M, Mendiondo EM, Ghiglieno F (2022) Advances in technological research for online and in situ water quality monitoring—A review. Sustainability, 14(9), p.5059 Singh Y, Walingo T (2024) Smart water quality monitoring with IOT wireless sensor networks. Sensors, 24(9), p.2871 Singh Y, Walingo T (2024) Smart water quality monitoring with IOT wireless sensor networks. Sensors, 24(9), p.2871 Skoufias G (2022) Remote sensing of harmful algal blooms: Data modeling and business aspects (Doctoral dissertation) Strigaro D, Capelli C, Cannata M (2024) An open early-warning system prototype for managing and studying algal blooms in Lake Lugano. 48:143–148The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Sudriani Y, Sebestyén V, Abonyi J (2023) Surface water monitoring systems—the importance of integrating information sources for sustainable watershed management. IEEE Access 11:36421–36451 Sugiharto WH, Susanto H, Prasetijo AB (2023) Real-time water quality assessment via IoT: monitoring pH, TDS, temperature, and turbidity. Ingénierie des Systèmes d’Information 28(4):823–831 Sujatha MG, Reza H, Ranganathan P (2024) January. A Conceptual Software Framework to Monitor Harmful Algal Blooms (HABs) in Lakes. In 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0610–0619). IEEE Sundar LS, Almujibah H, Alshahri AH, Ancha VR (2023) Assessment of carbon neutrality in waste water treatment systems through deep learning algorithm. Water Reuse 13(3):432–447 Tziortzioti C, Amaxilatis D, Mavrommati I, Chatzigiannakis I (2019) IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial. 343:117–130Electronic Notes in Theoretical Computer Science Ubina NA, Lan HY, Cheng SC, Chang CC, Lin SS, Zhang KX, Lu HY, Cheng CY, Hsieh YZ (2023) Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT). Smart agricultural technology, 5, p.100285 Vakalis D, Hellwig RT, Schweiker M, Gauthier S (2023) Challenges and opportunities of Internet-of-Things in occupant-centric building operations: towards a life cycle assessment framework. Current Opinion in Environmental Sustainability, 65, p.101383 Vijayakumar N, Ramya AR (2015) March. The real time monitoring of water quality in IoT environment. In 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1–5). IEEE Wang W, Feng C, Zhang B, Gao H (2019) Environmental monitoring based on fog computing paradigm and internet of things. IEEE Access 7:127154–127165 Wiryasaputra R, Huang CY, Lin YJ, Yang CT (2024) An IoT real-time potable water quality monitoring and prediction model based on cloud computing architecture. Sensors, 24(4), p.1180 Wong BP, Kerkez B (2016) Real-time environmental sensor data: An application to water quality using web services, vol 84. Environmental Modelling & Software, pp 505–517 Wongkiew S, Hu Z, Lee JW, Chandran K, Nhan HT, Marcelino KR, Khanal SK (2021) Nitrogen recovery via aquaponics–bioponics: Engineering considerations and perspectives. ACS ES&T Eng 1(3):326–339 Yu YB, Lee JH, Choi JH, Choi YJ, Jo AH, Choi CY, Kang JC, Kim JH (2023) The application and future of biofloc technology (BFT) in aquaculture industry: A review. J Environ Manage 342:118237 Zhang P, Peng C, Zhang J, Zhang J, Chen J, Zhao H (2022) Long-term harmful algal blooms and nutrients patterns affected by climate change and anthropogenic pressures in the zhanjiang bay, China. Front Mar Sci 9:849819 Zhang X, Yang L, He S, Wang M (2024) July. Design of a Water Quality Monitoring System for Self-Service Water Vending Machines Based on Internet of Things Technology. In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 243–247). IEEE Zulkifli CZ, Garfan S, Talal M, Alamoodi AH, Alamleh A, Ahmaro IYY, Sulaiman S, Ibrahim AB, Zaidan BB, Ismail AR, Albahri OS, Albahri AS, Soon CF, Harun NH, Chiang HH (2022) IoT-Based Water Monitoring Systems: A Systematic Review. Water , [online] 14(22), p.3621. https://doi.org/10.3390/w14223621 Tables Tables 1 and 9 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6848919","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":468211806,"identity":"d9773bb2-5221-4269-b153-1c7dc7ae1415","order_by":0,"name":"Ronewa Nethanani","email":"data:image/png;base64,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","orcid":"","institution":"University of Johannesburg","correspondingAuthor":true,"prefix":"","firstName":"Ronewa","middleName":"","lastName":"Nethanani","suffix":""},{"id":468211839,"identity":"42dd16aa-c7b6-4c15-8e29-6211b4d7aec0","order_by":1,"name":"Yoren Ndou","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"Yoren","middleName":"","lastName":"Ndou","suffix":""},{"id":468211840,"identity":"76820ef5-8787-432d-86f8-1628f795890b","order_by":2,"name":"Wilson Nchabeleng","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"Wilson","middleName":"","lastName":"Nchabeleng","suffix":""},{"id":468211841,"identity":"c85d502d-cbcf-4c0b-a677-26b25f652e6d","order_by":3,"name":"Sifundo Ndlovu","email":"","orcid":"","institution":"University of Johannesburg","correspondingAuthor":false,"prefix":"","firstName":"Sifundo","middleName":"","lastName":"Ndlovu","suffix":""}],"badges":[],"createdAt":"2025-06-08 18:18:46","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6848919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6848919/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84506927,"identity":"94ae0354-f44b-4f2c-96a9-c216690f4135","added_by":"auto","created_at":"2025-06-12 19:05:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29519,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of Research Strategy.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/791fae22d5ef1037e83aad9b.png"},{"id":84505917,"identity":"6c883ad2-07b7-40c2-9930-1087b1ecc937","added_by":"auto","created_at":"2025-06-12 18:49:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66447,"visible":true,"origin":"","legend":"\u003cp\u003eSelection Process.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/52ac49304e8fff643c37dce0.png"},{"id":84506749,"identity":"791cbd47-f8be-46e8-805c-62418100f0d5","added_by":"auto","created_at":"2025-06-12 18:57:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47941,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of Data Selection and Extraction.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/9f76b7622f3163f588c8edb8.png"},{"id":84505915,"identity":"f3918d17-ec35-465d-9d03-392b4049bee3","added_by":"auto","created_at":"2025-06-12 18:49:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49393,"visible":true,"origin":"","legend":"\u003cp\u003eProposed flow diagram of data items.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/4ee8c417c5ed7fabab17d801.png"},{"id":84506928,"identity":"d647c9d3-a7ef-41b6-8ea5-4e17bcac07dd","added_by":"auto","created_at":"2025-06-12 19:05:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e. 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12","display":"","copyAsset":false,"role":"figure","size":97497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 13.\u003c/strong\u003e Communication technologies and protocols used across studies.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/c7d78f659e79f8e059c91cf4.png"},{"id":84505929,"identity":"86f94351-da65-425e-b802-7b893e2dc8a7","added_by":"auto","created_at":"2025-06-12 18:49:07","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":130144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 14.\u003c/strong\u003e System architecture types in IoT-cloud implementations.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/d4aa7f6037a49f9f39a0676c.png"},{"id":84506930,"identity":"b9d92911-6373-4b47-a099-88c55104c99b","added_by":"auto","created_at":"2025-06-12 19:05:06","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":75783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 15. \u003c/strong\u003eSecurity and privacy mechanisms reported in reviewed studies.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/ab69c4b04b1ecaf868e7b71a.png"},{"id":84505926,"identity":"916ccca2-371f-48c2-b45f-59e043c35368","added_by":"auto","created_at":"2025-06-12 18:49:07","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":135049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 16. \u003c/strong\u003ePrivacy measures adopted in IoT-cloud biological water monitoring studies.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/f6732c459b1db81a014da1d4.png"},{"id":84507505,"identity":"435d8f5b-c88c-4455-999a-cc4c92658e68","added_by":"auto","created_at":"2025-06-12 19:13:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2620707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/da157e8e-22d5-40e7-9488-d1f891015d9d.pdf"},{"id":84505910,"identity":"b4e3348c-393a-4069-8ac5-865a2a68f1a5","added_by":"auto","created_at":"2025-06-12 18:49:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22457,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and9.docx","url":"https://assets-eu.researchsquare.com/files/rs-6848919/v1/5a309e61eec53103136438fa.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIoT and Cloud Computing in Biological Water Monitoring: A Systematic Review of Challenges, Architectures, and Emerging Trends\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEarly observation is critical for understanding and responding to the biological impacts triggered by eutrophication, such as nutrient-driven phytoplankton development and aquatic biodiversity alterations. Static bounded data models cannot monitor evolving dynamics caused by eutrophication. Owing to excess nutrients' more destructive impact on biological balance, continuous monitoring acquires supreme priority for early diagnosis and countermeasures (Chang, Imen and Vannah, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kandasamy et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The emergence of IoT and cloud technologies has significantly transformed environmental monitoring by enabling real-time, high-resolution data collection and processing. IoT platforms, such as water-based sensors, provide continuous environmental data (Sujatha, Reza and Ranganathan, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which are immediately transmitted to cloud servers via wireless networks like 2G (Mishra et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These data are then integrated and processed using cloud-based infrastructures\u0026mdash;ranging from social and sensor clouds to computation clouds\u0026mdash;for enhanced analysis and decision-making (Mishra et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The integration of satellite, drone, and in-situ sensor data further supports the use of microservices architectures for flexible and scalable processing \u0026bull; (Skoufias, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while platforms like ArcGIS and web-based servers facilitate centralized data storage and access [4]. Environmental and socioeconomic conditions play a significant role in the intensification and expansion of toxic algal blooms (HABs). Global climate change, through warming temperatures, increased stratification, and intense precipitation, promotes CyanoHABs and is accountable for their rising frequency and extension of their ranges (Gobler, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chapra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Eutrophication, driven mainly by anthropogenic nutrient loads from agriculture, aquaculture, and industry, further promotes bloom events, as evidenced in cases like the ZJB (Zhang et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chapra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)These flowers have serious effects on water bodies, public health, and the regional economy through polluting the drinking water supply, disrupting fisheries, and causing huge economic losses to aquaculture (Gobler, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chapra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Biological water monitoring using algae is a classical method of water quality assessment, as algae respond rapidly to environmental changes and to pollution levels. As bioindicators, different groups of algae point to definite trophic states\u0026mdash;green algae and diatoms are characteristic of oligotrophic (nutrient-poor) conditions, and blue-green algae (Cyanophyta) point to eutrophication (Khalil et al., Rocha, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Phytoplankton and zooplankton are sentinels of aquatic health, and their changes in abundance and community composition signal changes in water chemistry and the occurrence of pollutants (Chandel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rocha, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The predominance of some groups, including diatoms and cyanobacteria, has rendered them important tools in assessing aquatic ecosystem status and identifying nutrient enrichment (Khalil et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rocha, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The Internet of Things (IoT) plays a transformative role in algae monitoring by enabling real-time, high-frequency data collection through interconnected sensors and devices. These systems can continuously track critical water quality parameters such as turbidity, pH, temperature, dissolved oxygen, and water level using sensors connected to Wi-Fi-enabled microcontrollers like the ESP32, which transmit updates every few seconds to databases and mobile apps (Bodaragama et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This rapid data acquisition supports the development of machine learning models for predicting harmful algal bloom (HAB) events, overcoming the limitations of traditional, labor-intensive, and less responsive monitoring methods (Kwon et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Busari et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, IoT-based monitoring systems offer a cost-effective and scalable solution for timely detection and management of HABs (Kwon et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Busari et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cloud computing plays a vital role in algae monitoring by enabling the remote storage, access, and analysis of water quality data collected from IoT-enabled devices. Measurements taken by sensors submerged in lakes or rivers are transmitted to cloud platforms like Wia, allowing users to view real-time data on smartphones or computers from any location. This seamless integration supports efficient data management and facilitates timely analysis and response to changing