A Systematic Review of Recent Advances in IoT-Based Sensor Networks for Warehouse Management

A Systematic Review of Recent Advances in IoT-Based Sensor Networks for Warehouse Management | 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 A Systematic Review of Recent Advances in IoT-Based Sensor Networks for Warehouse Management Md. Ruqnuzzaman, M. Muklasur Rahman Opu, Md. Rajib Ahmed, Md Mahamudur Rahaman Shamim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8151337/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 Industrial processes have undergone significant changes as a result of the Internet of Things' (IoT) rapid growth, particularly in warehouse management systems (WMS). These days, IoT-powered sensor networks serve as the foundation for intelligent, networked ecosystems that enable data-driven decision-making, adaptive control, and real-time monitoring throughout international supply chains. With a focus on architectural design, communication protocols, data analytics frameworks, and intelligent automation tactics, this systematic analysis compiles recent advancements in IoT-based sensor networks for warehouse management. A total of 1,050 academic publications (2015–2025) were obtained from significant scientific databases using the PRISMA methodology. After eliminating 250 duplicates, 800 studies were reviewed, and 107 excellent articles were included in the final synthesis. Through energy-optimized connectivity, context-aware routing, edge intelligence, and AI-driven predictive analytics, the reviewed evidence demonstrates that IoT-enabled sensor systems significantly enhance warehouse efficiency. Nonetheless, enduring issues persist with cybersecurity, standardization, data interoperability, and sustainable scaling. This study closes by highlighting important research gaps and proposing an integrated conceptual framework. The goal is to develop next-generation, autonomous, safe, and sustainable IoT-based warehouse ecosystems. The study emphasizes resilience, adaptation, and sustainability in global logistics. It further aligns these advances with the human-centric vision of Industry 5.0 and the guiding principles of Industry 4.0. Environmental Engineering Systems and Networking Edge Computing Industrial IoT Internet of Things Smart Logistics Supply Chain Optimization Warehouse Management Systems Wireless Sensor Networks Figures Figure 1 1. Introduction A major driver of the Fourth Industrial Revolution (Industry 4.0) is the adoption of IoT-based sensor networks in warehouse management systems (WMS). This transformation is fundamentally changing supply chains, making inventory and warehouse management more efficient by leveraging IoT architecture to improve decision-making [ 1 ]. The main argument is that emerging technologies—including blockchain, cloud computing, IoT, and artificial intelligence—are transforming traditional, labor-intensive inventory processes into intelligent, automated, and flexible data-driven ecosystems. These advances enable real-time visibility and optimize decision-making throughout logistics operations [ 2 ]. Unprecedented prospects to improve operational efficiency, accuracy, and sustainability in supply chain and warehouse management have been made possible by the convergence of IoT technologies, distributed sensor networks, artificial intelligence (AI), and cloud-edge computing architectures. IoT-based sensor networks make it easier for devices to communicate with one another, allowing for predictive maintenance, dynamic workflow optimization, and ongoing asset monitoring—all of which increase the transparency and agility of contemporary logistics. Physical devices, including sensor devices, that track and collect various kinds of data about machines and human social activity, are among the "things" in the Internet of Things [ 3 ]. The full implementation of IoT-enabled warehouse intelligence is still hampered by a number of significant issues, despite impressive technological improvements. A popular technological idea, the Internet of Things (IoT) refers to ubiquitous Internet access that turns commonplace items into networked gadgets [ 4 ]. Managing the exponential expansion of data produced by distributed sensing infrastructures, guaranteeing end-to-end cybersecurity and data integrity within sensor networks, and establishing smooth interoperability among heterogeneous IoT devices are important challenges. To overcome these obstacles and create safe, scalable, and intelligent warehouse ecosystems, a methodical, interdisciplinary strategy combining expertise from data analytics, industrial engineering, and computer science is required. It is crucial to thoroughly analyze how IoT-based sensor networks are changing warehouse management in light of these technical advancements and to pinpoint any obstacles that still stand in the way of their widespread adoption. The Internet of Things (IoT) is a network-controlled process or system of interconnected objects [ 5 ]. Therefore, the goal of this systematic review is to present a thorough assessment of the latest developments in IoT-based sensor networks for warehouse management. IoT has opened up a world of opportunities for creativity, productivity, and operational efficiency by tying physical items together with sensors, actuators, and sophisticated networking systems [ 6 ]. It combines research trends, design paradigms, and technology developments to shed light on the present and potential future of IoT-enabled warehouse systems. The specific objectives of this review are to: Determine and classify new developments in IoT-based sensor networks that are pertinent to warehouse management. Examine and evaluate current architectural paradigms, communication protocols, and frameworks used in smart warehouse settings. Examine and summarize the main issues, new developments in technology, and research trends influencing the development of IoT-driven WMS. In order to create safe, scalable, and intelligent warehouse systems that are in line with Industry 4.0 and upcoming smart logistics ecosystems, suggest strategic research directions. 2. Background and Literature Review The development of wireless sensor networks (WSNs) and the larger Internet of Things (IoT) ecosystem has closely followed the evolution of warehouse management systems (WMS). The characteristics of IoT using wireless technologies differ slightly from traditional wired or wireless networking systems since there are a lot more communication devices [ 7 ]. Radio frequency identification (RFID) tags and barcode scanning were the mainstays of early WMS generations for inventory tracking and asset identification. Businesses and consumers may interact, identify, find, transact, and verify products thanks to radio frequency identification (RFID), a low-cost wireless technology that connects billions of items [ 8 ]. The real-time intelligence, context awareness, and adaptive responsiveness needed for contemporary, dynamic logistics settings were absent from these systems, despite the fact that they enhanced basic operating visibility. Since then, the development of IoT-enabled sensor networks has completely changed warehouse operations by making it possible to continuously monitor environmental factors like temperature, humidity, and spatial location. These factors are crucial for preserving product quality, making the best use of available resources, and guaranteeing logistics integrity throughout intricate supply chain ecosystems. Scholarly contributions have made great progress between 2015 and 2025 in both conceptual frameworks and real-world applications of IoT-based sensor systems in warehouse settings. Several key thematic areas that characterize contemporary research and industrial activity are shown by the studied literature: IoT designs for warehouse management: Foundational research initially demonstrated multi-layered IoT designs that included sensing, network, and application layers. The term "Internet of Things" (IoT) describes how closely the digital and physical worlds are intertwined [ 9 ]. These frameworks leverage lightweight communication protocols like Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) to provide efficient, low-latency data interchange in distributed warehouse systems. Many protocols with a variety of features and criteria have surfaced in the IoT space. Constrained Application Protocol (CoAP) and message queueing telemetry transport (MQTT) are two of the most widely used IoT application layer protocols [ 10 ]. Energy-Efficient Sensor Communication: Adaptive clustering algorithms, energy-aware routing, and duty-cycling methods have all been employed in thoughtful research to reduce energy consumption in dense IoT sensor deployments. A new paradigm known as the Internet of Things (IoT) enables connections between electrical gadgets and sensors via the internet to make our lives easier[ 11 ]. These methods are essential for increasing the lifespan of sensor nodes and guaranteeing the scalability and dependability of extensive WSN installations in industrial warehouse settings. The basic components of a WSN are sensor nodes[ 12 ]. Edge and Cloud Integration: A key component of real-time warehouse automation is hybrid architectures that integrate edge intelligence with cloud computing. The development of Internet applications has been significantly aided by both traditional cloud computing and the new edge computing[ 13 ].In order to sustain high throughput in hectic warehouse operations, edge analytics enables localized data processing, lowers latency, optimizes bandwidth utilization, and permits instantaneous, context-aware decision-making. AI and Predictive Maintenance: Improvements in intelligent automation and predictive maintenance have been fueled by the incorporation of artificial intelligence (AI) methods into Internet of Things sensor networks. Robotic workflow optimization, operational anomaly detection, and inventory shortage forecasting are all made possible by machine learning algorithms that evaluate sensor data. An innovative method for manufacturing is AI-driven predictive maintenance, which makes use of artificial intelligence (AI) to anticipate equipment breakdowns and schedule maintenance appropriately [ 14 ]. These AI-powered strategies improve dependability and efficiency while establishing the groundwork for self-governing and flexible warehouse ecosystems. Security and Interoperability: Data security and interoperability continue to be persistent issues in spite of tremendous advancements. IoT devices, communication protocols, and data standards are diverse, which makes it more difficult to integrate systems seamlessly and makes them more susceptible to cyberattacks. By seamlessly connecting systems, sensors, and gadgets to the internet, the Internet of Things is transforming how we engage with technology and allowing real-time data sharing and communication [ 15 ].In order to improve trust, traceability, and system resilience in IoT-enabled warehouse infrastructures, recent studies have suggested lightweight cryptographic algorithms and blockchain-based WSN management frameworks. Many aspects of daily life have also been enhanced by IoT, including the introduction of online smart services and data production and consumption [ 16 ]. All things considered, these studies show how cloud-edge, IoT, and AI paradigms intersect to form the basis of smart warehouse systems. Nonetheless, there is still a lack of a common understanding of scalable and secure designs. IoT-based sensor networks significantly improve real-time operational visibility, resource optimization, and decision-making accuracy in warehouse management, according to a comprehensive study of the literature. By implementing continuous monitoring and efficient management of industrial assets, the "Industrial Internet of Things" refers to the interconnection of intelligent and networked industrial modules or clusters that are strategically deployed to maximize production and reduce operating expenses[ 17 ]. Nonetheless, a thorough comprehension of safe multi-agent communication frameworks, uniform data governance, and cross-platform interoperability is still lacking. In order to create unified, safe, and scalable IoT architectures that will support the upcoming generation of intelligent warehouse systems, these knowledge gaps underscore the necessity for interdisciplinary research. 