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-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.

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.

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.

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.

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].

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.

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.

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.