Enhancing Cluster Head Selection for Energy Efficiency in Wireless Sensor Networks: A Study of Swarm Intelligence Algorithms

Wireless sensor networks (WSNs) are becoming more important than ever because to the Internet of Things (IoT), which is facilitating increased worldwide interconnectivity. Micro sensor nodes in WSNs that communicate wirelessly have advanced to new levels of wide-scale application implementation. Due to their short battery lives, WSNs must be energy efficient. CH selection plays a crucial influence in WSN effectiveness. Both single-hop and multi-hop routing techniques are used, but multi-hop routing may result in higher node loads close to sink nodes. The increasing node load brought on by growing distance from sink nodes, however, may provide problems for single-hop cluster routing approaches. This study emphasizes the significant potential for wireless sensor networks (WSNs)-based energy efficiency algorithms to increase the longevity of WSNs. These protocols provide promising answers for boosting WSN durability and efficiency by optimizing energy consumption and resource utilization. For clustering optimization in WSNs, swarm intelligence approaches including fuzzy logic, ant colony optimization, and particle swarm optimization (PSO) have been successfully used by many researchers. To enhance cluster head selection, these methods make use of fitness functions based on variables including residual energy, intra-cluster distance, and node degree. The bee colony optimization algorithm is the main topic of this study. Examining this energy efficiency methods' performance in intricate and wide-ranging WSN infrastructures is essential to verifying their viability and effectiveness.