Enhancing WSN Performance: A Hybrid DA-SA Model for Energy Efficiency Clustering and Data Transmission

Abstract

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes, whose limited energy reserves significantly impact the network's lifespan. Prolonging the operational lifetime of WSNs is critical, particularly for energy-efficient data transmission and routing. Clustering, a key technique in WSNs, relies heavily on the optimal selection of cluster heads (CHs) to manage data aggregation and routing efficiently. However, ensuring energy efficiency while maximizing the network's lifespan and minimizing delays remains a formidable challenge in WSN design. In order to address these challenges, a hybrid optimization approach combining the Dragonfly Algorithm (DA) with Simulated Annealing (SA) is proposed. This approach leverages DA's exploration capabilities for identifying potential CHs and SA's exploitation mechanisms for fine-tuning the selection process based on critical constraints such as residual energy, node distance, and packet transmission ratios. The hybrid model ensures centralized cluster formation, with the base station selecting CHs and notifying cluster nodes of their assignments. During data routing, the algorithm evaluates paths based on fitness values, selecting the most energy-efficient and latency-minimized route to the sink node. The proposed DA-SA approach demonstrates improved energy efficiency, prolonged network lifetime, and reduced computational overhead compared to traditional methods.