A Hybrid Energy-Efficient Framework for Wireless Sensor Networks Using Artificial Algae Algorithm and Ant Colony Optimization

The limited battery constraint in Wireless Sensor Networks poses a major challenge to the longevity and reliability of such networks. Energy efficiency is, therefore, critical to consider while designing these networks. In this study, we propose a novel hybrid energy-efficient framework that uses two bio-inspired algorithms. For the selection of cluster head nodes, we use Artificial Algae Algorithm, which is a novel algorithm in wireless sensor networks applications. The second algorithm that we used is the well-known Ant Colony Optimization for routing through selected cluster heads, which has been used by researchers several times. Artificial Algae Algorithm ensures cluster head selection based on a multi-objective fitness function incorporating remaining energy, distance to cluster heads, distance between cluster head and sink node, and node degree. Through extensive simulations, we compare the performance of our proposed method against famous algorithms such as LEACH, Grey Wolf Optimizer, and Particle Swarm Optimization. To gauge the performance, we used different metrics such as residual energy, number of alive nodes, First Node Dead, and Last Node Dead. It was observed that the residual energy of the proposed system, when calculated, was 186% and 33% more, respectively, than the two algorithms. The findings proved that the proposed method holds potential for enhancing the energy efficacy, steadiness, and lifetime of the system.