3. Methodology The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria are followed in this study to guarantee scientific rigor, reproducibility, and methodological transparency. Relevant literature on IoT-based sensor networks in warehouse management systems (WMS) was carefully found, screened, and synthesized using the PRISMA framework. New systematic reviews that solely used database and register searches[ 18 ]. In order to find a large number of peer-reviewed publications discussing IoT-driven warehouse management, sensor network design, edge computing integration, and related technologies, a thorough literature search was carried out across four major scientific databases: IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. As shown in Table 1 , Boolean search operators and keyword combinations were used to guarantee retrieval process precision and scope. Only papers published between 2015 and 2025 were included in the search, representing ten years of substantial progress in line with the widespread adoption of Industry 4.0 methods. Studies specifically focusing on IoT-based sensor networks applied to warehouse or inventory management, published in English, and appearing in respectable, peer-reviewed journals or excellent international conference proceedings were included in the inclusion criteria to guarantee methodological consistency. On the other hand, non-English articles, non-peer-reviewed materials like theses or editorials, and research that dealt with generic IoT frameworks without warehouse relevance were not included. Inaccessible complete texts and duplicate entries were also methodically eliminated. 1,050 records were retrieved at the start of the identification process, and 250 duplicates were eliminated, leaving 800 unique items for preliminary screening. 150 full-text publications were screened for eligibility after their titles and abstracts were reviewed; 107 of these studies satisfied all quality and relevance requirements and were added to the final synthesis. Throughout the investigation, transparency and reproducibility were guaranteed by a methodical, PRISMA-guided methodology. To guarantee thorough and objective selection, the review adhered to the PRISMA 2020 framework, which consists of four main phases: identification, screening, eligibility, and inclusion. Figure 1 (PRISMA Flow Diagram), which graphically depicts the methodical selection and refinement procedure used in this study, shows the entire workflow, including the quantity of records retrieved, screened, excluded, and finished. 3.1 Search Strategy A structured Boolean search approach was used across the chosen databases to guarantee thorough and repeatable literature retrieval. The search terms used logical operators to narrow and broaden the query scope by combining several IoT, sensor network, and warehouse management ideas. While preserving conceptual coherence, the final query strings were modified to fit the syntax of each database (IEEE Xplore, ScienceDirect, SpringerLink, and Scopus). Table 1 Database querying makes use of boolean search strategies and keyword combinations. Concept Keywords Boolean Operators / Search String Example IoT Concept “Internet of Things” OR IoT OR “Smart Devices” (IoT OR “Internet of Things” OR “Smart Devices”) Sensor Network “Wireless Sensor Network” OR “WSN” OR “Sensor Nodes” (“Wireless Sensor Network” OR WSN OR “Sensor Nodes”) Warehouse “Warehouse Management” OR “Smart Warehouse” OR “Inventory Control” (“Warehouse Management” OR “Smart Warehouse” OR “Inventory Control”) Integration “Edge Computing” OR “Cloud Computing” OR “Big Data” OR “AI” (“Edge Computing” OR “Cloud Computing” OR “Big Data” OR “Artificial Intelligence”) Combined Query – (IoT OR “Internet of Things”) AND (“Wireless Sensor Network” OR WSN) AND (“Warehouse Management” OR “Smart Warehouse”) AND (“Edge Computing” OR AI) — Applied in Scopus, IEEE Xplore, ScienceDirect, and SpringerLink 4. Results and Discussion The 107 chosen studies were systematically analyzed, and the results showed a number of interconnected patterns that together show how quickly IoT-based sensor networks have developed within warehouse management systems (WMS). Both the technological advancements influencing contemporary warehouses and the persistent difficulties defining current research directions in this area are highlighted by these them. The improvement of operational visibility and real-time monitoring is a key discovery. Continuous, high-resolution tracking of warehouse assets is made possible by the integration of IoT sensors with edge gateways. One innovative technology that is revolutionizing our daily lives is the Internet of Things (IoT) [ 19 ]. This provides precise spatiotemporal data that improves decision-making and reduces the need for human involvement. These sensor-driven monitoring systems ensure better inventory accuracy, product traceability, and a decrease in product loss or misplacement by promoting transparency across supply chain nodes. The focus on scalability and energy efficiency, two issues that are most commonly discussed in the reviewed literature, is equally important. Achieving maximum power utilization without sacrificing data transmission integrity becomes increasingly important as the density and scale of IoT sensor deployments rise. Low-power wide-area networks (LPWANs), adaptive communication protocols, and duty-cycling are some of the methods that have been extensively studied to strike a compromise between performance efficiency and energy usage. The energy efficiency and communication coverage of the various LPWAN communication systems were studied [ 20 ]. Notwithstanding these developments, scalability in diverse and expansive industrial settings still necessitates creative energy-conscious structures and astute routing systems. The increasing dependence of warehouse ecosystems on data analytics and predictive intelligence is another recurring issue. Data containing a large number of samples have stronger statistical power, as over-complicated data with great high-dimensional feature space could lead to a greater rate of false discovery[ 21 ]. Predictive stock replenishment, autonomous mobile robot (AMR) routing path optimization, and early problem detection in operational equipment are all made possible by the growing use of AI-driven analytical models. The technology for handling materials has dramatically improved in the last few decades. The transformation of automated guided vehicles (AGV) into autonomous mobile robots (AMR) is one significant advancement [ 22 ].IoT-generated sensor data and machine learning algorithms come together to assist the shift from reactive to predictive operational paradigms by enabling more proactive and informed warehouse management methods. One major obstacle to the realization of fully integrated smart warehouse settings is the problem of interoperability and standards. Cross-system integration is hampered by the isolated data silos created by many warehouses' continued reliance on proprietary or non-standard communication protocols. Although potential foundations for interoperability are provided by international standards like OPC Unified Architecture (OPC-UA) and oneM2M, their implementation is still uneven and fragmented. An intelligent information infrastructure that manages data gathered between objects or between objects and humans is known as oneM2M [ 23 ]. For smooth data interchange and multi-vendor interoperability in international warehouse operations, a single and standardized communication infrastructure must be established. Another urgent issue that was found in all of the studies that were surveyed is security and privacy. Warehouse infrastructures are more susceptible to spoofing attacks, illegal access, and data breaches as they get more interconnected. Applying cutting-edge technologies like blockchain, artificial intelligence (AI), and quantum computing to enhance malware detection in Internet of Things (IoT) digital devices and guarantee quicker reaction to any attacks is an essential cybersecurity threat mitigation technique [ 24 ]. Therefore, the majority of nations have already drafted data protection legislation to improve cybersecurity. When keeping and processing data, individuals and corporate organizations must adhere to legal requirements and rules [ 25 ].In order to preserve sensitive operational data and enable dispersed intelligence, recent research endeavors have suggested integrating blockchain technologies to create decentralized trust frameworks and federated learning models. To evaluate scalability and resilience, these methods must be further validated in actual industrial settings, as they are still primarily experimental. Lastly, as enterprises place a higher priority on environmental responsibility and energy saving, the idea of sustainability and Green IoT is receiving a lot of attention. To reduce the environmental impact of warehouse operations, innovations including self-powered communication modules and energy-harvesting sensor nodes are being developed. In addition to extending the lifespan of devices, these technologies support the larger goal of carbon-neutral, sustainable logistics networks that are in line with international sustainability objectives. All of these results highlight how IoT-based sensor networks have the ability to revolutionize warehouse management procedures. The industrial revolution's widespread production of goods has increased the need for contemporary warehouses[ 26 ]. The next generation of IoT-enabled warehouses, however, must be designed with an integrated approach—combining edge intelligence, adaptive communication frameworks, and secure distributed architectures—to achieve truly autonomous and sustainable operational ecosystems, as evidenced by persistent gaps in energy scalability, cybersecurity, and system interoperability. These realizations serve as the basis for determining current research gaps and potential avenues for future investigation, which are covered in Section 5 . 