Energy Consumption in Wireless Sensor Networks Using Fruit Fly and Ant Colony Optimization Algorithms in Homogenous and Heterogeneous Environments

Wireless sensor networks (WSNs) is crucial in sensing environmental conditions in far-flung areas. However, their energy consumption remains a critical issue, affecting the network’s lifetime and coverage area. Clustering has emerged as an efficient strategy to prolong sensor network lifespan, and the Fruit Fly Algorithm (FFA) and Ant Colony Optimization (ACO) are promising techniques for cluster formation and efficient path establishment, respectively. In this study, we propose an innovative approach that combines FFA for cluster formation and ACO for path establishment. This novel algorithm is implemented in MATLAB and evaluated in both homogeneous and heterogeneous environments. We compare our proposed algorithm with the Butterfly Optimization Algorithm (BOA) based clustering and Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Our findings suggest that the algorithm we proposed significantly outperforms both BOA based clustering and LEACH. It takes lifetime of Network and area of coverage, particularly in heterogeneous environments.