water conditions (Tziortzioti et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Modern IoT-based and AI-integrated algae monitoring systems significantly outperform traditional methods in both accuracy and efficiency. Advanced machine learning models such as SVM, random forest, and decision trees, when paired with sensor data and techniques like PCA, have achieved accuracy rates of up to 100%, surpassing conventional nonlinear models and manual monitoring approaches (Lee, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These systems also enable real-time analysis, low-power operation (e.g., 29 W), and edge computing capabilities, reducing reliance on constant cloud connectivity and lowering operational costs. As a result, they present a cost-effective, scalable alternative to labor-intensive traditional methods with minimal error margins and rapid processing (Lee, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Esty et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Interoperability and standardization are essential for ensuring seamless communication and integration among the diverse components of IoT-based algae monitoring systems. While specific references to standardization in algae-focused IoT were not found, broader technological enablers like cloud computing and advanced communication networks support interoperability by enabling fast, secure, and reliable data transmission between users, sensors, and cloud systems. Cloud platforms also enhance scalability, resource integration, and secure data access\u0026mdash;critical for managing large and varied datasets from distributed sources. Technologies like Docker further ensure consistent deployment environments, contributing to standardized and secure system operations (Esty et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Most IoT studies emphasize physicochemical parameters (pH, turbidity, DO) with comparatively few incorporating biological assays or in situ algal community profiling (Forhad et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Technical challenges (connectivity, data security) and ecological complexities (species interactions, trophic dynamics) are often addressed in isolation rather than via integrated frameworks (Ubina et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Effective algae monitoring underpins SDG 6 (Clean Water and Sanitation) by enabling early detection of water quality threats and guiding remediation efforts. Rapid adoption of IoT and cloud platforms in environmental monitoring has outpaced the development of best-practice guidelines, risking fragmented implementations and data silos. Bridging engineering innovations with ecological understanding is essential to design robust, adaptive monitoring systems that address both technical feasibility and environmental variability. Systematically map existing IoT and cloud-based algae monitoring solutions, highlighting their strengths and limitations. Integrate findings from sensor technology, data analytics, and aquatic ecology to propose holistic monitoring frameworks. Offer practical recommendations for scalable architecture designs, interoperability standards, and context-aware deployment strategies to advance the field (Strigaro, Capelli and Cannata, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research questions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith the growing use of IoT and cloud technology in environmental monitoring systems, various issues related to data management, access in real-time, and long-term dependability have been raised. In biological water monitoring, there are certain issues that must be discussed at the research level to provide assurance that the systems that are installed are not only operational, but also sustainable and versatile in various use scenarios and environments\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWhat are the most critical infrastructural and technical issues of the integration of IoT and cloud computing technologies with biological water monitoring systems?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn what ways do geographical, economic, and environmental factors influence the scalability and efficiency of cloud-integrated IoT systems in actual deployment?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat are technologies, approaches, and architecture patterns that have been proposed to address concerns such as energy usage, data protection, and system interoperability in cloud-IoT technology? Analysis of existing technological solutions can caution best practices and can assist in charting solutions based on a weighing of cost, complexity, and efficiency (Sujan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat are some of the new trends or opportunities in smart environmental monitoring that can be harnessed to enhance biological water quality monitoring, especially in automation and data analysis? Against the backdrop of advancements in edge computing, machine learning and decentralized networks, the question looks at new possibilities for enhancing accuracy, responsiveness, and maintainability (Sharma et al., 2023).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Rationale\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe reason to conduct this literature systematic review stems from the growing need for effective, real-time water quality monitoring systems capable of operating in changing conditions and generating actionable data for researchers, policymakers, and stakeholders. IoT platforms, if integrated with cloud infrastructures, deliver unparalleled levels of access to real-time analysis and historical archiving of data. Yet, notwithstanding such promise, real-world implementations often fall short because integration difficulties or technological limitations are readily forgotten (Mandal et al., 2024; Bera et al., 2024).\u003c/p\u003e \u003cp\u003eLots of studies have pointed out that even though IoT-enabled systems can be deployed at relatively modest cost, most of them cannot be run continuously due to battery constraints, unreliable cloud connectivity or poor sensor calibration (Chandrashekar et al., 2023; Adhikari \u0026amp; Saikia, 2022). On top of that, data security concerns and the lack of standard protocol for device-to-device communication add layers of complexity, especially in multi-site or collaborative research setups. This review responds to the need for a full synthesis of the issues and opportunities within this domain, providing insight not only into technical limitations but also into contextual limitations such as geographic unreachability and cost. It is intended to guide future deployments and research efforts by de-mystifying what has been studied, what has been tried in terms of solutions, and where the most critical gaps remain (Singh \u0026amp; Kumar, 2025; Sharma et al., 2025).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Objectives\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe general purpose of the current systematic literature review is to critically analyze the application of Internet of Things (IoT) and cloud computing technologies in biological water monitoring. With the environmental conditions in the entire world deteriorating due to climate change, eutrophication, industrial pollution, and agricultural runoff, the need for smart, adaptive, and dynamic systems for water quality monitoring has become increasingly important. The review aims to explore how Internet of Things (IoT) devices, in this case, low-power sensors such as those utilizing the ESP32 microcontroller, combined with cloud computing infrastructure can enable real-time monitoring of biological water quality parameters such as turbidity, temperature, pH, and total dissolved solids (TDS). These technologies can make a revolution by offering high-definition continuous data that is remotely viewable and can be analyzed, eliminating the limitation of the traditional manual sampling methods.\u003c/p\u003e \u003cp\u003eThe second primary objective is to research and examine the challenges of using IoT and cloud technology in water systems. Among those are sensor accuracy and calibration issues, power in remote locations, data security and privacy concerns, communication latency, and the lack of a unified protocol for interoperability of data. Integrating evidence from over 80 peer-reviewed articles found between 2015 and 2025, this review endeavors to learn about how such challenges impact the validity and credibility of such technologies once deployed in real-world applications. In addition to defining these constraints, the review discusses new possibilities facilitated by emerging technologies such as edge computing, artificial intelligence, low-power wide-area networks, and hybrid cloud-edge architectures. These can potentially enhance data analytics ability, reduce energy requirements, and make system response time faster, all of which are essential for the detection and management of biological threats such as toxic algal blooms. Finally, this review seeks to present actionable, evidence-based suggestions to system designers, policymakers, and researchers. By presenting an overview of the state of the art and highlighting the opportunity and limitation in current solutions, the review contributes towards the making of more adaptive, sustainable, and intelligent water quality monitoring systems to meet international aspirations like SDG 6: Clean Water and Sanitation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Research Contribution\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy filling most of the gaps in the body of literature of studies, this systematic review makes a multidimensional contribution to the research of IoT and cloud-integrated biological water quality monitoring:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMany of the researched studies employ literature analysis alone and are not tested empirically, though several of them propose promising designs and integrated solutions for water monitoring using IoT, AI, and cloud platforms. This study adds to the literature by making the identification of the actual-world impediments\u0026mdash;in the sense of deployment expense, calibration problems, and environmental limitations\u0026mdash;which bar the utilization or experiment test of these theoretical models in actual-world environments. It stresses that case studies and pilot runs should be utilized as a foundation for further research.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe impacts of regional conditions on technological feasibility of IoT-cloud solutions such as weather exposure, rural availability, or local economic constraints are not considered in a number of different assessments. By contrasting facts with a sense of context-awareness and distinguishing which solutions will and will not be geographically transferable, this review improves on this point. Through contextualization, this gives an improved framework on which to better inform judgments around technological feasibility to researchers and policymakers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThere are few studies examining system-level issues like energy consumption, interoperability, data privacy, and real-time synchronization despite several studies debating the feasibility of cloud and IoT integration. By categorizing and assessing these neglected hindrances, this study contributes by introducing a categorization of system-level faults preventing complete deployment. Relative maturity of various architectural elements is also discussed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn addition to recognizing roadblocks, this review charts new frontiers, specifically in edge computing, LPWANs, AI integration, and hybrid system architecture. This paper contributes by connecting mutually distinct technologies, such as the development of sensors or big data pipelines, and how a hybridized structure could increase sustainability, scalability, and responsiveness in real-time water quality monitoring systems.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5. Research Novelty\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFew papers have dealt with the synergistic issues and system-level issues that arise when integrating these technologies, although many papers have evaluated the advantage of integrating cloud computing or the Internet of Things in water monitoring systems. Focusing on practical deployment issues like energy limitations, data integrity, sensor deployment, and communication latency, this paper is the first to fully address this convergence.\u003c/p\u003e \u003cp\u003eMoreover, this paper combines technical depth with contextual examination, considering how physical location, regulatory environments, and user ability affect system functionality and sustainability. This contrasts with previous reviews that either generalize IoT application scenarios or focus on each of the individual components like sensor calibration or data storage.