5. Research Gaps and Future Directions The integration of IoT-based sensor networks into warehouse management systems has advanced remarkably, but there are still a number of important research gaps that prevent the development of completely autonomous, safe, and sustainable smart warehouse ecosystems. The incorporation of technologies from the fourth industrial revolution into the supply chain, such as the Internet of Things (IoT) architecture for inventory and warehouse management, is a game-changer for decision-making processes [ 27 ].To facilitate the shift from experimental prototypes to reliable, scalable industrial implementations that can support the upcoming generation of intelligent logistics networks, these gaps must be filled. Interoperability, in particular the lack of standardized middleware frameworks that enable smooth connection and data exchange across diverse IoT platforms, is one of the most enduring problems. Existing warehouse infrastructures frequently depend on proprietary or vendor-specific solutions, which restrict cross-platform integration and produce data silos. For various sensing, networking, and analytics components to function together, open, interoperable frameworks built on established communication protocols and semantic data models are essential. Integration of Edge and AI is another important research frontier that is yet mostly unexplored. The rise and integration of heterogeneous distributed compute paradigms—and the edge—are driving a fundamental shift in computing and artificial intelligence (AI), with the edge serving as the front-row seat for delivering cloud service offerings to end users [ 28 ]. Although data analytics and predictive modeling have benefited greatly from the widespread usage of artificial intelligence (AI), little research has been done on integrating AI capabilities right at the network edge for localized, real-time decision-making. The discipline of artificial intelligence (AI) seeks to create intelligent entities that automate the process of dynamically generating different design decisions for industrial computational deployment in the context of resource management [ 29 ]. Context-aware responsiveness, lower communication latency, and improved scalability would all be made possible by such integration. Novel architectures that integrate distributed computing with lightweight AI models tailored for energy-constrained sensor contexts are needed to advance this field. Another significant unresolved issue with IoT-driven warehouse systems is security and privacy. One of the most well-liked new concepts in recent years is the Internet of Things. It uses a network of interconnected parts to find, send, and analyze data [ 30 ]. Traditional cryptographic mechanisms are unfeasible due to the limited computing and energy resources of Internet of Things devices. In order to lessen the computational effect of security algorithms, lightweight cryptographic algorithms are being examined in the scientific literature [ 31 ]. As a result, the development of intrusion detection and encryption systems that are both lightweight and adaptive is still an unresolved challenge. Furthermore, sophisticated trust management frameworks and decentralized authentication methods are required to provide end-to-end security across hybrid cloud–edge infrastructures. For dependable services, precise data gathering, device authentication, and safe decision-making in IoT trust management are essential [ 32 ]. Simultaneously, the current body of research has paid little consideration to data governance and ethical compliance. It is more crucial than ever to comprehend data governance, privacy, and ethics related to data availability and use [ 33 ]. Clearer ethical standards and governance frameworks are necessary for the proper gathering, processing, and sharing of sensitive operational data as warehouse operations grow more data-driven. Potential privacy violations and noncompliance with international data protection laws like the General Data Protection Regulation (GDPR) are hazards associated with the absence of standardized data protection frameworks. According to the GDPR, organizations that handle personal data must specify the types of personal data they want to handle and why they are doing so [ 34 ]. Lastly, warehouse management is still in its early stages of pursuing sustainability and Green IoT. Few studies specifically address carbon-neutral IoT infrastructures, recyclable sensor materials, or energy-harvesting network topologies, despite the fact that energy-efficient communication and sensor optimization have been investigated. The vast advancements in communication and sensing technology have led to the connecting of "things" around us for a variety of purposes that improve the quality of our lives[ 35 ]. The creation of environmentally friendly, sustainable IoT solutions that meet business sustainability standards and global climate goals should be the top priority of future research. When taken as a whole, these gaps outline distinct paths for further study. To fully utilize IoT-based sensor networks in warehouse management, it is essential to advance interoperability standards, enable edge-intelligent architectures, provide safe and moral data frameworks, and give sustainable design principles first priority. In addition to improving operational intelligence and resilience, closing these gaps will set the stage for the development of a new generation of independent, moral, and sustainable industrial ecosystems. It will take interdisciplinary cooperation between engineers, data scientists, and logistics specialists to close these gaps. The basis for a new generation of intelligent and secure warehouse ecosystems will be laid by this collaboration, which will improve sustainability, ethical data governance, and operational resilience. 6. Proposed Future Directions Future developments in IoT-based sensor networks for warehouse management can be guided by a number of strategic directions, which build upon the highlighted research gaps. The creation of unifying semantic interoperability frameworks that can smoothly combine diverse IoT platforms, devices, and communication protocols inside warehouse ecosystems is a top goal. Scale, near real-time automation, flawless interoperability, and the constantly expanding and diversifying IoT ecosystems have all remained major obstacles [ 36 ].To overcome the present fragmentation of IoT infrastructures, such frameworks should make use of standardized middleware architectures and ontology-based data models to facilitate consistent data exchange, contextual comprehension, and cross-vendor interoperability. The deployment of AI-powered self-healing wireless sensor networks (WSNs) with predictive failure detection and autonomous recovery capabilities is equally crucial. WSNs have become a game-changing technology for distributed sensing, data gathering, and communication in a variety of applications, from industrial automation to environmental monitoring [ 37 ]. Machine learning methods can be used by these intelligent networks to dynamically improve network architecture, predict node failures, and guarantee resilient performance even in unpredictable operating environments. Predictive analytics integration will greatly improve network reliability and lower maintenance costs at both the edge and cloud levels. Through the deployment of network, storage, and computation resources to the edge locations of wireless access networks, edge computing enhances the cloud computing concept by enabling end devices to run resource-intensive applications [ 38 ]. Additionally, combining blockchain technology with distributed edge networks is a viable option to create logistics frameworks that are safe, open, and auditable. Blockchain technology guarantees data integrity, decentralized authentication, and robust threat prevention in the designed architecture, hence enforcing proactive security[ 39 ].In multi-stakeholder supply chain systems, blockchain's decentralized architecture can guarantee immutable data provenance, traceability, and real-time warehouse transaction verification, enhancing cybersecurity and trust. Last but not least, investigating bio-inspired routing and optimization algorithms offers a novel strategy for attaining long-term energy efficiency in dense IoT sensor deployments. However, efforts to improve security, accuracy, and usefulness through complicated algorithms have made these systems more complex, which has resulted in issues like increasing power consumption and energy inefficiency [ 40 ]. These algorithms can improve routing reliability, reduce energy consumption, and facilitate large-scale scalability by taking inspiration from biological processes, including ant colony optimization, brain adaptation, and swarm intelligence. When taken as a whole, these future possibilities offer a thorough road map for developing IoT-based warehouse systems in the direction of increased sustainability, security, and intelligence. The next generation of smart warehouses can attain previously unheard-of levels of autonomy, resilience, and operational excellence by combining biologically inspired optimization, decentralized trust frameworks, self-healing intelligence, and semantic interoperability. These recommended strategies collectively establish IoT as the enabling backbone for ensuring adaptable, secure, and sustainable warehouse operations under Industry 5.0. Together, these paths support the new Industry 5.0 vision, which emphasizes technological ecosystems that are human-centric, moral, and flexible. Industry 5.0's fundamental elements, underlying technology, and guiding principles have been established [ 41 ]. 7. Conceptual Framework This paper suggests a conceptual framework that combines distributed computation, intelligent sensing, and secure communication into a single architectural model to direct the creation of next-generation IoT-enabled warehouse management systems. Five interconnected layers make up the structure (shown in Fig. 2), which, when combined, allow for independent, flexible, and sustainable warehouse operations. Real-time information on asset location, environmental conditions, and equipment status is gathered by heterogeneous IoT devices at the Sensing Layer, such as RFID tags, environmental sensors, and mobile robots. Through the use of communication and control protocols, the network and control layer's numerous routers, controllers, and servers effectively integrate every robot in the physical layer's MRS [ 42 ]. Duty-cycling and, when practical, energy-harvesting techniques are included in the design of these nodes to ensure energy-efficient operation. For wireless Internet of Things (IoT) networks with battery-powered nodes, energy consumption has emerged as the primary concern[ 43 ]. Edge gateways and embedded processors carry out initial analytics and inference operations near data sources in the Edge Layer, which serves as the intermediate processing tier. "Edge computing (EC)" aims to reduce bandwidth consumption and communication delay, facilitate real-time data processing, lower operating costs, increase scalability, and improve service quality[ 44 ]. By making decisions locally, network latency is reduced and the computational strain on cloud resources is relieved. The Network and Middleware Layer uses standardized communication protocols like MQTT, CoAP, and OPC-UA to create smooth data flow. The work has been assessed using digital twins as an application in the context of vehicle-to-cloud communication[ 45 ]. It incorporates semantic middleware, which promotes interoperability between multi-vendor systems and harmonizes data formats. At this layer, blockchain and lightweight encryption are integrated to guarantee decentralized trust management and safe, verifiable data exchange. The characteristics of blockchain technology, including its flexibility, support for integration, anonymity, decentralization, and autonomous control, have led to its tremendous growth [ 46 ]. Large-scale data aggregation, machine learning, and predictive analytics are carried out at the Cloud Intelligence Layer to facilitate long-term decision support, predictive maintenance, and worldwide optimization. In order to improve informed decision-making, predictive analytics is crucial in offering profound insights to reduce uncertainty and increase overall efficiency in terms of resource allocation, inventory management, and demand fulfillment[ 47 ].In order to match operational insights with strategic planning, this layer also interfaces with supply chain management (SCM) and enterprise resource planning (ERP) systems. One well-known and widely used solution in corporate organizations that has been shown to integrate and automate operations, increase performance, and lower costs is the Enterprise Resource