Sustainable Cluster Head Selection in Wireless Sensor Networks Through Bio-Inspired Optimization

The selection of cluster heads is a critical challenge in Wireless Sensor Networks (WSNs) because it has a direct impact on the overall performance, network lifetime, and energy efficiency. The Crayfish Optimization Algorithm (COA) is a bio-inspired metaheuristic approach that is introduced in this study. It is intended to improve cluster head selection by emulating the foraging and decision-making behavior of crayfish. COA optimizes the selection of cluster heads by considering residual energy, connectivity, and network coverage. This is achieved by utilizing a pheromone-like memory mechanism to balance exploration and exploitation, thereby facilitating adaptive learning and enhanced convergence. The simulation results indicate that COA substantially improves data transmission efficiency, extends the lifespan of networks, and reduces energy consumption in comparison to current methods. It is well-suited for large-scale, dynamic WSN environments due to its robustness and scalability.