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis section outlines the methodology used in the study. It begins with the Eligibility Criteria (Section 2.1), which define specific guidelines for including or excluding studies to ensure only relevant research is considered. Information Sources (Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e) identifies the databases used to locate studies, emphasizing key academic repositories. The Search Strategy (Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e) details the keywords and techniques applied to retrieve appropriate studies. In the Selection Process (Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e), studies are initially screened to verify compliance with the eligibility criteria. Next, the Data Collection Process (Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e) involves systematically extracting essential information from the selected studies. Data Items (Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e) specify the types of data gathered for further evaluation. To ensure reliability, Study Risk of Bias (Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e2.7\u003c/span\u003e) assesses potential bias or limitations in the included studies. Effect Measures (Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e2.8\u003c/span\u003e) describe how the impact of various elements was evaluated. The Synthesis Methods (Section \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003e2.9\u003c/span\u003e) explain how the results across studies were integrated. Reporting Bias (Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e2.10\u003c/span\u003e) investigates any selective reporting of outcomes, and Certainty Assessment (Section 2.11) evaluates the confidence level in the overall evidence. In the Results (Section 3), findings from the study are presented. Results of Study Selection (Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e) outline how many studies were included and the reasons behind their selection. Study Characteristics (Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e) highlight key attributes of these studies. Risk of Bias in Studies (Section \u003cspan refid=\"Sec29\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e) notes any methodological flaws. Results of Individual Studies (Section 3.4) present the outcomes of each study, while Results of Syntheses (Section 3.5) compile the findings into a broader perspective. Reporting Biases (Section 3.6) reassess possible selective reporting, and Certainty of Evidence (Section 3.7) indicates the overall reliability of the combined results. The Discussion (Section 4) critically interprets the findings in relation to IoT and cloud computing applications in biological water monitoring, highlighting key insights, challenges, and emerging trends identified across the reviewed studies. It explores the practical significance of these findings within the context of real-time water quality monitoring and environmental management. Section 5, Practical Recommendations, offers targeted suggestions for researchers and practitioners, focusing on system scalability, data security, and integration strategies for IoT-cloud architectures. The review concludes with a final summary, emphasizing the study's overall contribution to advancing knowledge and guiding future research in sustainable water monitoring technologies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Eligibility Criteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo make sure that only relevant and high-quality studies were included in this review, clear eligibility rules were used to select the materials. The focus of this research is on how the Internet of Things (IoT) and Cloud Computing are used in biological water monitoring, especially the issues, challenges, and opportunities. Only studies that clearly discuss these technologies within the context of biological water monitoring were included. In addition, the selected studies needed to have a clear research framework or methodology, particularly one that shows how technology affects biological systems or water quality analysis (Khan et al., 2021). Only articles written in English were used, as this is the main language of scientific communication and it avoids issues with translation that may lead to misinterpretation (Khanyi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thobejane et al., 2024; Skosana et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mkhize et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Also, the review only considered studies published between 2015 and 2025. This time range was chosen to reflect the most recent advancements in IoT and Cloud Computing, which have rapidly evolved in the past decade (Khanyi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thobejane et al., 2024; Skosana et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mkhize et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Studies that did not talk about IoT or Cloud Computing in relation to biological water monitoring, that lacked a clear methodology, were not in English, or were published outside the chosen time frame were excluded from the review.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProposed Inclusion and Exclusion Criteria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion Criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion Criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocuses on IoT and Cloud Computing Issues Challenges and Opportunities in Biological Water Monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies not IoT and Cloud Computing Issues Challenges and Opportunities in Biological Water Monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMust include a clear research framework or methodology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLacks a framework or methodology relevant to biological impacts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMust be written in English\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublished in other languages\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublication Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublished between 2015 and 2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutside the specified period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Information Source\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data for this study was collected from a diverse array of peer-reviewed articles, conference papers, and technical studies published between 2015 and 2025. These sources were obtained from established academic databases such as Web of Science, SCOPUS, and Google Scholar, ensuring scholarly rigor and relevance. The selection included research focusing on IoT and Cloud Computing applications in water quality monitoring. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Summarizes the online research databases consulted, emphasizing the role of each source and how it aligns with the study\u0026rsquo;s inclusion and exclusion criteria to ensure methodological rigor.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Online Research Repositories Used.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess Platform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInclusion/Exclusion Criteria Applied\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose of Use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoogle Scholar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrowser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnsure broad coverage across multidisciplinary sources\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScopus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Athens (UJ Oline Library)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccesses high-quality, peer-reviewed journal articles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeb of Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen Athens (UJ Oline Library)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProvides publications with strong research impact and citations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Search Strategy\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo ensure a comprehensive collection of literature related to the topic \" IoT and Cloud Computing Issues, Challenges, and Opportunities in Biological Water Monitoring\", a systematic search was conducted using three primary academic databases: Scopus, Web of Science, and Google Scholar. These databases were selected due to their extensive coverage of peer-reviewed scientific and technical publications relevant to the domains of Internet of Things (IoT), cloud computing, and environmental monitoring. The search strategy employed a carefully constructed combination of keywords and Boolean operators designed to retrieve relevant studies. The main search terms included:(\"IoT\" OR \"Internet of Things\") AND (\"cloud computing\") AND (\"biological water monitoring\" OR \"water quality\" OR \"biological contamination\" OR \"bacteria\" OR \"microbial\") AND (\"issues\" OR \"challenges\" OR \"opportunities\").The search was limited to publications that met the inclusion criteria, namely studies published between 2015 and 2025, written in English, and explicitly addressing the issues, challenges, or opportunities associated with IoT and cloud computing in the context of biological water monitoring. Studies were excluded if they did not focus on this intersection, lacked a clear research framework or methodology, were outside the specified date range, or were published in languages other than English. The retrieved studies were exported to Microsoft Excel for duplication and screening, after which they proceeded to the eligibility and full-text review stages as described in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e. The process is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, a PRISMA flow diagram was suggested to visually document the study selection process, providing a clear and transparent overview of the search and screening process.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Selection Process\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe screening and selection of relevant literature followed a structured approach to ensure the quality and relevance of the studies included in this review. Articles were initially retrieved from SCOPUS, Web of Science, and Google Scholar, and all records were exported to Microsoft Excel for organization, duplication, and documentation. The inclusion criteria specified that articles must focus on IoT and cloud computing issues, challenges, and opportunities in biological water monitoring. Only studies published between 2015 and 2025 were considered, and eligible works were required to be written in English. Furthermore, each selected article had to include a clear research framework or methodology relevant to the integration of IoT and cloud computing technologies in monitoring biological parameters such as microbial activity, bacterial content, or other biological indicators in water systems. Studies were excluded if they did not specifically address the intersection of IoT and cloud computing in biological water monitoring, or if they were focused on unrelated technological or environmental domains. Articles that lacked a relevant methodological framework, were published outside the 2015\u0026ndash;2025-time range or were written in languages other than English were also omitted. The screening process began with a title and abstract review to eliminate clearly irrelevant records, followed by a full-text analysis to assess alignment with the inclusion criteria. This rigorous filtering ensured that only studies with strong methodological grounding and direct relevance to the review topic were selected for further analysis. To enhance the reliability of the findings, a team of four reviewers independently conducted the screening process. Each study was assessed by at least two reviewers. To further minimize potential bias, a fifth independent reviewer was designated to resolve any disagreements or inconsistencies through consensus-building. This structured and collaborative approach ensured a transparent, rigorous, and unbiased selection process. The diagram below visually summarizes the selection process (Khanyi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thobejane et al., 2024; Skosana et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mkhize et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Collection Process\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData for this review was gathered through Google Scholar, Web of Science, and Scopus, with three independent reviewers participating to guarantee accuracy, reliability, and reduce possible biases. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the data collection process, outlining each stage to improve clarity and replicability. At first, every reviewer independently gathered data from the chosen studies, concentrating on key aspects like study features, results, and particular IoT-cloud computing metrics pertinent to biological water monitoring. This deliberate extraction method, intentionally free from automation tools, facilitated a thorough and detailed approach. After individual extraction, the reviewers performed cross-checks to ensure consistency and precision. Any differences found during this phase were addressed through direct conversations, guaranteeing a cohesive dataset. In cases where several reports related to one study, particular decision criteria were used to choose the most thorough and current information. After verifying accuracy and completeness, the gathered data was compiled into an Excel database for final validation and further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Items\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn line with the research focus on IoT and cloud computing issues, challenges, and opportunities in biological water monitoring, a structured approach was adopted for extracting key data items from the selected literature. Each study was assessed based on specific technical and contextual parameters critical to the implementation of smart water monitoring systems. The data items extracted included: study title, year of publication, and the source database (Google Scholar, Scopus, or Web of Science), as well as the type of cloud platform used (e.g., AWS IoT, Azure, ThingSpeak, Blynk, or custom servers). Additional attributes involved integration with microcontrollers, data upload methods (such as MQTT, HTTP, or API), and data storage mechanisms (e.g., SQL, JSON, or real-time databases).To evaluate architectural efficiency, the studies were categorized based on whether they used edge processing, cloud-only, or hybrid architectures. Security mechanisms were also documented, including encryption standards, authentication protocols, and access control measures. Privacy concerns, such as data anonymization and mitigation techniques, were considered, along with latency handling and the system\u0026rsquo;s real-time responsiveness. Special attention was given to scalability challenges (e.g., network constraints, energy limitations, and hardware issues) and opportunities for future development, including the integration of AI, system expansion, or predictive analytics capabilities.This multidimensional data extraction framework enabled a consistent and comparative evaluation across the literature, as seen in prior systematic technology reviews (Myataza et al, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gumede et al, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mudau et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mtjilibe et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such detailed categorization helped identify trends, limitations, and future potential within IoT-cloud systems tailored for biological water quality monitoring.In total, 80 studies were initially retrieved from three major academic repositories 48 from Google Scholar, 24 from Web of Science, and 8 from Scopus (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Following the application of inclusion and exclusion criteria, 4 studies were selected for detailed analysis and scoring based on the established evaluation matrix.