Energy-Efficient Cluster Head Rotation in WSNs Using Bee Colony Optimization

In this study, we present a method designed to improve energy efficiency and balance the workload across Wireless Sensor Networks (WSNs). Our approach dynamically selects and rotates cluster heads (CHs) based on factors such as remaining energy, node mobility, distance to the base station, and data processing needs. By focusing on nodes with more energy and lower mobility, we aim to extend the network's operational life and prevent any single node from being overburdened. At the heart of our method is the Artificial Bee Colony (ABC) optimization algorithm, which mimics the foraging behavior of bees. This algorithm helps to identify the best nodes to act as CHs, balancing the energy load across the network and maintaining strong connectivity within clusters. Our simulations show that this method outperforms existing protocols like FEEC and PSAP-WSN, particularly when it comes to distributing energy more evenly and extending the network's lifespan. By continuously rotating the CHs, we ensure that energy consumption is spread out, leading to improved network performance and sustainability. The results indicate that this dynamic and adaptive approach is highly effective in maintaining a balanced energy distribution, making it a robust solution for energy management in WSNs.