Wireless Sensor Network Energy Optimization by Frog Leaping Genetic Algorithm

In order to operate effectively in challenging climatic conditions, it is essential for sensor devices to possess the capability of engaging in bidirectional communication with each Internet of Things (IoT) device. This study aims to gather data on Wireless Sensor Network (WSN) devices, specifically focusing on the available battery energy and geographical placement within a designated zone. The analysis of the data for cluster head selection is conducted at a centralized node, often the base station. This centralized approach has the advantage of reducing the cost of WNS devices in terms of both CPU requirements and energy consumption. Clustering of nodes was done by a frog leaping genetic algorithm where memeplex based crossover operation has improved the work efficiency. Due to dynamic nature of the work nodes, a clustering approach was used by the work. Proposed frog leaping WSN optimization (FLWSNO) model was implemented on MATLAB. An experiment was conducted to investigate various wireless sensor network (WSN) circumstances. The results demonstrate that the suggested approach has effectively enhanced the longevity of the network.