AI-Driven Energy-Efficient Clustering and Routing in Wireless Sensor Networks: A Comprehensive Review

Abstract

IoT environments are more dependent on Wireless Senor Networks (WSN) that offer real time figure-in of environmental monitoring, industrial applications, healthcare, military and smart city applications. The pursuit of network operation is, however, confronted with severe challenges such as energy constraint, computation limitation, dynamic network topology, and unstable links. The emergence of novel methods such as AI, ML, DL, RL, and bio-inspired metaheuristic optimization methods offers an exciting avenue to improve routing efficiency and clustering robustness of WSNs. This work is aimed at providing a comprehensive overview on state-of-the-art AI driven clustering and routing techniques. They involved a host of AI related diverse elements including integrated optimization frameworks, RL based adaptive routing, multi-objective genetic models, intelligent paradigms with Software-Defined Networking (SDN), FPGA accelerated routing, and other best matching solutions for particular Qos aware application. Most of the schemes achied e from 12 to 30% of energy improvement and 15 to 32% of Network lifetime improvement over other schemes when compared one to another. Moreover, the paper identifies core research challenges such as multi-objective trade-offs modeling, security against adversarial attacks, scalability beyond 1000 nodes, computational feasibility testing in small hardware, and pinpointing that none of the existing solutions tackles all of them perfectly. This study is concluded by/proposing ah structured research directions/paths leading to [27] the realization of scalable, secure, interpretable and hardware-aware AI-WSN routing architectures.