Dual CH Selection and Routing for Network Lifetime Maximization in Wireless Sensor Network Using Red Billed Blue Magpie Optimization Algorithm

In Wireless Sensor Networks (WSNs), an effective way to maximize network lifetime is by minimizing energy consumption during clustering and routing processes.However, the existing clustering and routing algorithms have limitations such as excessive communication overhead, load imbalance and high energy consumption, causing reduced network lifetime in WSNs.To overcome these problems, an Adaptive T-variation Strategy -Red Billed Blue Magpie Optimization algorithm (ATS-RBMO) is proposed to maximize network lifetime by efficiently reducing energy consumption, network congestion and communication overhead during data transmission.The conventional RBMO algorithm employs static parameters, which result in getting stuck in local optima due to a fixed balance between exploration and exploitation.Therefore, RBMO with the ATS strategy is introduced to address this issue by adaptively adjusting the parameters to enhance convergence speed and avoid local optima.This enables the selection of optimal dual cluster heads and routing paths in the network, ensuring balanced energy consumption among nodes and increasing overall network lifetime.The experimental results of the proposed RBMO achieves a network lifetime of 98% and 78% for 100 and 500 nodes respectively, which is higher than existing approaches like Boosted Sooty Tern and Piranhav Foraging Optimization (BST-PFO) algorithm.