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1. Data Collection Method\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data collection process in this study followed a systematic and structured methodology to ensure that relevant literature was accurately identified, filtered, and analyzed in alignment with the research objectives. The process was divided into five key phases (Myataza et al, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gumede et al, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mudau et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mtjilibe et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing the selection of relevant studies, data were extracted using a standardized template. This template captured both bibliographic information (including Study ID, Title, Year, and Repository) and technical parameters such as the cloud platform used (e.g., AWS IoT, Azure, ThingSpeak), microcontroller integration, system architecture (cloud, edge, or hybrid), communication protocols (e.g., MQTT, HTTP, API), and data storage methods (SQL, JSON, or real-time databases). In addition, each study was analyzed for key technological factors, including scalability challenges (e.g., network and hardware limitations), opportunities for future expansion (e.g., AI integration or system scalability), and system capabilities in terms of latency, real-time monitoring, and responsiveness. Security and privacy features were also evaluated, with attention to encryption protocols, authentication mechanisms, data anonymization, and compliance with regulatory standards.To enable comparative analysis, a scoring matrix method was used to rate each study across a set of defined technical and functional criteria. This scoring facilitated the classification of literature into three analytical themes issues, challenges, and opportunities highlighting current trends and identifying technological gaps in the use of IoT and cloud computing in biological water quality monitoring. This structured method ensured the reliability, depth, and reproducibility of the data collection process.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2. Collected Data Variables Definition\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, several key variables were identified and extracted from the selected literature to evaluate the integration of IoT and cloud computing in biological water monitoring. These variables were chosen based on their relevance to system performance, scalability, data handling, and the overall effectiveness of the monitoring architecture.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePaper ID: A unique identifier assigned to each reviewed study to facilitate organization and reference during the analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTitle: The full title of the research paper as published.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eYear of Publication: The year in which the study was officially published, providing temporal context for the technology used.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eResearch Database: Indicates the database or repository (e.g., Google Scholar, Scopus, Web of Science) where the study was retrieved, reflecting the study's accessibility and indexing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCloud Platform Used: Specifies the cloud service (e.g., ThingSpeak, Blynk, AWS IoT, Azure, or custom solutions) used for data storage, analysis, and visualization.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIntegration with Microcontroller: Details the type of hardware (e.g., Arduino, ESP32, Raspberry Pi) used for sensing and processing at the edge.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScalability Challenges: Identifies any technical or infrastructural issues that hinder system scalability, such as limited network coverage or energy constraints.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOpportunities for Future Expansion: Outlines prospects for system enhancement, such as AI integration, predictive analytics, or broader geographic deployment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eData Upload Method: Describes the protocol used to transmit sensor data to the cloud (e.g., MQTT, HTTP, API), which impacts latency and efficiency.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eData Storage Mechanism: Indicates the format or system used to store collected data (e.g., SQL, JSON, real-time databases), influencing retrieval and analysis capabilities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEdge vs. Cloud Processing Architecture: Specifies whether processing occurs primarily at the edge, in the cloud, or in a hybrid model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecurity Mechanisms: Details the presence of encryption, authentication methods, and other security protocols to protect transmitted and stored data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrivacy Concerns and Mitigations: Notes any attention given to user or environmental data privacy, including anonymization techniques or compliance with regulatory standards.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLatency and Real-Time Response Metrics: Reports on the system\u0026rsquo;s responsiveness, such as data refresh rate or delay in alerts, which is crucial for real-time monitoring scenarios.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLink to Full Text: Provides a URL or DOI for direct access to the full paper, ensuring transparency and verifiability.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThese variables were systematically extracted and tabulated to enable comparative scoring and synthesis, guiding the evaluation of current technological approaches and identifying areas for future research and innovation in IoT-based water quality monitoring systems.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Variables Collected.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeployment region, monitoring environment, sample size, IoT/cloud architecture, and communication protocol.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncludes key performance metrics (sensor accuracy, data latency, uptime, energy use), their alignment with monitoring goals, and scalability impacts on system growth.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncludes the investment valuation of IoT/cloud deployments, the competitive advantages gained, and the overall ROI.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal influences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncludes the vendor ecosystem, adoption and demand trends, analytics-algorithm updates, emerging technological innovations, and environment‐specific monitoring drivers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Effect Measures\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIt is important to specify the effect measures used for each outcome when compiling results in IoT and cloud-based biological water monitoring to ensure accuracy and clarity in the results presentation. Setting criteria for study inclusion is an essential element of any statistical synthesis, as it involves making subjective decisions that may affect the results. These decisions need to be made clearly. For example, systematic methods like tabulating and coding important features like monitoring environments, system architectures, and performance metrics can help determine which studies are eligible for synthesis. For instance, studies assessing the efficacy of various IoT-cloud implementations may be coded based on standards like real-time data transmission, energy efficiency, or system scalability.\u003c/p\u003e \u003cp\u003eFor every synthesized outcome, effect measures (i.e., risk ratios for categorical outcomes, and mean differences for continuous outcomes) should be reported. This makes it as easy as possible to compare and comprehend different monitoring system configurations and how they influence the performance and reliability of biological water quality assessments. In that way, reviewers can ensure that the synthesis accurately reflects the data collected by providing a transparent basis for their conclusions and by revealing the techniques used to select and group studies for synthesis, including the criteria and coding methods.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Synthesis Methods .\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1. Eligibility Assessment and Study Selection Criteria for Synthesis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe synthesis process for evaluating the application of IoT and cloud computing in biological water monitoring involved systematically organizing studies into key thematic areas (Myataza et al, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gumede et al, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mudau et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mtjilibe et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This was accomplished by careful screening and data extraction, in accordance with the specified inclusion and exclusion criteria detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The criteria guaranteed that every study concentrated on the significance of IoT and cloud computing in biological water monitoring, encompassed a research framework, was published in English, and was within the publication period of 2015 to 2025. Research that met these criteria was gathered in an Excel spreadsheet, where information was extracted and arranged based on the categories presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSynthesis-Specific Grouping.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Extracted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Details\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaper ID, Research Title, Year of Publication, Online Database\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCloud Platform Used, Integration with Microcontrollers, Scalability Challenges, Opportunities for Future Expansion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethods of Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Upload Method, Data Storage Mechanism, Edge vs. Computing Processing Architecture, Security Mechanisms, Privacy Concerns and Mitigations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes and Impacts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatency and Real-time Response Metrics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe eligibility process for synthesis included multiple organized steps to guarantee consistency and transparency. Initially, studies were evaluated based on the inclusion and exclusion criteria to ensure they match the review\u0026rsquo;s emphasis on IoT, and cloud computing uses in biological water monitoring. This evaluation examined relevance, the presence of a research framework, language, and adherence to publication date. After the primary screening, a thorough extraction of data was conducted, systematically documenting study specifics, contextual elements, methodologies, and results. The extracted features were subsequently compared to established synthesis categories, including system design, energy efficiency, data latency, and scalability, enabling precise classification. For studies that demonstrated significant results in various areas or seemed like borderline cases, subjective evaluations were conducted to categorize them into the most pertinent synthesis group according to their main contribution. This organized method guaranteed that the classification for synthesis was clear, uniform, and indicative of the studies' influence on IoT and cloud-connected water quality monitoring systems\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.8.2. Data Preparation and Processing Methods for Synthesis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo maintain consistency and comparability among the data obtained from the analyzed studies, particular data preparation and transformation techniques were employed. These techniques tackled problems associated with absent data and inconsistencies in data formats, guaranteeing that all information was consistently arranged for presentation and synthesis. The methods for data preparation for review are listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Preparation Methods for Review.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMethods Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHandling Missing Information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStudies lacking essential information were excluded from the review. For studies providing data in ranges (e.g., survey responses between 90\u0026ndash;120 participants), midpoint estimates were used to standardize the figures.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Conversions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFractions and percentages were converted to decimals using Microsoft Excel, ensuring uniformity and facilitating direct comparisons across all data points.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.8.3. Methods for Tabulating and Visualizing Study Results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo successfully convey the findings of this systematic review, we utilized a mix of tabular and graphical approaches as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e. These methods were chosen to guarantee a clear, understandable, and thorough presentation of the results concerning IoT and cloud computing applications in biological water monitoring. We arranged tables to systematically organize and compare essential findings from various studies. These tables contained information on study contributions, advantages, challenges of IoT-cloud integration, and effects on biological monitoring systems. To emphasize relevance, tables were arranged according to factors such as publication year and citation counts, showcasing the most impactful studies in the field. Microsoft Excel and Microsoft Word were utilized to produce visual displays, such as pie charts, graphs, and flow charts. Pie charts depict the distribution of studies focusing on different elements, including system architecture, performance indicators, or implementation issues. Graphs presented trends over time and distributions of study results, facilitating the recognition of patterns and relationships. Flowcharts illustrated the study selection and synthesis process, providing a clear visual overview of the methodological steps carried out. These visual tools were created to improve clarity, enabling readers to swiftly grasp essential insights and trends. By combining these visual tools with comprehensive tabulation, we sought to deliver a sophisticated and easily understandable summary of the compiled data.