Wireless sensor network Energy -Efficient clustering in wireless sensor network using a hybrid meta heuristic approach based on firefly optimization and genetic algorithm

Wireless sensor network

Abstract

 Wireless Sensor Networks (WSNs) are essential for applications such as military operations, healthcare, and environmental monitoring. However, a major challenge in WSNs is extending the network lifetime, which can be effectively managed through a cluster-based network organization. Choosing the best cluster head is essential since it has a direct impact on network performance. However, problems including early node depletion, unequal energy distribution, and shortened network longevity are frequently caused by the cluster head selection techniques now in use. These issues have not been resolved by conventional methods like Randomized Clustering and Fixed Cluster Head selection, underscoring the need for more effective approaches. This paper presents an optimized cluster head selection technique that combines Genetic Algorithm (GA) and Firefly Optimization (FFO) in a hybrid metaheuristic approach to overcome these difficulties. Cluster heads are first identified and their positions are updated using FFO. Then, GA employs fitness values to determine which cluster head is most effective for transmitting data to the base station. According to simulation data, the suggested algorithm, FOGA, improves network lifetime by 1% over FFO and 2% over GA, outperforming both FFO and GA. Compared to FFO and GA, energy usage is lower by 15% and 25%, respectively. These findings show that FOGA successfully increases network lifetime and energy efficiency.