Planning (ERP) system[ 48 ]. Lastly, the Application Layer offers managers and autonomous systems decision-support tools, automated control interfaces, and visualization dashboards. Continuous performance monitoring, autonomous mobile robot (AMR) route optimization, and real-time inventory tracking are all made possible by this layer. Autonomous Mobile Robots (AMRs) are now essential automation tools in smart homes, modern industries, and service sectors[ 49 ]. When taken as a whole, the suggested framework lays the groundwork for intelligent, safe, and sustainable warehouse ecosystems that combine technological independence with human-centered, ecologically conscious design, embodying the ideals of Industry 4.0 and the new Industry 5.0 paradigm. As seen in Fig. 2, the conceptual architecture of IoT-enabled smart warehouses is shown below. Table 2 An overview of the suggested multi-layer Internet of Things framework for intelligent warehouse management Layer Primary Functions Core Technologies / Techniques Objectives Sensing Layer Data acquisition from IoT nodes, environment, and assets RFID, WSN, energy-harvesting sensors Real-time monitoring and asset visibility Edge Layer Local processing, inference, and control Edge gateways, embedded AI, microcontrollers Reduced latency and bandwidth usage Network & Middleware Layer Data exchange, interoperability, and security MQTT, CoAP, OPC-UA, Blockchain, lightweight encryption Reliable, secure communication across heterogeneous systems Cloud Intelligence Layer Aggregation, analytics, and prediction Machine learning, big data, ERP/SCM integration Global optimization and predictive maintenance Application Layer Visualization, control, and automation Dashboards, AMR routing, decision-support systems Intelligent automation and management insights 8. Novelty and Contribution By offering a thorough and integrative examination of the development of IoT-based sensor networks within warehouse management systems (WMS), this review significantly advances both academic research and industrial practice. It provides a synthesis of technology breakthroughs over a ten-year period (2015–2025), methodically outlining the major developments, approaches, and frameworks that have influenced the evolution of smart warehouse settings. By combining disparate research streams' fragmented insights, the study demonstrates how IoT-driven connections might revolutionize operational efficiency, accuracy, and sustainability in contemporary logistics ecosystems. The creation of an organized taxonomy of IoT applications in the domains of intelligent automation, real-time monitoring, and logistics constitutes a major contribution of this review. This taxonomy serves as a fundamental resource for researchers and practitioners alike by clearly classifying the ways in which IoT-based sensor networks are applied in different facets of warehouse management, such as inventory tracking, predictive analytics, robotic coordination, and environmental control. A conceptual framework for next-generation smart warehouses is also presented in the paper, with a focus on safe, autonomous, and energy-efficient IoT systems. The suggested paradigm emphasizes how edge computing, AI, and sensor-driven analytics can work together to support self-optimization and adaptive decision-making in industrial processes. With improved performance, safety, and efficiency, these developments have the potential to completely transform the Internet of Things (IoT), smart cities, and many other industries[ 50 ]. This assessment concludes by outlining strategic research avenues for promoting interoperability standards, AI–edge synergy, and sustainable warehouse operations. The report offers a roadmap for creating intelligent, robust, and ecologically conscious IoT ecosystems that are in line with the tenets of Industry 4.0 and the human-centric values of Industry 5.0 by defining forward-looking paths for innovation and system design. Together, these contributions establish this review as a fundamental resource for academics, technology developers, and business professionals seeking to develop, deploy, and enhance intelligent, safe, and sustainable next-generation IoT-based warehouse management systems. 9. Conclusion Warehouse management has seen a dramatic change with the introduction of IoT-based sensor networks, signaling a move toward intelligent automation, data-driven operational decision-making, and real-time visibility. Ten years of study (2015–2025) have been compiled in this systematic review to show how IoT technologies have developed from basic tracking systems to complex, adaptable infrastructures that can sustain large-scale, autonomous, and intelligent warehouse ecosystems. The results highlight significant developments in AI-driven automation, predictive analytics, and edge-enabled sensing, all of which improve the effectiveness, responsiveness, and intelligence of contemporary warehouse systems. Nonetheless, there are still enduring issues with interoperability, cybersecurity, scalability, and sustainability, all of which pose significant obstacles to the development of completely autonomous smart warehouses. The creation of unified communication frameworks, safe and compatible data pipelines, and energy-efficient architectures intended to preserve performance and dependability in dynamic industrial environments is necessary to overcome these constraints. The next generation of warehouse management systems needs to be self-optimizing, intelligent, and secure IoT ecosystems that use ethical data governance, distributed intelligence, and robust network topologies. IoT-driven sensor networks will continue to be the technological foundation of robust, transparent, and sustainable supply chain ecosystems as global logistics and supply chains undergo further rapid digital transformation, promoting efficiency, adaptability, and innovation across industrial operations globally. In order to evaluate the suggested conceptual framework's scalability, interoperability, and durability in practical settings, future studies should empirically validate it across a range of industrial situations. In the upcoming ten years, intelligent, self-sufficient, and sustainable warehouse ecosystems will become a reality if these obstacles are overcome. Declarations Conflict of Interest The authors affirm that the work described in this publication was not influenced by any known competing financial interests or personal relationships. Funding Declaration No specific grant from a public, private, or nonprofit funding organization was obtained for this study. Acknowledgment The authors would like to express their sincere gratitude to the Department of Computer Science and Engineering at Islamic University in Kushtia, Bangladesh, for its ongoing academic and institutional support during the development of this study, "A Systematic Review of Recent Advances in IoT-Based Sensor Networks for Warehouse Management." The University of Alabama at Birmingham, Trine University, and Western Illinois University are also acknowledged by the writers for their scholarly materials, technological assistance, and collaborative research, all of which were crucial to the accomplishment of this work. 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ResearchGate Oct. 10.3390/app11114879 Blockchain Integration in IoT: Applications, Opportunities, and Challenges, Computers, Materials and Continua, vol. 83, no. 2, pp. 1561–1605 (2025) Apr. 10.32604/cmc.2025.063304 Iseri F, Iseri H, Chrisandina NJ, Iakovou E, Pistikopoulos EN (July 2025) AI-based predictive analytics for enhancing data-driven supply chain optimization. J Glob Optim. 10.1007/s10898-025-01509-1 Bahssas DM, AlBar AM, Hoque R (June 2015) Enterprise Resource Planning (ERP) Systems: Design, Trends and Deployment. Int Technol Manag Rev 5(2):72–81. 10.2991/itmr.2015.5.2.2 Wang S, Ahmad NS (Mar. 2025) AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review. Eng Sci Technol Int J 63:101977. 10.1016/j.jestch.2025.101977 Mwashita W (2025) In: Naidoo P, Sibanda M (eds) Recent Developments in Artificial Intelligence (AI) Sensors, in Measurement, Automation, and Control in Artificial Intelligence and Machine Learning . Springer Nature Switzerland, Cham, pp 229–245. doi: 10.1007/978-3-031-91802-5_10 . 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. 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Introduction","content":"\u003cp\u003eA major driver of the Fourth Industrial Revolution (Industry 4.0) is the adoption of IoT-based sensor networks in warehouse management systems (WMS). This transformation is fundamentally changing supply chains, making inventory and warehouse management more efficient by leveraging IoT architecture to improve decision-making [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The main argument is that emerging technologies\u0026mdash;including blockchain, cloud computing, IoT, and artificial intelligence\u0026mdash;are transforming traditional, labor-intensive inventory processes into intelligent, automated, and flexible data-driven ecosystems. These advances enable real-time visibility and optimize decision-making throughout logistics operations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnprecedented prospects to improve operational efficiency, accuracy, and sustainability in supply chain and warehouse management have been made possible by the convergence of IoT technologies, distributed sensor networks, artificial intelligence (AI), and cloud-edge computing architectures. IoT-based sensor networks make it easier for devices to communicate with one another, allowing for predictive maintenance, dynamic workflow optimization, and ongoing asset monitoring\u0026mdash;all of which increase the transparency and agility of contemporary logistics. Physical devices, including sensor devices, that track and collect various kinds of data about machines and human social activity, are among the \"things\" in the Internet of Things [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe full implementation of IoT-enabled warehouse intelligence is still hampered by a number of significant issues, despite impressive technological improvements.\u003c/p\u003e\u003cp\u003eA popular technological idea, the Internet of Things (IoT) refers to ubiquitous Internet access that turns commonplace items into networked gadgets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Managing the exponential expansion of data produced by distributed sensing infrastructures, guaranteeing end-to-end cybersecurity and data integrity within sensor networks, and establishing smooth interoperability among heterogeneous IoT devices are important challenges. To overcome these obstacles and create safe, scalable, and intelligent warehouse ecosystems, a methodical, interdisciplinary strategy combining expertise from data analytics, industrial engineering, and computer science is required.\u003c/p\u003e\u003cp\u003eIt is crucial to thoroughly analyze how IoT-based sensor networks are changing warehouse management in light of these technical advancements and to pinpoint any obstacles that still stand in the way of their widespread adoption. The Internet of Things (IoT) is a network-controlled process or system of interconnected objects [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, the goal of this systematic review is to present a thorough assessment of the latest developments in IoT-based sensor networks for warehouse management. IoT has opened up a world of opportunities for creativity, productivity, and operational efficiency by tying physical items together with sensors, actuators, and sophisticated networking systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It combines research trends, design paradigms, and technology developments to shed light on the present and potential future of IoT-enabled warehouse systems.