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 8.\u003c/strong\u003e Data Presentation and Synthesis Workflow.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSteps\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.9428%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"0\" style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e1. Data Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"0\" style=\"width: 67.8254%;\"\u003e\n \u003cp\u003eCollect raw data from reviewed studies.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e2. Data Preparation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.14%;\"\u003e\n \u003cp\u003eAddresses missing information and perform data conversions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e3.Tabulation Methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.14%;\"\u003e\n \u003cp\u003eStructure tables to include study contributions, benefits, challenges, and impacts; order tables by publication year and citation count.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e4.Graphical Methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.14%;\"\u003e\n \u003cp\u003eCreate pie charts, graphs, and flow charts to visually represent study selection and outcome\u003c/p\u003e\n \u003cp\u003edistribution.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e5.Presentation of Results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.14%;\"\u003e\n \u003cp\u003eCombine tabular and graphical methods to offer a comprehensive and transparent view of\u003c/p\u003e\n \u003cp\u003efindings.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.4763%;\"\u003e\n \u003cp\u003e6.Review and finalize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72.14%;\"\u003e\n \u003cp\u003eReview for completeness and accuracy; prepare results for inclusion in the review.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003c/br\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.8.4. Methods for Data Synthesis and Meta-Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDue to the considerable variability among the studies included, a conventional meta-analysis was considered unfeasible. Rather, structured summaries and descriptive statistics were employed to consolidate the findings, as this method allowed for the differences in study designs and outcome measures. Microsoft Excel and Microsoft Word aided in the creation of tables, graphs, pie charts, and flow charts, which efficiently structured and showcased the varied data, enabling the recognition of patterns and trends. The organized summaries provided a thorough insight into the advantages and difficulties of integrating IoT and cloud computing in biological water monitoring, showcasing the variety of contexts and results in the research. This synthesis approach provided a clear and approachable display of results, considering the distinct features of every study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.8.5. Investigation of Heterogeneity Sources\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo investigate possible sources of heterogeneity in the study outcomes, we performed subgroup analyses that assessed differences according to factors like deployment context, monitoring goals, and particular IoT-cloud system designs. For example, we classified studies based on their monitoring settings (e.g., aquaculture, freshwater lakes, or industrial effluent monitoring) to examine whether the efficacy of IoT-cloud integration differed among applications. We furthermore examined how the system configuration (e.g., cloud-only versus hybrid architecture) affects monitoring results. These subgroup analyses facilitated the comparison of outcomes across varying levels of each factor, employing statistical interaction tests to assess whether the noted effects significantly varied among subgroups. Since a meta-analysis could not be conducted because of missing standardized effect estimates, results were compiled in tables to facilitate a visual evaluation of how these subgroup factors affected the effectiveness and challenges of IoT-cloud systems across various biological monitoring scenarios. Although these analyses offered important insights, they were exploratory instead of being pre-defined in our protocol. Thus, the results must be viewed carefully, considering the constraints of the existing data and the techniques used.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.8.6. Sensitivity Analyses\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe review conducted a detailed examination of the main technology providers of the leading providers\u0026mdash;AWS, Microsoft Azure, and Google Cloud\u0026mdash;considering deployment modes such as on-premises, cloud-based, and hybrid environments relevant to biological water monitoring. It examined the methodological paradigms of the reviewed studies, such as study designs (quantitative, qualitative, or mixed methods), sample sizes, and participant characteristics, to provide an image of methodological diversity across the literature. Methods of data collection\u0026mdash;such as interviews, questionnaires, direct observation, and document analysis\u0026mdash;were evaluated, as were analytical methods used, ranging from statistical analysis to thematic interpretation. When synthesizing among studies, performance measures by IT parameters (such as data processing speed, scalability, and precision of the system), business parameters (such as operational efficiency, cost, and revenue effect), and organizational parameters (such as user satisfaction and stakeholder involvement) were looked at. Long-term effects like strategic advantage and sustained growth were also considered. By structuring the literature in a systematic manner along these axes, the research was able to discern emerging trends, overcome methodological heterogeneity, and lay a solid groundwork for future research agendas.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Reporting Bias Assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo ensure the credibility and reliability of this systematic review on IoT and cloud computing challenges and opportunities in biological water monitoring, a careful assessment of potential reporting bias was conducted. Reporting bias refers to the selective revealing or suppression of research findings, which can lead to a skewed understanding of the topic (Higgins et al., 2019). In this study, efforts were made to minimize such bias by adhering to a comprehensive and transparent search strategy that included multiple reputable databases such as Google Scholar, Web of Science, and SCOPUS. To further address bias, inclusion and exclusion criteria were consistently applied across all records, and study selection was independently verified. The full texts were reviewed not only for relevance but also for the completeness of reported technical details such as cloud platform usage, data protocols, scalability challenges, and security mechanisms. Where potential publication bias was suspected such as the overrepresentation of studies with positive outcomes or commercial cloud integrations it was noted during data extraction. Any missing or unclear data was acknowledged rather than excluded to avoid selective reporting.By maintaining transparency in the study identification, screening, and synthesis process, this review aimed to mitigate the effects of reporting bias and provide a balanced overview of current trends and gaps in IoT-cloud integration for water quality monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Certainty Assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo make sure the findings in this review are trustworthy, each study was carefully checked for how strong and reliable its evidence is. This was done by looking at several things: how clearly the study used cloud technology, what kind of communication protocols it used (like MQTT or HTTP), the system design (whether it used edge, cloud, or both), and whether it talked about security and privacy. We also looked at how well the system handled real-time data, if it could grow to handle more devices (scalability), and whether other people could repeat the work based on what was written. If a study explained all of these clearly and in detail, it was rated as high certainty. If the study left out important details or had unclear methods, it was rated as medium or low certainty. This way of checking evidence follows the updated guidelines suggested by Sch\u0026uuml;nemann et al. (2022), which help make sure that technology reviews are fair and consistent.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis section synthesizes findings from a diverse body of studies examining the integration of IoT and cloud computing technologies within biological water monitoring systems. The review highlights that IoT, and cloud platforms together create a transformative technological framework capable of enabling real-time, high-resolution monitoring of aquatic environments. These technologies enhance data acquisition, transmission, storage, and analysis, thereby addressing key limitations of traditional water quality monitoring methods. Research across the reviewed literature reveals that cloud integrated IoT systems significantly improve the scalability, responsiveness, and automation of biological monitoring networks. Such systems support the continuous tracking of vital biological indicators such as algal blooms, microbial content, and nutrient levels by linking remote sensing devices with cloud-based analytical platforms. Moreover, the adoption of cloud computing not only facilitates centralized data management and remote access but also strengthens decision-making through real-time visualization and predictive modeling. The results further underscore the importance of architectural choices, including edge-cloud hybrid systems, as well as considerations around security, data latency, energy efficiency, and interoperability. These technical and operational dimensions collectively influence the reliability and sustainability of IoT-enabled water monitoring solutions. Through a systematic literature synthesis, this review identifies best practices, recurring challenges, and emerging opportunities that can guide future deployment and research in the field of smart water quality monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Selection Results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the PRISMA-compliant flow diagram outlining the systematic review\u0026rsquo;s screening and inclusion process. A total of 17,872 records were identified from three databases, with Google Scholar contributing the majority. After title and abstract screening, 17,792 records were excluded based on relevance and predefined exclusion criteria. All 80 remaining reports were retrieved and assessed for eligibility, ultimately being included in the final systematic literature review (SLR). This rigorous selection process ensured a focused, high-quality dataset for analysis of IoT and cloud computing applications in biological water monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Study Results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, scholarly interest in the integration of IoT and cloud computing for biological water monitoring has seen notable fluctuations over the past decade. The number of relevant studies peaked in 2020 with 21 publications, followed by a secondary peak in 2023 with 12 studies. This trend suggests a growing recognition of the critical role these technologies play in environmental monitoring, particularly in the context of increased global attention to water security and sustainable development goals. The decline in 2024 and 2025 may reflect either a saturation of foundational research or a shift toward more specialized, empirical implementation studies that fall outside broad review criteria.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays the distribution of academic sources used in this systematic review. The majority of studies were retrieved from Google Scholar (60%), followed by Scopus (30%) and Web of Science (10%). This reflects a broad, inclusive search strategy that prioritizes accessibility and multidisciplinary coverage in identifying relevant IoT and cloud computing studies for biological water monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e highlights the diversity of cloud platforms employed in the reviewed literature. A significant proportion of studies (45%) utilized custom or private cloud solutions, reflecting a preference for configurable and secure architectures tailored to specific monitoring requirements. Open platforms like ThingSpeak accounted for 13.75% of implementations, while commercial solutions such as AWS IoT, Blynk, and Azure collectively made up less than 15% of the total. Notably, over a quarter of the studies did not specify the backend used, indicating gaps in reporting and potential reproducibility concerns.