Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks (WSNs) are a collection of sensor nodes distributed in space and connected through wireless communication. The sensor nodes gather and store data about the real world around them. However, the nodes that are dependent on batteries will ultimately suffer an energy loss with time, which affects the lifetime of the network. This research proposes to achieve its primary goal by reducing energy consumption and increasing the network's lifetime and stability. The present technique employs the hybrid Mayfly Optimization AlgorithmEnhanced Ant Colony Optimization (MFOA-EACO), where the Mayfly Optimization Algorithm (MFOA) is used to select the best cluster head (CH) from a set of nodes, and the Enhanced Ant Colony Optimization (EACO) technique is used to determine an optimal route between the cluster head and base station. The performance evaluation of our suggested hybrid approach is based on many parameters, including the number of active and dead nodes, node degree, distance, and energy usage. Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future. The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm (HSFLBOA), Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm (HSRODE-FFA), Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm (SADSS-IABCA), and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution (EECHS-ISSADE).