\u003c/p\u003e\u003cp\u003eThe specific objectives of this review are to:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDetermine and classify new developments in IoT-based sensor networks that are pertinent to warehouse management.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExamine and evaluate current architectural paradigms, communication protocols, and frameworks used in smart warehouse settings.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eExamine and summarize the main issues, new developments in technology, and research trends influencing the development of IoT-driven WMS.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIn order to create safe, scalable, and intelligent warehouse systems that are in line with Industry 4.0 and upcoming smart logistics ecosystems, suggest strategic research directions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"2. Background and Literature Review","content":"\u003cp\u003eThe development of wireless sensor networks (WSNs) and the larger Internet of Things (IoT) ecosystem has closely followed the evolution of warehouse management systems (WMS). The characteristics of IoT using wireless technologies differ slightly from traditional wired or wireless networking systems since there are a lot more communication devices [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Radio frequency identification (RFID) tags and barcode scanning were the mainstays of early WMS generations for inventory tracking and asset identification. Businesses and consumers may interact, identify, find, transact, and verify products thanks to radio frequency identification (RFID), a low-cost wireless technology that connects billions of items [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The real-time intelligence, context awareness, and adaptive responsiveness needed for contemporary, dynamic logistics settings were absent from these systems, despite the fact that they enhanced basic operating visibility.\u003c/p\u003e\u003cp\u003eSince then, the development of IoT-enabled sensor networks has completely changed warehouse operations by making it possible to continuously monitor environmental factors like temperature, humidity, and spatial location. These factors are crucial for preserving product quality, making the best use of available resources, and guaranteeing logistics integrity throughout intricate supply chain ecosystems. Scholarly contributions have made great progress between 2015 and 2025 in both conceptual frameworks and real-world applications of IoT-based sensor systems in warehouse settings.\u003c/p\u003e\u003cp\u003eSeveral key thematic areas that characterize contemporary research and industrial activity are shown by the studied literature:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIoT designs for warehouse management: Foundational research initially demonstrated multi-layered IoT designs that included sensing, network, and application layers. The term \"Internet of Things\" (IoT) describes how closely the digital and physical worlds are intertwined [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These frameworks leverage lightweight communication protocols like Message Queuing Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) to provide efficient, low-latency data interchange in distributed warehouse systems. Many protocols with a variety of features and criteria have surfaced in the IoT space. Constrained Application Protocol (CoAP) and message queueing telemetry transport (MQTT) are two of the most widely used IoT application layer protocols [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnergy-Efficient Sensor Communication: Adaptive clustering algorithms, energy-aware routing, and duty-cycling methods have all been employed in thoughtful research to reduce energy consumption in dense IoT sensor deployments. A new paradigm known as the Internet of Things (IoT) enables connections between electrical gadgets and sensors via the internet to make our lives easier[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These methods are essential for increasing the lifespan of sensor nodes and guaranteeing the scalability and dependability of extensive WSN installations in industrial warehouse settings. The basic components of a WSN are sensor nodes[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEdge and Cloud Integration: A key component of real-time warehouse automation is hybrid architectures that integrate edge intelligence with cloud computing. The development of Internet applications has been significantly aided by both traditional cloud computing and the new edge computing[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].In order to sustain high throughput in hectic warehouse operations, edge analytics enables localized data processing, lowers latency, optimizes bandwidth utilization, and permits instantaneous, context-aware decision-making.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAI and Predictive Maintenance: Improvements in intelligent automation and predictive maintenance have been fueled by the incorporation of artificial intelligence (AI) methods into Internet of Things sensor networks. Robotic workflow optimization, operational anomaly detection, and inventory shortage forecasting are all made possible by machine learning algorithms that evaluate sensor data. An innovative method for manufacturing is AI-driven predictive maintenance, which makes use of artificial intelligence (AI) to anticipate equipment breakdowns and schedule maintenance appropriately [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These AI-powered strategies improve dependability and efficiency while establishing the groundwork for self-governing and flexible warehouse ecosystems.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSecurity and Interoperability: Data security and interoperability continue to be persistent issues in spite of tremendous advancements. IoT devices, communication protocols, and data standards are diverse, which makes it more difficult to integrate systems seamlessly and makes them more susceptible to cyberattacks. By seamlessly connecting systems, sensors, and gadgets to the internet, the Internet of Things is transforming how we engage with technology and allowing real-time data sharing and communication [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].In order to improve trust, traceability, and system resilience in IoT-enabled warehouse infrastructures, recent studies have suggested lightweight cryptographic algorithms and blockchain-based WSN management frameworks. Many aspects of daily life have also been enhanced by IoT, including the introduction of online smart services and data production and consumption [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. All things considered, these studies show how cloud-edge, IoT, and AI paradigms intersect to form the basis of smart warehouse systems. Nonetheless, there is still a lack of a common understanding of scalable and secure designs.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIoT-based sensor networks significantly improve real-time operational visibility, resource optimization, and decision-making accuracy in warehouse management, according to a comprehensive study of the literature. By implementing continuous monitoring and efficient management of industrial assets, the \"Industrial Internet of Things\" refers to the interconnection of intelligent and networked industrial modules or clusters that are strategically deployed to maximize production and reduce operating expenses[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nonetheless, a thorough comprehension of safe multi-agent communication frameworks, uniform data governance, and cross-platform interoperability is still lacking. In order to create unified, safe, and scalable IoT architectures that will support the upcoming generation of intelligent warehouse systems, these knowledge gaps underscore the necessity for interdisciplinary research.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria are followed in this study to guarantee scientific rigor, reproducibility, and methodological transparency. Relevant literature on IoT-based sensor networks in warehouse management systems (WMS) was carefully found, screened, and synthesized using the PRISMA framework. New systematic reviews that solely used database and register searches[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In order to find a large number of peer-reviewed publications discussing IoT-driven warehouse management, sensor network design, edge computing integration, and related technologies, a thorough literature search was carried out across four major scientific databases: IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Boolean search operators and keyword combinations were used to guarantee retrieval process precision and scope. Only papers published between 2015 and 2025 were included in the search, representing ten years of substantial progress in line with the widespread adoption of Industry 4.0 methods.\u003c/p\u003e\u003cp\u003eStudies specifically focusing on IoT-based sensor networks applied to warehouse or inventory management, published in English, and appearing in respectable, peer-reviewed journals or excellent international conference proceedings were included in the inclusion criteria to guarantee methodological consistency. On the other hand, non-English articles, non-peer-reviewed materials like theses or editorials, and research that dealt with generic IoT frameworks without warehouse relevance were not included. Inaccessible complete texts and duplicate entries were also methodically eliminated.\u003c/p\u003e\u003cp\u003e1,050 records were retrieved at the start of the identification process, and 250 duplicates were eliminated, leaving 800 unique items for preliminary screening. 150 full-text publications were screened for eligibility after their titles and abstracts were reviewed; 107 of these studies satisfied all quality and relevance requirements and were added to the final synthesis. Throughout the investigation, transparency and reproducibility were guaranteed by a methodical, PRISMA-guided methodology.\u003c/p\u003e\u003cp\u003eTo guarantee thorough and objective selection, the review adhered to the PRISMA 2020 framework, which consists of four main phases: identification, screening, eligibility, and inclusion. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (PRISMA Flow Diagram), which graphically depicts the methodical selection and refinement procedure used in this study, shows the entire workflow, including the quantity of records retrieved, screened, excluded, and finished.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Search Strategy\u003c/h2\u003e\u003cp\u003eA structured Boolean search approach was used across the chosen databases to guarantee thorough and repeatable literature retrieval. The search terms used logical operators to narrow and broaden the query scope by combining several IoT, sensor network, and warehouse management ideas. While preserving conceptual coherence, the final query strings were modified to fit the syntax of each database (IEEE Xplore, ScienceDirect, SpringerLink, and Scopus).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDatabase querying makes use of boolean search strategies and keyword combinations.