Applications and Challenges of Network Technologies in IoT-Based Biological Water Monitoring Systems

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Khatide, Kabelo V.S. Laka, Thabo N. Khauoe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7337247/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 increasing demand for sustainable and real-time biological water monitoring has driven the integration of Internet of Things (IoT) technologies and advanced network communication systems. These solutions enable continuous environmental assessment across diverse ecosystems but face persistent technical and operational challenges. This systematic review synthesizes current evidence on the applications, benefits, and challenges of network technologies in IoT-based biological water monitoring systems, with a focus on communication protocols, deployment environments, and performance considerations. A comprehensive literature search was conducted across Google Scholar, Web of Science, and Scopus for studies published between 2015 and 2025. Following PRISMA guidelines, 19,275 records were screened, and 58 peer-reviewed studies met the inclusion criteria. Data were extracted on network technologies, IoT protocols, application areas, deployment environments, performance priorities, and reported limitations. Short-range wireless technologies (13.79%), sensor networks (10.34%), and cellular networks (6.90%) were predominant network categories, while LPWAN technologies such as LoRa and NB-IoT supported long-range, low-power applications. IoT protocol combinations—particularly MQTT with HTTP or CoAP—were most common (44.83%), followed by web protocols (17.24%). Water quality monitoring (31.03%) and environmental monitoring (29.31%) dominated application areas, with deployments balanced between urban (15.52%) and rural (15.52%) settings, and 14.66% in water-based environments. Real-time capability (32.16%) was the leading performance focus, but major challenges included sensor calibration and accuracy (31.87%), power and energy constraints (18.48%), and environmental/operational conditions (16.81%). Future research priorities emphasized energy efficiency and communication protocols (26.94%), sensor technology and deployment (22.57%), and AI/ML integration (20.94%). IoT-based biological water monitoring research demonstrates strong potential in combining robust sensing with interoperable communication frameworks. However, widespread adoption is hindered by persistent challenges in energy management, sensor accuracy, and environmental resilience. Addressing these gaps requires standardized evaluation frameworks, energy-efficient network designs, and strengthened interoperability across heterogeneous IoT systems. Collaboration between academia, industry, and policy stakeholders will be essential for advancing deployment readiness and achieving global water sustainability goals. Electrochemistry Marine and Freshwater Ecology Marine and Freshwater Biology Electrical Engineering IoT biological water monitoring wireless sensor networks network technologies LPWAN communication protocols water quality systematic review performance metrics energy efficiency Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7337247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":498229089,"identity":"e3a58fc0-956a-4f2e-bf09-392488f0456a","order_by":0,"name":"Ndaya N. 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These solutions enable continuous environmental assessment across diverse ecosystems but face persistent technical and operational challenges. This systematic review synthesizes current evidence on the applications, benefits, and challenges of network technologies in IoT-based biological water monitoring systems, with a focus on communication protocols, deployment environments, and performance considerations. A comprehensive literature search was conducted across Google Scholar, Web of Science, and Scopus for studies published between 2015 and 2025. Following PRISMA guidelines, 19,275 records were screened, and 58 peer-reviewed studies met the inclusion criteria. Data were extracted on network technologies, IoT protocols, application areas, deployment environments, performance priorities, and reported limitations. Short-range wireless technologies (13.79%), sensor networks (10.34%), and cellular networks (6.90%) were predominant network categories, while LPWAN technologies such as LoRa and NB-IoT supported long-range, low-power applications. IoT protocol combinations\u0026mdash;particularly MQTT with HTTP or CoAP\u0026mdash;were most common (44.83%), followed by web protocols (17.24%). Water quality monitoring (31.03%) and environmental monitoring (29.31%) dominated application areas, with deployments balanced between urban (15.52%) and rural (15.52%) settings, and 14.66% in water-based environments. Real-time capability (32.16%) was the leading performance focus, but major challenges included sensor calibration and accuracy (31.87%), power and energy constraints (18.48%), and environmental/operational conditions (16.81%). Future research priorities emphasized energy efficiency and communication protocols (26.94%), sensor technology and deployment (22.57%), and AI/ML integration (20.94%). 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