IoT Applications Using Clustering Protocols in Wireless Sensor Networks wsns: Review
DOI:
https://doi.org/10.29304/jqcsm.2025.17.11978Keywords:
Wierless Sensor Network, Cluster head, Clustering, Energy – Efficient, IoT applications.Abstract
Wireless sensor networks for Internet of Things applications face many challenges and problems, the most important of which is the battery life of the sensor, which leads to damage to battery usage and the process of using algorithms and protocols to help maintain battery life for a longer period of time by reducing the transmission and reception process and improving energy usage. The challenges of WSN focus on optimizing energy consumption through efficient network protocols, data aggregation techniques, adaptive power control, and energy harvesting methods. In this work present twenty works that provide different protocols and algorithms to improve the life of the wireless sensor network in managing energy distribution effectively and by evaluating the protocols and algorithms used to know their efficiency in energy usage and their suitability for application in the Internet of Things with a focus on the Leach algorithm for its simplicity, efficiency, scalability and suitability for adaptation in the Internet of Things and its application in high-density networks requires fast data processing through comparison. These protocols include adaptive clustering, energy-efficient routing, hybrid techniques, and reinforcement learning-based methods. In this Review, techniques such as fuzzy logic integration (e.g., FLH-P), 5G MIMO technology (e.g., IMIMO-5G BEE), XOR coding with adaptive sampling (e.g., EDAS), SDN-based multi-hop clustering (e.g., SD-MHC-RPL), and reinforcement learning (e.g., Deep Q-Network) were used, which are characterized by their energy efficiency. These algorithms suffer from some problems such as the expansion of dense networks and the increase in computational complexity in dynamic environments, dependence on specific infrastructures such as 5G, and sensitivity to parameter tuning. These algorithms are characterized by increasing the packet delivery rate, reducing energy consumption, and enhancing throughput. Leach improves energy efficiency through adaptive clustering and routing. FLH-P enhances cluster head selection using fuzzy logic but increases computational complexity. IMIMO-5G BEE utilizes 5G MIMO technology for multi-mode transmission but is limited by its dependence on 5G infrastructure.
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