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e illustrates the variety of microcontroller and sensor platforms employed in the reviewed studies. Arduino-based systems led in usage (16.25%), closely followed by general-purpose microcontroller setups (15%), often customized for specific environmental monitoring scenarios. ATmega and PIC microcontrollers represented a notable 11.25%, valued for their simplicity and low power demands. ESP-based systems (8.75%) and Raspberry Pi platforms (7.5%) were also popular for their processing power and wireless capabilities. The figure reflects a preference for accessible, low-cost, and modular solutions, which are essential for scalable IoT deployments in biological water monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e summarizes the range of technical challenges identified across the reviewed literature. General network and coverage issues were cited most frequently (15.05%), underscoring the persistent difficulty of ensuring reliable connectivity in remote or aquatic environments. GSM/Zigbee bandwidth constraints (11.83%) and LoRa limitations (9.67%) were also frequently mentioned, reflecting trade-offs between communication range, payload size, and energy consumption. Other barriers included infrastructure-related costs (8.6%), scalability issues, and inconsistent communication protocols\u0026mdash;all of which hinder the broader adoption of IoT-cloud systems for real-time biological water monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003e presents the distribution of communication technologies implemented in IoT-based water monitoring systems. Web protocols such as HTTP were the most utilized (28.75%), reflecting their ease of integration with cloud services and dashboards. API-based interfaces accounted for 22.5%, enabling modular data exchange across platforms. MQTT, a lightweight IoT-specific protocol, was used in 17.5% of cases due to its efficiency in bandwidth-limited environments. Surprisingly, nearly 14% of studies did not specify the communication method, underscoring the need for more transparent technical reporting in future research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e14\u003c/span\u003e, system architecture in IoT-enabled biological water monitoring varies considerably. Over half of the reviewed studies (52.94%) implemented cloud-centric designs, leveraging cloud platforms for real-time processing and storage. Hybrid edge-cloud solutions were observed in 23.53% of cases, supporting more responsive local analytics. A smaller portion of studies employed embedded-only architectures or unspecified configurations, indicating room for further development in adaptive and distributed processing frameworks tailored to energy-constrained or bandwidth-limited field environments.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e15\u003c/span\u003e, a significant proportion (55%) of the reviewed studies lacked explicit mention of security or privacy mechanisms, underscoring a major gap in reporting and system robustness. Where measures were reported, encryption (17.5%) and authentication (6.25%) were most common, followed by access control, secure communication, and token-based authorization. Advanced techniques like federated privacy and cryptographic privacy frameworks were rarely employed, reflecting the early stage of maturity in securing IoT-cloud biological monitoring infrastructures.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e16\u003c/span\u003e, data privacy remains an underreported aspect in IoT-cloud environmental monitoring systems. Half of the studies reviewed made no mention of privacy mechanisms, raising concerns about data sensitivity and regulatory compliance. Among those that did, data anonymization was the leading technique (30%), followed by general privacy awareness initiatives (11.25%). Minimal adoption of access control and other advanced privacy frameworks highlights the need for more rigorous privacy-by-design strategies in future deployments.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Study reearch questions results\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003e1. What are the most critical infrastructural and technical issues of the integration of IoT and cloud computing technologies with biological water monitoring systems?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe most critical technical issues identified include limited network coverage (15.05%), GSM and Zigbee bandwidth constraints (11.83%), and LoRa limitations (9.67%). These hinder reliable data transmission, especially in remote or aquatic environments. Deployment cost was also cited as a major challenge (8.6%). Furthermore, 55% of studies failed to specify any security protocols, and 50% omitted privacy mechanisms entirely, indicating a widespread lack of attention to data protection. Additionally, many systems lacked architectural clarity\u0026mdash;26.25% did not specify which cloud platform was used, and 14% did not describe the communication protocols\u0026mdash;affecting system reproducibility and standardization.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2. In what ways do geographical, economic, and environmental factors influence the scalability and efficiency of cloud-integrated IoT systems in actual deployment?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGeographical and environmental factors significantly affect the scalability of IoT systems. Poor connectivity in rural or underwater locations limits the functionality of GSM, Wi-Fi, and even LoRa modules. Economically, regions with limited funding opt for open-source or low-cost platforms\u0026mdash;reflected by the 45% use of custom/private clouds and 13.75% use of ThingSpeak. Environmentally, sensor durability is impacted by factors such as algae growth, water chemistry, and temperature fluctuations. These issues demand adaptive, context-specific systems that can perform reliably under varying local conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3. What are technologies, approaches, and architecture patterns that have been proposed to address concerns such as energy usage, data protection, and system interoperability in cloud-IoT technology?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo address energy and latency concerns, 23.53% of studies implemented hybrid edge-cloud architectures, reducing data transmission loads by enabling local processing. For communication, MQTT (17.5%) emerged as a preferred low-power, IoT-optimized protocol. In terms of security and privacy, encryption (17.5%) and authentication (6.25%) were the most common methods used, though underreported. Microcontrollers like ESP32 and low-power platforms such as Arduino and Raspberry Pi were frequently used (together over 30%), due to their balance between functionality and energy efficiency. Interoperability, however, remains a challenge due to the lack of standard communication frameworks across studies.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4. What are some of the new trends or opportunities in smart environmental monitoring that can be harnessed to enhance biological water quality monitoring, especially in automation and data analysis?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEmerging trends include the use of AI and machine learning models\u0026mdash;such as SVM, PCA, and random forest\u0026mdash;to predict harmful algal blooms (HABs) with high accuracy. The increasing use of edge computing allows real-time, in-field analytics, reducing cloud dependency. Open cloud platforms like ThingSpeak and mobile-friendly dashboards improve user access and public engagement. However, most studies have yet to adopt advanced privacy-preserving analytics or federated learning, signaling future opportunities. These innovations offer promising avenues to enhance responsiveness, scalability, and intelligence in biological water monitoring systems.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis systematic review synthesized findings from 80 studies published between 2015 and 2025 on the integration of IoT and cloud computing in biological water monitoring. The review highlights both technological advancements and critical gaps in current implementations. Custom or private cloud platforms were the most frequently used (45%), while ThingSpeak (13.75%) and AWS IoT (8.75%) represented notable open-access and commercial alternatives. On the hardware side, Arduino-based (16.25%) and general-purpose microcontrollers (15%) dominated deployments.\u003c/p\u003e \u003cp\u003eCommunication protocols were highly varied, with HTTP (28.75%), API interfaces (22.5%), and MQTT (17.5%) being the most utilized. However, over 55% of the studies did not specify any security mechanisms, and 50% lacked any mention of privacy-preserving measures\u0026mdash;despite the sensitive nature of environmental data. Encryption (17.5%) and data anonymization (30%) were the most common techniques where privacy and security were reported. In terms of architectural design, more than half (52.94%) of the systems relied solely on cloud-based processing, with limited uptake of edge or hybrid edge-cloud configurations that are essential for reducing latency and improving system resilience. Network limitations (15.05%), GSM/Zigbee bandwidth issues (11.83%), and high deployment costs (8.6%) were the top barriers reported.\u003c/p\u003e \u003cp\u003eMoving forward, robust, context-aware architectures that integrate edge computing, AI-based analytics, and lightweight protocols are necessary. Future systems must prioritize not only functionality and cost-effectiveness but also data protection and scalability. To enable this, standardized reporting practices, open benchmarks, and cross-disciplinary collaboration will be essential in advancing smart water quality monitoring systems aligned with SDG 6: Clean Water and Sanitation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdebayo AI, Olubanjo KT, Fadeke AM, Uyanah JJ, Zirra AT, Igbaoreto WA, Fakoyede PD (2024) From static sampling to dynamic insights: The future of water quality monitoring with sensors, IOT, and drones. Sci World J 20(1):454\u0026ndash;466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeleke IA, Nwulu NI, Ogbolumani OA (2023) A hybrid machine learning and embedded IoT-based water quality monitoring system, vol 22. Internet of things, p 100774\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgbehadji IE, Mabhaudhi T, Botai J, Masinde M (2023) A systematic review of existing early warning systems\u0026rsquo; challenges and opportunities in cloud computing early warning systems. Climate, 11(9), p.188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjith JB, Manimegalai R, Ilayaraja V (2020) February. An IoT based smart water quality monitoring system using cloud. In 2020 International conference on emerging trends in information technology and engineering (ic-ETITE) (pp. 1\u0026ndash;7). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlharbe N, Atkins AS, Champion J (2015) Use of cloud computing with wireless sensor networks in an Internet of Things environment for a smart hospital network. eTELEMED 2015, p.63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlprol AE, Mansour AT, Ibrahim MEED, Ashour M (2024) Artificial intelligence technologies revolutionizing wastewater treatment: Current trends and future prospective. Water, 16(2), p.314\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmador-Castro F, Gonz\u0026aacute;lez-L\u0026oacute;pez ME, Lopez-Gonzalez G, Garcia-Gonzalez A, D\u0026iacute;az-Torres O, Carbajal-Espinosa O, Gradilla-Hern\u0026aacute;ndez MS (2024) Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication. Journal of Environmental Management, 352, p.119959\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade GA, Esteban S, Risco-Mart\u0026iacute;n JL, Chac\u0026oacute;n J, Besada-Portas E (2024) Modeling-and Simulation-Driven Methodology for the Deployment of an Inland Water Monitoring System. Information, 15(5), p.267\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArratia B, Rosas E, Calafate CT, Cano JC, Cecilia JM, Manzoni P (2024) AlLoRa: Empowering environmental intelligence through an advanced LoRa-based IoT solution. Comput Commun 218:44\u0026ndash;58\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzhari M, Abarda A, Ettaki B, Zerouaoui J, Dakkon M (2020) Higgs boson discovery using machine learning methods with pyspark. Procedia Comput Sci 170:1141\u0026ndash;1146\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandara RMPNS, Jayasignhe AB, Retscher G (2025) The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review. Sensors, 25(6), p.1918\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarabde M, Danve S (2015) Real time water quality monitoring system. Int J Innovative Res Comput Communication Eng 3(6):5064\u0026ndash;5069\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodaragama BT, Miyurangana EM, Jayakod YTL, Vipulasiri DD, Rajapaksha US, Krishara J (2024) April. IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1\u0026ndash;6). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBokingkito PB Jr, Llantos OE (2017) Design and implementation of real-time mobile-based water temperature monitoring system. Procedia Comput Sci 124:698\u0026ndash;705\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusari I, Sahoo D, Harmel RD, Haggard BE (2023) A review of machine learning models for harmful algal bloom monitoring in freshwater systems. J Nat Resour Agricultural Ecosyst 1(2):63\u0026ndash;76\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabra J, Castro D, Colorado J, Mendez D, Trujillo L (2017) June. An IoT approach for wireless sensor networks applied to e-health environmental monitoring. In 2017 IEEE international conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 578\u0026ndash;583). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapella JV, Bonastre A, Ors R, Peris M (2019) A new application of internet of things and cloud services in analytical chemistry: Determination of bicarbonate in water. Sensors, 19(24), p.5528\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandel P, Mahajan D, Thakur K, Kumar R, Kumar S, Brar B, Sharma D, Sharma AK (2024) A review on plankton as a bioindicator: A promising tool for monitoring water quality. World Water Policy 10(1):213\u0026ndash;232\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang NB, Imen S, Vannah B (2015) Remote sensing for monitoring surface water quality status and ecosystem state in relation to the nutrient cycle: a 40-year perspective. Crit Rev Environ Sci Technol 45(2):101\u0026ndash;166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChapra SC, Boehlert B, Fant C, Bierman VJ Jr, Henderson J, Mills D, Mas DM, Rennels L, Jantarasami L, Martinich J, Strzepek KM (2017) Climate change impacts on harmful algal blooms in US freshwaters: a screening-level assessment. Environ Sci Technol 51(16):8933\u0026ndash;8943\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan AS, Panikulangara L, Jain R (2022) September. JAL MITRA water quality monitoring and reporting using IoT, cloud \u0026amp; analytics. In 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 376\u0026ndash;381). