Cluster Head Selection Optimization for Lifetime Enhancement in WSN

WSNs (Wireless sensor networks) are essential in terms of large-scale environmental monitoring, smart infrastructure, and cyber-physical systems, and constrained node energy and autonomy of operation have a very strong impact on the network lifetime. Productive clustering and sturdy cluster head choice are thus the being of the perpetuality in the information collection and communication dependability. In this work, a multi-objective fitness preparation has been proposed, to combine the consideration of residual energy, node distribution, and communication distance and get an optimization of the CHS (cluster head selection) strategy. The concept of a hybrid optimization can combine a chaotic search behavior with the fuzzy decision modeling to improve the stability in selecting as well as prevent early energy depletion. Unequal clustering is actively used to control the size of clusters based on how close they are to the BS (base station) and periodic re-clustering balances energy consumption used by nodes. The evaluation carried out based on simulation proves that the proposed solution is better than traditional LEACH, protocols based on PSO (particle swarm optimization), and this protocol based on grey wolf optimization in terms of network lifetime, energy efficiency, and stability period. The findings substantiate enhanced load balancing and ensured sensing functionality, which is why the strategy can be used in the energy-limited and massive deployment of WSNs.