A Comprehensive Survey on AI-Driven Optimization and Dynamic Cluster Head Selection for Energy-Efficient Wireless Sensor Networks

Abstract— There is no doubt that the Wireless SensorNetworks (WSNs) are indispensable to a variety of applicationswhich include environmental monitoring, healthcare, smartgrids, and industrial IoT systems. However, WSNs still sufferfrom the same problems of limited power supply, insecure datatransfer, and network scalability. The current review attemptsto show all the recent developments in the optimization of theAI-based approaches designed to solve the limitations ofWSNs. Emphasis is put on the application of artificialintelligence and machine learning techniques that cover hybridmodels, deep learning, swarm intelligence, and heuristicalgorithms that help in the detection of anomalies, selection ofcluster heads, routing efficiency, and management of energy inWSNs overall. The dynamic selection of the cluster heads is oneof the most talked-about issues in the paper; particularly, themethods using the metaheuristic and clustering algorithmswhich greatly improve the lifetime of the network and balancethe energy usage are highlighted. The paper is going to discussmethodologies, provide a summary of performancecomparison, pinpoint research voids like deployment issues inreal life and overhead of computation, and suggest futureresearch paths such as federated learning and real-timeadaptive analytics. This review will help researchers andpractitioners who want to create WSN solutions that areenergy-efficient, secure, and resilient and that are supportedby intelligent optimization frameworks.