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChavhan N, Bhattad R, Khot S, Patil S, Pawar A, Pawar T, Gawli P (2025) APAH: An autonomous IoT driven real-time monitoring system for Industrial wastewater. Digital Chemical Engineering, 14, p.100217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdury MSU, Emran TB, Ghosh S, Pathak A, Alam MM, Absar N, Andersson K, Hossain MS (2019) IoT based real-time river water quality monitoring system. Procedia Comput Sci 155:161\u0026ndash;168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalal S (2023) August. Machine learning model for water quality evaluation: Systematic review. In 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon) (pp. 1013\u0026ndash;1018). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas B, Jain PC (2017) July. Real-time water quality monitoring system using Internet of Things. In 2017 International conference on computer, communications and electronics (Comptelix) (pp. 78\u0026ndash;82). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeekshath R, Dharanya P, Kabadia MKD, Dinakaran MGD, Shanthini S (2018) IoT based environmental monitoring system using arduino UNO and thingspeak. Int J Sci Technol Eng 4(9):68\u0026ndash;75\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkosana S, Mlambo S, Madiope T, Thango B (2024) Evaluating Wireless Network Technologies (3G, 4G, 5G) and Their Infrastructure: A Systematic Review. Preprints 2024101331\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMkhize A, Mokhothu KD, Tshikhotho M, Thango BA (2025) Evaluating the Impact of Cloud Computing on SME Performance. Syst Rev Businesses 5(2):23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/businesses5020023\u003c/span\u003e\u003cspan address=\"10.3390/businesses5020023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az M, Mart\u0026iacute;n C, Rubio B (2016) State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. J Netw Comput Appl 67:99\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing S, Tukker A, Ward H (2023) Opportunities and risks of internet of things (IoT) technologies for circular business models: A literature review. J Environ Manage 336:117662\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsty JN, Mussa ASM, Fazle M, Rabbi MRH, Muhit MG, Shahriar N (2025) EcoGuard: Advancing IoT-based Aquaculture with Machine Learning for Enhanced Productivity and Automation. J IoT Social Mob Analytics Cloud 7(1):18\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores-Iwasaki M, Guadalupe GA, Pachas-Caycho M, Chapa-Gonza S, Mori-Zabarbur\u0026uacute; RC, Guerrero-Abad JC (2025) Internet of Things (IoT) Sensors for Water Quality Monitoring in Aquaculture Systems: A Systematic Review and Bibliometric Analysis. AgriEngineering, 7(3), p.78\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForhad HM, Uddin MR, Chakrovorty RS, Ruhul AM, Faruk HM, Kamruzzaman S, Sharmin N, Jamal ASIM, Haque MMU, Morshed AM (2024) IoT based real-time water quality monitoring system in water treatment plants (WTPs). Heliyon, 10(23)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrido-Momparler V, Peris M (2022) Smart sensors in environmental/water quality monitoring using IoT and cloud services. Trends Environ Anal Chem 35:e00173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeetha S, Gouthami SJSW (2016) Internet of things enabled real time water quality monitoring system. Smart Water 2:1\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh S, Ghosh K, Karamakar S, Prasad S, Debabhuti N, Sharma P, Tudu B, Bhattacharyya N, Bandyopadhyay R (2019) December. Development of an IOT based robust architecture for environmental monitoring using UAV. In 2019 IEEE 16th India Council International Conference (INDICON) (pp. 1\u0026ndash;4). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGim\u0026eacute;nez JG, Granero A, Senent-Aparicio J, G\u0026oacute;mez-Jakobsen F, Mercado JM, Blanco-G\u0026oacute;mez P, Ruiz JM, Cecilia JM (2024) Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study, vol 81. Ecological Informatics, p 102554\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGobler CJ (2020) Climate change and harmful algal blooms: insights and perspective, vol 91. Harmful algae, p 101731\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGopinath MP, Tamizharasi GS, Kavisankar L, Sathyaraj R, Karthi S, Aarthy SL, Balamurugan B (2019) A secure cloud-based solution for real-time monitoring and management of Internet of underwater things (IOUT). Neural Comput Appl 31:293\u0026ndash;308\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanyi MB, Xaba SN, Mlotshwa NA, Thango B, Matshaka L (2024) A Roadmap to Systematic Review: Evaluating the Role of Data Networks and Application Programming Interfaces in Enhancing Operational Efficiency in Small and Medium Enterprises. Sustainability 16(23):10192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su162310192\u003c/span\u003e\u003cspan address=\"10.3390/su162310192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThobejane LT, Thango BA (2024) Partial Discharge Source Classification in Power Transformers: A Systematic Literature Review. Appl Sci 14(14):6097\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaque H, Labeeb K, Riha RB, Khan MNR (2021) March. IoT based water quality monitoring system by using Zigbee protocol. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 619\u0026ndash;622). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemdan EED, Essa YM, El-Sayed A, Shouman M, Moustafa AN (2021) July. Smart water quality analysis using IoT and big data analytics: a review. In 2021 International Conference on Electronic Engineering (ICEEM) (pp. 1\u0026ndash;5). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHilal AM, Althobaiti MM, Eisa TAE, Alabdan R, Hamza MA, Motwakel A, Duhayyim A, Negm M (2022) N., An intelligent carbon-based prediction of wastewater treatment plants using machine learning algorithms. Adsorption Science \u0026amp; Technology, 2022, p.8448489\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuan J, Li H, Wu F, Cao W (2020) Design of water quality monitoring system for aquaculture ponds based on NB-IoT. Aquacultural Engineering, 90, p.102088\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuentenao H, Mu\u0026ntilde;oz C, Ot\u0026aacute;rola F, Huircan J, Somos M (2024) October. IoT System for Real-Time Monitoring of Turbidity in Lakes. In 2024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA) (pp. 1\u0026ndash;6). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MM, Kashem MA, Alyami SA, Moni MA (2023) Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival. Microprocess Microsyst 102:104930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJabbar WA, Ting TM, Hamidun MFI, Kamarudin AHC, Wu W, Sultan J, Alsewari AA, Ali MA (2024) Development of LoRaWAN-based IoT system for water quality monitoring in rural areas, vol 242. Expert systems with applications, p 122862\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMudau MC, Moshapo LW, Monyela TM, Thango BA (2024) The Role of Manufacturing Operations in SMEs Performance: A Systematic Review. Preprints 2024100539\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMtjilibe T, Rameetse E, Mgwenya N, Thango B (2024) Exploring the Challenges and Opportunities of Social Media for Organizational Engagement in SMEs: A Comprehensive Systematic Review. Available at SSRN 4998542\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJais NAM, Abdullah AF, Kassim MSM, Abd Karim MM, Muhadi NA (2024) Improved accuracy in IoT-Based water quality monitoring for aquaculture tanks using low-cost sensors: Asian seabass fish farming. Heliyon, 10(8)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJan F, Min-Allah N, D\u0026uuml;şteg\u0026ouml;r D (2021) Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications. Water, 13(13), p.1729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJothimani A, Edward AS, Gowthem KM, Karthikeyan R (2017) Implementation of smart sensor interface network for water quality monitoring in industry using iot. Indian J Sci Technol 10(6):1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026uacute;nior ACDS, Munoz R, Quezada MDL\u0026Aacute;, Neto AVL, Hassan MM, De Albuquerque VHC (2021) Internet of water things: A remote raw water monitoring and control system. IEEE Access 9:35790\u0026ndash;35800\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamaludin KH, Ismail W (2017) December. Water quality monitoring with internet of things (IoT). In 2017 IEEE Conference on Systems, Process and Control (ICSPC) (pp. 18\u0026ndash;23). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandasamy L, Mahendran A, Sangaraju SHV, Mathur P, Faldu SV, Mazzara M (2025) Enhanced remote sensing and deep learning aided water quality detection in the Ganges River, India supporting monitoring of aquatic environments. Results in Engineering, 25, p.103604\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalil S, Mahnashi MH, Hussain M, Zafar N, Khan FS, Afzal U, Shah GM, Niazi UM, Awais M, Irfan M (2021) Exploration and determination of algal role as Bioindicator to evaluate water quality\u0026ndash;Probing fresh water algae. Saudi J Biol Sci 28(10):5728\u0026ndash;5737\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan N, Khattak KS, Ullah S, Khan Z (2019) December. A low-cost IoT based system for environmental monitoring. In 2019 international conference on frontiers of information technology (FIT) (pp. 173\u0026ndash;1735). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyataza A, Mafunga M, Mkhulisi NS, Thango BA (2024) A Systematic Review of ERP, CRM, and HRM Systems for SMEs: Managerial and Employee Support. Preprints 2024100384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGumede TT, Chiworeka JM, Magoda AS, Thango B (2024) Building Effective Social Media Strategies for Business: A Systematic Review. Preprints 2024100379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim D, Lee K, Jeong S, Song M, Kim B, Park J, Heo TY (2024) Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data. Environmental Research, 262, p.119823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoditala NK, Pandey PS (2018) August. Water quality monitoring system using IoT and machine learning. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) (pp. 1\u0026ndash;5). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrishnan SR, Nallakaruppan MK, Chengoden R, Koppu S, Iyapparaja M, Sadhasivam J, Sethuraman S (2022) Smart water resource management using Artificial Intelligence\u0026mdash;A review. Sustainability, 14(20), p.13384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKukartsev V, Orlov V, Semenova E, Rozhkova A (2024) Optimizing water quality classification using random forest and machine learning. In BIO Web of Conferences (Vol. 130, p. 03007). EDP Sciences\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar D (2022) May. IoT and GSM Based Real-Time Water Monitoring System with Low Power Consumption. In Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021 (pp. 165\u0026ndash;172). Singapore: Springer Nature Singapore\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar D, Sharma H, Prakash A, Husain M (2024) Cloud Computing for IoT Sensing Data. Technological Advancements in Data Processing for Next Generation Intelligent Systems. IGI Global, pp 1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar J, Gupta R, Sharma S, Chakrabarti T, Chakrabarti P, Margala M (2024) IoT-Enabled Advanced Water Quality Monitoring System for Pond Management and Environmental Conservation. IEEE Access\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwon DH, Hong SM, Abbas A, Pyo J, Lee HK, Baek SS, Cho KH (2023) Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environ Eng Res, 28(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai L, Liu Y, Zhang Y, Cao Z, Yin Y, Chen X, Jin J, Wu S (2024) Long-term spatiotemporal mapping in lacustrine environment by remote sensing: Review with case study, challenges, and future directions. Water Res, p.122457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee C, Wang YJ (2020) Development of a cloud-based IoT monitoring system for Fish metabolism and activity in aquaponics. Aquacult Eng 90:102067\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JY (2025) Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods. Environmental Monitoring and Assessment, 197(4), p.405\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Ho L, Bruneel S, Goethals P (2024) Applications of unmanned vehicle systems for multi-spatial scale monitoring and management of aquatic ecosystems: A review. Ecological Informatics, p 102926\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahdzir MM, Yahaya SH, Fauadi MHFM, Yeow TT (2025) Integrating water quality model and aeration with IoT technology in water quality management: A conceptual framework. Multidisciplinary Sci J 7(3):2025139\u0026ndash;2025139\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMallick A, Barnwal RP (2025) January. A Scalable Framework for Multi-cloud IoT Data Synchronization. In Proceedings of the 26th International Conference on Distributed Computing and Networking (pp. 364\u0026ndash;369)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMallikarjun S, Mangalpady A (2025) A review on application of innovative techniques for sustainable environmental management in mines. Disaster Adv 18(5):191\u0026ndash;195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManoj M, Dhilip Kumar V, Arif M, Bulai ER, Bulai P, Geman O (2022) State of the art techniques for water quality monitoring systems for fish ponds using iot and underwater sensors: A review. Sensors, 22(6), p.2088\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta S, Singh A (2024) October. Integrating IoT and AI for Holistic Water Quality Monitoring and Management. In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT) (pp. 1\u0026ndash;5). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMezni H, Driss M, Boulila W, Atitallah SB, Sellami M, Alharbi N (2022) Smartwater: A service-oriented and sensor cloud-based framework for smart monitoring of water environments. Remote Sensing, 14(4), p.922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiasayedava L, McBride K, Tuhtan JA (2022) Automated environmental compliance monitoring of rivers with IoT and open government data. Journal of Environmental Management, 303, p.114283\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller T, Durlik I, Kostecka E, Kozlovska P, Łobodzińska A, Sokołowska S, Nowy A (2025) Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data. Electronics, 14(4), p.696\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra DR, Kumar A, Ramaswamy L, Boddula VK, Das MC, Page BP, Weber SJ (2020) CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms, vol 96. Harmful algae, p 101828\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMounir M, Chkouri MY, El Khamlichi Y, Touhafi A (2024) November. Emerging Technologies for Water Quality Monitoring and Assessment: A Systematic Review. In 2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE) (pp. 1\u0026ndash;8). