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

WSNs (Wireless sensor networks) play an important role in sensing environmental conditions in far-flung areas. However, their energy usage remains a crucial 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 Biogeography-Based Optimization Algorithm (BOA) and the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm. Our findings show that the proposed algorithm significantly outperforms both BOA and LEACH as per the network longevity and coverage area, particularly in heterogeneous environments.