\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\u003eConcept\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKeywords\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBoolean Operators / Search String Example\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIoT Concept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Internet of Things\u0026rdquo; OR IoT OR \u0026ldquo;Smart Devices\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(IoT OR \u0026ldquo;Internet of Things\u0026rdquo; OR \u0026ldquo;Smart Devices\u0026rdquo;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSensor Network\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Wireless Sensor Network\u0026rdquo; OR \u0026ldquo;WSN\u0026rdquo; OR \u0026ldquo;Sensor Nodes\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ldquo;Wireless Sensor Network\u0026rdquo; OR WSN OR \u0026ldquo;Sensor Nodes\u0026rdquo;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWarehouse\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Warehouse Management\u0026rdquo; OR \u0026ldquo;Smart Warehouse\u0026rdquo; OR \u0026ldquo;Inventory Control\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ldquo;Warehouse Management\u0026rdquo; OR \u0026ldquo;Smart Warehouse\u0026rdquo; OR \u0026ldquo;Inventory Control\u0026rdquo;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntegration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Edge Computing\u0026rdquo; OR \u0026ldquo;Cloud Computing\u0026rdquo; OR \u0026ldquo;Big Data\u0026rdquo; OR \u0026ldquo;AI\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u0026ldquo;Edge Computing\u0026rdquo; OR \u0026ldquo;Cloud Computing\u0026rdquo; OR \u0026ldquo;Big Data\u0026rdquo; OR \u0026ldquo;Artificial Intelligence\u0026rdquo;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCombined Query\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(IoT OR \u0026ldquo;Internet of Things\u0026rdquo;) AND (\u0026ldquo;Wireless Sensor Network\u0026rdquo; OR WSN) AND (\u0026ldquo;Warehouse Management\u0026rdquo; OR \u0026ldquo;Smart Warehouse\u0026rdquo;) AND (\u0026ldquo;Edge Computing\u0026rdquo; OR AI) \u0026mdash; \u003cem\u003eApplied in Scopus, IEEE Xplore, ScienceDirect, and SpringerLink\u003c/em\u003e\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"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThe 107 chosen studies were systematically analyzed, and the results showed a number of interconnected patterns that together show how quickly IoT-based sensor networks have developed within warehouse management systems (WMS). Both the technological advancements influencing contemporary warehouses and the persistent difficulties defining current research directions in this area are highlighted by these them.\u003c/p\u003e\u003cp\u003eThe improvement of operational visibility and real-time monitoring is a key discovery. Continuous, high-resolution tracking of warehouse assets is made possible by the integration of IoT sensors with edge gateways. One innovative technology that is revolutionizing our daily lives is the Internet of Things (IoT) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This provides precise spatiotemporal data that improves decision-making and reduces the need for human involvement. These sensor-driven monitoring systems ensure better inventory accuracy, product traceability, and a decrease in product loss or misplacement by promoting transparency across supply chain nodes.\u003c/p\u003e\u003cp\u003eThe focus on scalability and energy efficiency, two issues that are most commonly discussed in the reviewed literature, is equally important. Achieving maximum power utilization without sacrificing data transmission integrity becomes increasingly important as the density and scale of IoT sensor deployments rise. Low-power wide-area networks (LPWANs), adaptive communication protocols, and duty-cycling are some of the methods that have been extensively studied to strike a compromise between performance efficiency and energy usage. The energy efficiency and communication coverage of the various LPWAN communication systems were studied [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notwithstanding these developments, scalability in diverse and expansive industrial settings still necessitates creative energy-conscious structures and astute routing systems.\u003c/p\u003e\u003cp\u003eThe increasing dependence of warehouse ecosystems on data analytics and predictive intelligence is another recurring issue. Data containing a large number of samples have stronger statistical power, as over-complicated data with great high-dimensional feature space could lead to a greater rate of false discovery[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Predictive stock replenishment, autonomous mobile robot (AMR) routing path optimization, and early problem detection in operational equipment are all made possible by the growing use of AI-driven analytical models. The technology for handling materials has dramatically improved in the last few decades. The transformation of automated guided vehicles (AGV) into autonomous mobile robots (AMR) is one significant advancement [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].IoT-generated sensor data and machine learning algorithms come together to assist the shift from reactive to predictive operational paradigms by enabling more proactive and informed warehouse management methods.\u003c/p\u003e\u003cp\u003eOne major obstacle to the realization of fully integrated smart warehouse settings is the problem of interoperability and standards. Cross-system integration is hampered by the isolated data silos created by many warehouses' continued reliance on proprietary or non-standard communication protocols. Although potential foundations for interoperability are provided by international standards like OPC Unified Architecture (OPC-UA) and oneM2M, their implementation is still uneven and fragmented. An intelligent information infrastructure that manages data gathered between objects or between objects and humans is known as oneM2M [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For smooth data interchange and multi-vendor interoperability in international warehouse operations, a single and standardized communication infrastructure must be established.\u003c/p\u003e\u003cp\u003eAnother urgent issue that was found in all of the studies that were surveyed is security and privacy. Warehouse infrastructures are more susceptible to spoofing attacks, illegal access, and data breaches as they get more interconnected. Applying cutting-edge technologies like blockchain, artificial intelligence (AI), and quantum computing to enhance malware detection in Internet of Things (IoT) digital devices and guarantee quicker reaction to any attacks is an essential cybersecurity threat mitigation technique [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, the majority of nations have already drafted data protection legislation to improve cybersecurity. When keeping and processing data, individuals and corporate organizations must adhere to legal requirements and rules [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].In order to preserve sensitive operational data and enable dispersed intelligence, recent research endeavors have suggested integrating blockchain technologies to create decentralized trust frameworks and federated learning models. To evaluate scalability and resilience, these methods must be further validated in actual industrial settings, as they are still primarily experimental.\u003c/p\u003e\u003cp\u003eLastly, as enterprises place a higher priority on environmental responsibility and energy saving, the idea of sustainability and Green IoT is receiving a lot of attention. To reduce the environmental impact of warehouse operations, innovations including self-powered communication modules and energy-harvesting sensor nodes are being developed. In addition to extending the lifespan of devices, these technologies support the larger goal of carbon-neutral, sustainable logistics networks that are in line with international sustainability objectives.\u003c/p\u003e\u003cp\u003eAll of these results highlight how IoT-based sensor networks have the ability to revolutionize warehouse management procedures. The industrial revolution's widespread production of goods has increased the need for contemporary warehouses[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The next generation of IoT-enabled warehouses, however, must be designed with an integrated approach\u0026mdash;combining edge intelligence, adaptive communication frameworks, and secure distributed architectures\u0026mdash;to achieve truly autonomous and sustainable operational ecosystems, as evidenced by persistent gaps in energy scalability, cybersecurity, and system interoperability. These realizations serve as the basis for determining current research gaps and potential avenues for future investigation, which are covered in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e"},{"header":"5. Research Gaps and Future Directions","content":"\u003cp\u003eThe integration of IoT-based sensor networks into warehouse management systems has advanced remarkably, but there are still a number of important research gaps that prevent the development of completely autonomous, safe, and sustainable smart warehouse ecosystems. The incorporation of technologies from the fourth industrial revolution into the supply chain, such as the Internet of Things (IoT) architecture for inventory and warehouse management, is a game-changer for decision-making processes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].To facilitate the shift from experimental prototypes to reliable, scalable industrial implementations that can support the upcoming generation of intelligent logistics networks, these gaps must be filled.\u003c/p\u003e\u003cp\u003eInteroperability, in particular the lack of standardized middleware frameworks that enable smooth connection and data exchange across diverse IoT platforms, is one of the most enduring problems. Existing warehouse infrastructures frequently depend on proprietary or vendor-specific solutions, which restrict cross-platform integration and produce data silos. For various sensing, networking, and analytics components to function together, open, interoperable frameworks built on established communication protocols and semantic data models are essential.\u003c/p\u003e\u003cp\u003eIntegration of Edge and AI is another important research frontier that is yet mostly unexplored. The rise and integration of heterogeneous distributed compute paradigms\u0026mdash;and the edge\u0026mdash;are driving a fundamental shift in computing and artificial intelligence (AI), with the edge serving as the front-row seat for delivering cloud service offerings to end users [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Although data analytics and predictive modeling have benefited greatly from the widespread usage of artificial intelligence (AI), little research has been done on integrating AI capabilities right at the network edge for localized, real-time decision-making. The discipline of artificial intelligence (AI) seeks to create intelligent entities that automate the process of dynamically generating different design decisions for industrial computational deployment in the context of resource management [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Context-aware responsiveness, lower communication latency, and improved scalability would all be made possible by such integration. Novel architectures that integrate distributed computing with lightweight AI models tailored for energy-constrained sensor contexts are needed to advance this field.