Dynamic Weighting Technique Based Arctic Puffin Optimization for Energy Efficient Data Transmission in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are designed using various sensor nodes, which are employed for communication in diverse applications that include healthcare, industrial areas, and smart cities. Specifically, WSNs demand energy-efficient clustering and routing mechanisms that assist in extending network lifetime due to the limited battery capacity of sensor nodes. However, many existing meta-heuristic-based approaches incorporate fixed objective weights that struggle to adapt under dynamic network conditions and result in premature convergence and uneven energy depletion among cluster heads. To address these challenges, this manuscript proposes a Dynamic Weighting Technique-based Arctic Puffin Optimization (DWT-APO) algorithm for cluster head selection and optimal routing in WSNs. The DWT-APO introduces an adaptive weight adjustment mechanism that dynamically helps balance exploration and exploitation throughout the optimization process. Finally, a unified multi-objective fitness function is formulated by combining residual energy, communication distance, delay, and node degree, which enhances cluster stability and routing efficiency. The simulation outcomes illustrate that the proposed DWT-APO attains improved network lifetime up to 60%, enhancing residual energy by 66.7%, and throughput by 24% when compared to the Ultra-Scalable Ensemble Clustering Technique (U-SENC) with Flamingo Search Algorithm (FSA) under high-round network conditions.