Using Hybrid GA/PSO-Mobile Sink to Improve Energy Efficiency and Network Lifetime for LEACH Protocol in WSNs
Noor Raad Saadallah, Salah Abdulghani Alabady · 2023
Utilising a hybrid Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) strategy in conjunction with a mobile sink, this study proposes a new approach for improving energy efficiency and extending the network lifetime in Wireless Sensor Networks (WSNs). This work is founded on the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. The conventional use of static sink nodes frequently results in an energy imbalance among sensor nodes, especially those close to the sink, leading to the premature exhaustion of their energy reserves. To combat this difficulty, a mobile sink framework is proposed, accompanied by a hybrid optimization strategy. Utilizing the Genetic Algorithm (GA), the optimal Cluster Heads (CHs) for the mobile sink’s trajectory are determined. If the optimal path cannot be determined, the PSO phase selects Collector Points for the mobile sink. Afterward, the optimal route between these sites is computed, and the superior route is selected for the ongoing cycle. The proposed hybrid GA/PSO-mobile sink strategy is contrasted to conventional WSN protocols through extensive simulation and performance evaluation. The results demonstrate significant improvements in energy efficiency and network lifetime, validating the approach’s efficacy in extending the operational lifetime of WSNs under the LEACH protocol while maintaining efficient data aggregation and communication.