\u003c/p\u003e\u003cp\u003eAnother significant unresolved issue with IoT-driven warehouse systems is security and privacy. One of the most well-liked new concepts in recent years is the Internet of Things. It uses a network of interconnected parts to find, send, and analyze data [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Traditional cryptographic mechanisms are unfeasible due to the limited computing and energy resources of Internet of Things devices. In order to lessen the computational effect of security algorithms, lightweight cryptographic algorithms are being examined in the scientific literature [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As a result, the development of intrusion detection and encryption systems that are both lightweight and adaptive is still an unresolved challenge. Furthermore, sophisticated trust management frameworks and decentralized authentication methods are required to provide end-to-end security across hybrid cloud\u0026ndash;edge infrastructures. For dependable services, precise data gathering, device authentication, and safe decision-making in IoT trust management are essential [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimultaneously, the current body of research has paid little consideration to data governance and ethical compliance. It is more crucial than ever to comprehend data governance, privacy, and ethics related to data availability and use [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Clearer ethical standards and governance frameworks are necessary for the proper gathering, processing, and sharing of sensitive operational data as warehouse operations grow more data-driven. Potential privacy violations and noncompliance with international data protection laws like the General Data Protection Regulation (GDPR) are hazards associated with the absence of standardized data protection frameworks. According to the GDPR, organizations that handle personal data must specify the types of personal data they want to handle and why they are doing so [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, warehouse management is still in its early stages of pursuing sustainability and Green IoT. Few studies specifically address carbon-neutral IoT infrastructures, recyclable sensor materials, or energy-harvesting network topologies, despite the fact that energy-efficient communication and sensor optimization have been investigated. The vast advancements in communication and sensing technology have led to the connecting of \"things\" around us for a variety of purposes that improve the quality of our lives[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The creation of environmentally friendly, sustainable IoT solutions that meet business sustainability standards and global climate goals should be the top priority of future research.\u003c/p\u003e\u003cp\u003eWhen taken as a whole, these gaps outline distinct paths for further study. To fully utilize IoT-based sensor networks in warehouse management, it is essential to advance interoperability standards, enable edge-intelligent architectures, provide safe and moral data frameworks, and give sustainable design principles first priority. In addition to improving operational intelligence and resilience, closing these gaps will set the stage for the development of a new generation of independent, moral, and sustainable industrial ecosystems. It will take interdisciplinary cooperation between engineers, data scientists, and logistics specialists to close these gaps. The basis for a new generation of intelligent and secure warehouse ecosystems will be laid by this collaboration, which will improve sustainability, ethical data governance, and operational resilience.\u003c/p\u003e"},{"header":"6. Proposed Future Directions","content":"\u003cp\u003eFuture developments in IoT-based sensor networks for warehouse management can be guided by a number of strategic directions, which build upon the highlighted research gaps. The creation of unifying semantic interoperability frameworks that can smoothly combine diverse IoT platforms, devices, and communication protocols inside warehouse ecosystems is a top goal. Scale, near real-time automation, flawless interoperability, and the constantly expanding and diversifying IoT ecosystems have all remained major obstacles [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].To overcome the present fragmentation of IoT infrastructures, such frameworks should make use of standardized middleware architectures and ontology-based data models to facilitate consistent data exchange, contextual comprehension, and cross-vendor interoperability.\u003c/p\u003e\u003cp\u003eThe deployment of AI-powered self-healing wireless sensor networks (WSNs) with predictive failure detection and autonomous recovery capabilities is equally crucial. WSNs have become a game-changing technology for distributed sensing, data gathering, and communication in a variety of applications, from industrial automation to environmental monitoring [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Machine learning methods can be used by these intelligent networks to dynamically improve network architecture, predict node failures, and guarantee resilient performance even in unpredictable operating environments. Predictive analytics integration will greatly improve network reliability and lower maintenance costs at both the edge and cloud levels. Through the deployment of network, storage, and computation resources to the edge locations of wireless access networks, edge computing enhances the cloud computing concept by enabling end devices to run resource-intensive applications [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, combining blockchain technology with distributed edge networks is a viable option to create logistics frameworks that are safe, open, and auditable. Blockchain technology guarantees data integrity, decentralized authentication, and robust threat prevention in the designed architecture, hence enforcing proactive security[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].In multi-stakeholder supply chain systems, blockchain's decentralized architecture can guarantee immutable data provenance, traceability, and real-time warehouse transaction verification, enhancing cybersecurity and trust.\u003c/p\u003e\u003cp\u003eLast but not least, investigating bio-inspired routing and optimization algorithms offers a novel strategy for attaining long-term energy efficiency in dense IoT sensor deployments. However, efforts to improve security, accuracy, and usefulness through complicated algorithms have made these systems more complex, which has resulted in issues like increasing power consumption and energy inefficiency [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These algorithms can improve routing reliability, reduce energy consumption, and facilitate large-scale scalability by taking inspiration from biological processes, including ant colony optimization, brain adaptation, and swarm intelligence.\u003c/p\u003e\u003cp\u003eWhen taken as a whole, these future possibilities offer a thorough road map for developing IoT-based warehouse systems in the direction of increased sustainability, security, and intelligence. The next generation of smart warehouses can attain previously unheard-of levels of autonomy, resilience, and operational excellence by combining biologically inspired optimization, decentralized trust frameworks, self-healing intelligence, and semantic interoperability. These recommended strategies collectively establish IoT as the enabling backbone for ensuring adaptable, secure, and sustainable warehouse operations under Industry 5.0. Together, these paths support the new Industry 5.0 vision, which emphasizes technological ecosystems that are human-centric, moral, and flexible. Industry 5.0's fundamental elements, underlying technology, and guiding principles have been established [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e"},{"header":"7. Conceptual Framework","content":"\u003cp\u003eThis paper suggests a conceptual framework that combines distributed computation, intelligent sensing, and secure communication into a single architectural model to direct the creation of next-generation IoT-enabled warehouse management systems. Five interconnected layers make up the structure (shown in Fig.\u0026nbsp;2), which, when combined, allow for independent, flexible, and sustainable warehouse operations.\u003c/p\u003e\u003cp\u003eReal-time information on asset location, environmental conditions, and equipment status is gathered by heterogeneous IoT devices at the Sensing Layer, such as RFID tags, environmental sensors, and mobile robots. Through the use of communication and control protocols, the network and control layer's numerous routers, controllers, and servers effectively integrate every robot in the physical layer's MRS [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Duty-cycling and, when practical, energy-harvesting techniques are included in the design of these nodes to ensure energy-efficient operation. For wireless Internet of Things (IoT) networks with battery-powered nodes, energy consumption has emerged as the primary concern[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEdge gateways and embedded processors carry out initial analytics and inference operations near data sources in the Edge Layer, which serves as the intermediate processing tier. \"Edge computing (EC)\" aims to reduce bandwidth consumption and communication delay, facilitate real-time data processing, lower operating costs, increase scalability, and improve service quality[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. By making decisions locally, network latency is reduced and the computational strain on cloud resources is relieved.\u003c/p\u003e\u003cp\u003eThe Network and Middleware Layer uses standardized communication protocols like MQTT, CoAP, and OPC-UA to create smooth data flow. The work has been assessed using digital twins as an application in the context of vehicle-to-cloud communication[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. It incorporates semantic middleware, which promotes interoperability between multi-vendor systems and harmonizes data formats. At this layer, blockchain and lightweight encryption are integrated to guarantee decentralized trust management and safe, verifiable data exchange. The characteristics of blockchain technology, including its flexibility, support for integration, anonymity, decentralization, and autonomous control, have led to its tremendous growth [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLarge-scale data aggregation, machine learning, and predictive analytics are carried out at the Cloud Intelligence Layer to facilitate long-term decision support, predictive maintenance, and worldwide optimization. In order to improve informed decision-making, predictive analytics is crucial in offering profound insights to reduce uncertainty and increase overall efficiency in terms of resource allocation, inventory management, and demand fulfillment[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].In order to match operational insights with strategic planning, this layer also interfaces with supply chain management (SCM) and enterprise resource planning (ERP) systems. One well-known and widely used solution in corporate organizations that has been shown to integrate and automate operations, increase performance, and lower costs is the Enterprise Resource Planning (ERP) system[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLastly, the Application Layer offers managers and autonomous systems decision-support tools, automated control interfaces, and visualization dashboards. Continuous performance monitoring, autonomous mobile robot (AMR) route optimization, and real-time inventory tracking are all made possible by this layer. Autonomous Mobile Robots (AMRs) are now essential automation tools in smart homes, modern industries, and service sectors[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhen taken as a whole, the suggested framework lays the groundwork for intelligent, safe, and sustainable warehouse ecosystems that combine technological independence with human-centered, ecologically conscious design, embodying the ideals of Industry 4.0 and the new Industry 5.0 paradigm. As seen in Fig.