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandhakumar S, Vengat R, Ramkumar T, Rakesh R, K (2020) IoT Based Pollution Monitoring System for Effective Industrial Pollution Monitoring and Control. Biosc. Biotech Res Comm, 13(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen CM, Mays J, Plesa D, Rao S, Nguyen M, Chiao JC (2015) May. Wireless sensor nodes for environmental monitoring in Internet of Things. In 2015 IEEE MTT-S International Microwave Symposium (pp. 1\u0026ndash;4). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishan RK, Akter S, Sony RI, Hoque MM, Anee MJ, Hossain A (2024) Development of an IoT-based multi-level system for real-time water quality monitoring in industrial wastewater. Discover Water, 4(1), p.43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyangaresi VO, AlRababah AA, Yenurkar GK, Chinthaginjala R, Yasir M (2025) Anonymous Authentication Scheme Based on Physically Unclonable Function and Biometrics for Smart Cities. Eng Rep 7(1):e13079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkpara EC, Sehularo BE, Wojuola OB (2022) On-line water quality inspection system: the role of the wireless sensory network. Environmental Research Communications, 4(10), p.102001\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlanubi OO, Akano TT, Asaolu OS (2024) Design and development of an IoT-based intelligent water quality management system for aquaculture. Journal of Electrical Systems and Information Technology, 11(1), p.15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlatomiwa L, Ambafi JG, Dauda US, Longe OM, Jack KE, Ayoade IA, Abubakar IN, Sanusi AK (2023) A review of Internet of Things-based visualisation platforms for tracking household carbon footprints. Sustainability, 15(20), p.15016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePamula AS, Ravilla A, Madiraju SVH (2022) Applications of the internet of things (IoT) in real-time monitoring of contaminants in the air, water, and soil. Engineering Proceedings, 27(1), p.26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey S, Mishra S, Jain RK (2023) May. IoT Interface Device for Sensing Arsenic in Contaminated Water. In 2023 International Conference on Recent Advances in Electrical, Electronics \u0026amp; Digital Healthcare Technologies (REEDCON) (pp. 1\u0026ndash;6). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark J, Kim KT, Lee WH (2020) Recent advances in information and communications technology (ICT) and sensor technology for monitoring water quality. Water, 12(2), p.510\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasika S, Gandla ST (2020) Smart water quality monitoring system with cost-effective using IoT. Heliyon, 6(7)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerumal T, Sulaiman MN, Leong CY (2015) October. Internet of Things (IoT) enabled water monitoring system. In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) (pp. 86\u0026ndash;87). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePranata AA, Lee JM, Kim DS (2017) June. Towards an IoT-based water quality monitoring system with brokerless pub/sub architecture. In 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) (pp. 1\u0026ndash;6). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasad AN, Mamun KA, Islam FR, Haqva H (2015) December. Smart water quality monitoring system. In 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE) (pp. 1\u0026ndash;6). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePule M, Yahya A, Chuma J (2017) Wireless sensor networks: A survey on monitoring water quality. J Appl Res Technol 15(6):562\u0026ndash;570\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian P, Liu G (2024) Application of High-Frequency Intelligent Sensing Network in Monitoring and Early Warning of Water Quality Dynamic Change. International Journal of Computational Intelligence Systems, 17(1), p.195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazman NA, Ismail W, Abd Razak WZ, Ismail MH, Jamaludin I, J (2023) Design and analysis of water quality monitoring and filtration system for different types of water in Malaysia. Int J Environ Sci Technol 20(4):3789\u0026ndash;3800\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha AA (1992) Algae as biological indicators of water pollution. A general approach, Algae and environment\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz-Vanoye JA, Diaz-Parra O, M\u0026aacute;rquez Vera MA, Fuentes-Penna A, Barrera-C\u0026aacute;mara RA, Ruiz-Jaimes MA, Toledo-Navarro Y, Bern\u0026aacute;be-Loranca MB, Simancas-Acevedo E, Trejo-Macotela FR, Vera-Jim\u0026eacute;nez MA (2025) A Comprehensive Review of Quality of Aquaculture Services in Integrated Multi-Trophic Systems. Fishes, 10(2), p.54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalah K, Alfalasi A, Alfalasi M, Alharmoudi M, Alzaabi M, Alzyeodi A, Ahmad RW (2020) January. IoT-enabled shipping container with environmental monitoring and location tracking. In 2020 IEEE 17th annual consumer communications \u0026amp; networking conference (CCNC) (pp. 1\u0026ndash;6). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaravanan K, Anusuya E, Kumar R, Son LH (2018) Real-time water quality monitoring using Internet of Things in SCADA. Environmental monitoring and assessment, 190(9), p.556\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva GME, Campos DF, Brasil JAT, Tremblay M, Mendiondo EM, Ghiglieno F (2022) Advances in technological research for online and in situ water quality monitoring\u0026mdash;A review. Sustainability, 14(9), p.5059\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh Y, Walingo T (2024) Smart water quality monitoring with IOT wireless sensor networks. Sensors, 24(9), p.2871\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh Y, Walingo T (2024) Smart water quality monitoring with IOT wireless sensor networks. Sensors, 24(9), p.2871\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkoufias G (2022) Remote sensing of harmful algal blooms: Data modeling and business aspects (Doctoral dissertation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrigaro D, Capelli C, Cannata M (2024) An open early-warning system prototype for managing and studying algal blooms in Lake Lugano. 48:143\u0026ndash;148The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSudriani Y, Sebesty\u0026eacute;n V, Abonyi J (2023) Surface water monitoring systems\u0026mdash;the importance of integrating information sources for sustainable watershed management. IEEE Access 11:36421\u0026ndash;36451\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugiharto WH, Susanto H, Prasetijo AB (2023) Real-time water quality assessment via IoT: monitoring pH, TDS, temperature, and turbidity. Ing\u0026eacute;nierie des Syst\u0026egrave;mes d\u0026rsquo;Information 28(4):823\u0026ndash;831\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSujatha MG, Reza H, Ranganathan P (2024) January. A Conceptual Software Framework to Monitor Harmful Algal Blooms (HABs) in Lakes. In 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0610\u0026ndash;0619). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSundar LS, Almujibah H, Alshahri AH, Ancha VR (2023) Assessment of carbon neutrality in waste water treatment systems through deep learning algorithm. Water Reuse 13(3):432\u0026ndash;447\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTziortzioti C, Amaxilatis D, Mavrommati I, Chatzigiannakis I (2019) IoT sensors in sea water environment: Ahoy! Experiences from a short summer trial. 343:117\u0026ndash;130Electronic Notes in Theoretical Computer Science\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUbina NA, Lan HY, Cheng SC, Chang CC, Lin SS, Zhang KX, Lu HY, Cheng CY, Hsieh YZ (2023) Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT). Smart agricultural technology, 5, p.100285\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVakalis D, Hellwig RT, Schweiker M, Gauthier S (2023) Challenges and opportunities of Internet-of-Things in occupant-centric building operations: towards a life cycle assessment framework. Current Opinion in Environmental Sustainability, 65, p.101383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVijayakumar N, Ramya AR (2015) March. The real time monitoring of water quality in IoT environment. In 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1\u0026ndash;5). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Feng C, Zhang B, Gao H (2019) Environmental monitoring based on fog computing paradigm and internet of things. IEEE Access 7:127154\u0026ndash;127165\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiryasaputra R, Huang CY, Lin YJ, Yang CT (2024) An IoT real-time potable water quality monitoring and prediction model based on cloud computing architecture. Sensors, 24(4), p.1180\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong BP, Kerkez B (2016) Real-time environmental sensor data: An application to water quality using web services, vol 84. Environmental Modelling \u0026amp; Software, pp 505\u0026ndash;517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWongkiew S, Hu Z, Lee JW, Chandran K, Nhan HT, Marcelino KR, Khanal SK (2021) Nitrogen recovery via aquaponics\u0026ndash;bioponics: Engineering considerations and perspectives. ACS ES\u0026amp;T Eng 1(3):326\u0026ndash;339\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu YB, Lee JH, Choi JH, Choi YJ, Jo AH, Choi CY, Kang JC, Kim JH (2023) The application and future of biofloc technology (BFT) in aquaculture industry: A review. J Environ Manage 342:118237\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang P, Peng C, Zhang J, Zhang J, Chen J, Zhao H (2022) Long-term harmful algal blooms and nutrients patterns affected by climate change and anthropogenic pressures in the zhanjiang bay, China. Front Mar Sci 9:849819\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Yang L, He S, Wang M (2024) July. Design of a Water Quality Monitoring System for Self-Service Water Vending Machines Based on Internet of Things Technology. In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 243\u0026ndash;247). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZulkifli CZ, Garfan S, Talal M, Alamoodi AH, Alamleh A, Ahmaro IYY, Sulaiman S, Ibrahim AB, Zaidan BB, Ismail AR, Albahri OS, Albahri AS, Soon CF, Harun NH, Chiang HH (2022) IoT-Based Water Monitoring Systems: A Systematic Review. \u003cem\u003eWater\u003c/em\u003e, [online] 14(22), p.3621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w14223621\u003c/span\u003e\u003cspan address=\"10.3390/w14223621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 9 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Johannesburg","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet of Things (IoT), Cloud Computing, Water Quality Monitoring, Environmental Sensors, Data Security","lastPublishedDoi":"10.21203/rs.3.rs-6848919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6848919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of Internet of Things (IoT) and cloud computing technologies has revolutionized biological water quality monitoring by enabling high-resolution, real-time sensing and data analytics. Despite the potential, various challenges persist in deployment, data management, scalability, and security. This systematic review investigates the current landscape of IoT and cloud-enabled biological water monitoring, identifying commonly used technologies, architectural models, and recurring limitations while highlighting opportunities for advancement. A total of 17,872 records were screened from Google Scholar, Web of Science, and Scopus, of which 80 studies met inclusion criteria. The review adhered to PRISMA guidelines, and data were extracted and categorized across themes including cloud platforms, microcontrollers, communication protocols, system architectures, and security mechanisms. Most studies were published between 2020 and 2023, with Google Scholar contributing 60% of the included records. Custom/private cloud servers (45%) were the most used backend platforms, while ThingSpeak (13.75%) and AWS IoT (8.75%) were notable open/cloud-based solutions. Hardware trends favored Arduino-based (16.25%) and general microcontroller-based systems (15%), with ESP32-based and Raspberry Pi platforms also widely adopted. Major implementation barriers included connectivity issues (15.05%), GSM/Zigbee congestion (11.83%), and deployment cost (8.6%). HTTP (28.75%) and API (22.5%) were dominant communication methods, with MQTT used in 17.5% of cases. Architecture-wise, over half of the systems followed a cloud-only model, while hybrid and embedded systems remained underutilized. Alarmingly, 55% of studies did not report security mechanisms, and 50% lacked explicit privacy measures; when reported, encryption (17.5%) and data anonymization (30%) were the most common. The integration of IoT and cloud technologies in biological water monitoring is maturing, yet significant challenges remain\u0026mdash;particularly in standardization, energy efficiency, and security. The review underscores the urgent need for context-aware architectures, transparent reporting, and stronger emphasis on privacy-by-design. Future work should leverage edge computing, AI integration, and standardized frameworks to enhance scalability, accuracy, and sustainability in aquatic monitoring systems.\u003c/p\u003e","manuscriptTitle":"IoT and Cloud Computing in Biological Water Monitoring: A Systematic Review of Challenges, Architectures, and Emerging Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 18:49:01","doi":"10.21203/rs.3.rs-6848919/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64b2bc7b-79dc-4c67-8d0e-ae4054e286a0","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49708408,"name":"Marine and Freshwater Biology"},{"id":49708409,"name":"Chemical Engineering"}],"tags":[],"updatedAt":"2025-06-12T18:49:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 18:49:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6848919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6848919","identity":"rs-6848919","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}