Particle swarm optimization–assisted ivy algorithm for cluster head selection in wireless sensor networks

Energy-efficient Cluster Head (CH) recognition is a key challenge in Wireless Sensor Networks (WSNs), as sensor nodes possess limited energy resources and data transmission is energy-intensive. Most hierarchical routing protocols in WSNs consider CH selection and inter-cluster routing as two separate optimization problems. This may result in an energy imbalance and even cause early failure of the WSN, particularly in large-scale WSNs. To overcome this problem, the present study proposes a novel Particle Swarm Optimization–Assisted Ivy Algorithm (IVY-PSO) for CH selection in WSNs. PSO can perform global search, and the adaptive local exploitation of Ivy plants is also considered in the proposed approach to achieve an optimal trade-off between global and local searches. To consider energy efficiency in CH selection, a multi-objective fitness function is developed to optimize the selection process in WSNs. To further reduce long-distance transmissions and hotspot effects, K-means clustering is employed to group CHs, while equidistant relay nodes and Dijkstra’s algorithm are used to construct energy-efficient multi-hop transmission paths to the base station. In-depth MATLAB-based simulations reveal that the proposed IVY-PSO-based protocol significantly prolongs network lifespan and improves energy balance compared with current clustering and routing techniques. The findings confirm that IVY-PSO provides a robust and effective optimization framework for CH selection and hierarchical routing in WSNs.