\u0026nbsp;2, the conceptual architecture of IoT-enabled smart warehouses is shown below.\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\u003eAn overview of the suggested multi-layer Internet of Things framework for intelligent warehouse management\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\u003eLayer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary Functions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCore Technologies / Techniques\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eObjectives\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSensing Layer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData acquisition from IoT nodes, environment, and assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRFID, WSN, energy-harvesting sensors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReal-time monitoring and asset visibility\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEdge Layer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocal processing, inference, and control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEdge gateways, embedded AI, microcontrollers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReduced latency and bandwidth usage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNetwork \u0026amp; Middleware Layer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData exchange, interoperability, and security\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMQTT, CoAP, OPC-UA, Blockchain, lightweight encryption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReliable, secure communication across heterogeneous systems\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCloud Intelligence Layer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAggregation, analytics, and prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMachine learning, big data, ERP/SCM integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGlobal optimization and predictive maintenance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eApplication Layer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisualization, control, and automation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDashboards, AMR routing, decision-support systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntelligent automation and management insights\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"8. Novelty and Contribution","content":"\u003cp\u003eBy offering a thorough and integrative examination of the development of IoT-based sensor networks within warehouse management systems (WMS), this review significantly advances both academic research and industrial practice. It provides a synthesis of technology breakthroughs over a ten-year period (2015\u0026ndash;2025), methodically outlining the major developments, approaches, and frameworks that have influenced the evolution of smart warehouse settings. By combining disparate research streams' fragmented insights, the study demonstrates how IoT-driven connections might revolutionize operational efficiency, accuracy, and sustainability in contemporary logistics ecosystems.\u003c/p\u003e\u003cp\u003eThe creation of an organized taxonomy of IoT applications in the domains of intelligent automation, real-time monitoring, and logistics constitutes a major contribution of this review. This taxonomy serves as a fundamental resource for researchers and practitioners alike by clearly classifying the ways in which IoT-based sensor networks are applied in different facets of warehouse management, such as inventory tracking, predictive analytics, robotic coordination,\u003c/p\u003e\u003cp\u003eand environmental control.\u003c/p\u003e\u003cp\u003eA conceptual framework for next-generation smart warehouses is also presented in the paper, with a focus on safe, autonomous, and energy-efficient IoT systems. The suggested paradigm emphasizes how edge computing, AI, and sensor-driven analytics can work together to support self-optimization and adaptive decision-making in industrial processes. With improved performance, safety, and efficiency, these developments have the potential to completely transform the Internet of Things (IoT), smart cities, and many other industries[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis assessment concludes by outlining strategic research avenues for promoting interoperability standards, AI\u0026ndash;edge synergy, and sustainable warehouse operations. The report offers a roadmap for creating intelligent, robust, and ecologically conscious IoT ecosystems that are in line with the tenets of Industry 4.0 and the human-centric values of Industry 5.0 by defining forward-looking paths for innovation and system design.\u003c/p\u003e\u003cp\u003eTogether, these contributions establish this review as a fundamental resource for academics, technology developers, and business professionals seeking to develop, deploy, and enhance intelligent, safe, and sustainable next-generation IoT-based warehouse management systems.\u003c/p\u003e"},{"header":"9. Conclusion","content":"\u003cp\u003eWarehouse management has seen a dramatic change with the introduction of IoT-based sensor networks, signaling a move toward intelligent automation, data-driven operational decision-making, and real-time visibility. Ten years of study (2015\u0026ndash;2025) have been compiled in this systematic review to show how IoT technologies have developed from basic tracking systems to complex, adaptable infrastructures that can sustain large-scale, autonomous, and intelligent warehouse ecosystems.\u003c/p\u003e\u003cp\u003eThe results highlight significant developments in AI-driven automation, predictive analytics, and edge-enabled sensing, all of which improve the effectiveness, responsiveness, and intelligence of contemporary warehouse systems. Nonetheless, there are still enduring issues with interoperability, cybersecurity, scalability, and sustainability, all of which pose significant obstacles to the development of completely autonomous smart warehouses. The creation of unified communication frameworks, safe and compatible data pipelines, and energy-efficient architectures intended to preserve performance and dependability in dynamic industrial environments is necessary to overcome these constraints.\u003c/p\u003e\u003cp\u003eThe next generation of warehouse management systems needs to be self-optimizing, intelligent, and secure IoT ecosystems that use ethical data governance, distributed intelligence, and robust network topologies. IoT-driven sensor networks will continue to be the technological foundation of robust, transparent, and sustainable supply chain ecosystems as global logistics and supply chains undergo further rapid digital transformation, promoting efficiency, adaptability, and innovation across industrial operations globally.\u003c/p\u003e\u003cp\u003eIn order to evaluate the suggested conceptual framework's scalability, interoperability, and durability in practical settings, future studies should empirically validate it across a range of industrial situations. In the upcoming ten years, intelligent, self-sufficient, and sustainable warehouse ecosystems will become a reality if these obstacles are overcome.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors affirm that the work described in this publication was not influenced by any known competing financial interests or personal relationships.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eDeclaration\u003c/p\u003e\u003cp\u003eNo specific grant from a public, private, or nonprofit funding organization was obtained for this study.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe authors would like to express their sincere gratitude to the Department of Computer Science and Engineering at Islamic University in Kushtia, Bangladesh, for its ongoing academic and institutional support during the development of this study, \"A Systematic Review of Recent Advances in IoT-Based Sensor Networks for Warehouse Management.\" The University of Alabama at Birmingham, Trine University, and Western Illinois University are also acknowledged by the writers for their scholarly materials, technological assistance, and collaborative research, all of which were crucial to the accomplishment of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eApplication of Supervisory Control Theory with Warehouse Automation Case Study Accessed: Nov. 10, 2025. 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Springer Nature Switzerland, Cham, pp 229\u0026ndash;245. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-91802-5_10\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-91802-5_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Edge Computing, Industrial IoT, Internet of Things, Smart Logistics, Supply Chain Optimization, Warehouse Management Systems, Wireless Sensor Networks","lastPublishedDoi":"10.21203/rs.3.rs-8151337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8151337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndustrial processes have undergone significant changes as a result of the Internet of Things' (IoT) rapid growth, particularly in warehouse management systems (WMS). These days, IoT-powered sensor networks serve as the foundation for intelligent, networked ecosystems that enable data-driven decision-making, adaptive control, and real-time monitoring throughout international supply chains.\u003c/p\u003e\u003cp\u003eWith a focus on architectural design, communication protocols, data analytics frameworks, and intelligent automation tactics, this systematic analysis compiles recent advancements in IoT-based sensor networks for warehouse management. A total of 1,050 academic publications (2015\u0026ndash;2025) were obtained from significant scientific databases using the PRISMA methodology. After eliminating 250 duplicates, 800 studies were reviewed, and 107 excellent articles were included in the final synthesis.\u003c/p\u003e\u003cp\u003eThrough energy-optimized connectivity, context-aware routing, edge intelligence, and AI-driven predictive analytics, the reviewed evidence demonstrates that IoT-enabled sensor systems significantly enhance warehouse efficiency. Nonetheless, enduring issues persist with cybersecurity, standardization, data interoperability, and sustainable scaling.\u003c/p\u003e\u003cp\u003eThis study closes by highlighting important research gaps and proposing an integrated conceptual framework. The goal is to develop next-generation, autonomous, safe, and sustainable IoT-based warehouse ecosystems. The study emphasizes resilience, adaptation, and sustainability in global logistics. It further aligns these advances with the human-centric vision of Industry 5.0 and the guiding principles of Industry 4.0.\u003c/p\u003e","manuscriptTitle":"A Systematic Review of Recent Advances in IoT-Based Sensor Networks for Warehouse Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 03:18:27","doi":"10.21203/rs.3.rs-8151337/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":"e4b45390-89f9-4036-8203-9725d8574cd0","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58223535,"name":"Environmental Engineering"},{"id":58223536,"name":"Systems and Networking"}],"tags":[],"updatedAt":"2025-11-20T03:18:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 03:18:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8151337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8151337","identity":"rs-8151337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}