Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by finite amount energy to gather environmental data. WSNs use nodes (SNs) collect transmit However, the power supplied network is restricted. Thus, SNs must store as often extend lifespan network. In proposed study, effective clustering longer lifetimes achieved using multi-swarm optimization (MSO) game theory based on locust search (LS-II). this research, MSO used improve optimum routing, while LS-II approach employed specify number cluster heads (CHs) select best ones. After CHs identified, other components allocated closest them. A theory-based energy-efficient applied WSNs. Here each SN considered player game. The can implement beneficial methods for itself depending length idle listening time active phase then determine choose whether or not rest. with (MSGE-LS) efficiently selects CHs, minimizes consumption, improves lifetime networks. findings study indicate that MSGE-LS an method because its result proves it increases clusters, average extension, reduction packet loss, end-to-end delay.