Optimal Cluster Head Selection in Wireless Sensor Network via Combined Osprey-Chimp Optimization Algorithm: CIOO

The development of Wireless Sensor Network (WSN) has gained significant attention for smart systems due to their potential use in a wide range of areas. WSN consists of tiny, independently arranged sensor nodes that run on batteries. The resources and energy usage for sensor nodes are the major factors. Particularly, the unbalanced nodes’ raises the energy use and reduces the network life-span. Energy efficiency in WSN cluster head selection remains a challenging task. The best method has been developed for reducing node energy consumption is clustering. However, the current clustering strategy failed to properly allocate the energy needs of the nodes without considering energy features, node quantity, as well as adaptability. Hence, there is need for advanced clustering process with new optimization tactics, and accordingly, a new cluster-head selection model in WSN is proposed in this work. Initially, the clustering process is done by the k-means algorithm. The Cluster Head (CH) selection is the subsequent progress under the consideration of node’s energy, distances, delays, and risks as well. A novel CIOO (Chimp Integrated Osprey Optimization) algorithm combining the Osprey and Chimp optimization algorithm is proposed for Cluster Head Selection (CHS). Finally, the performance of proposed model is evaluated over the conventional methods.

@article{B2023,
  title     = {Optimal Cluster Head Selection in Wireless Sensor Network via Combined Osprey-Chimp Optimization Algorithm: CIOO},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Vikhyath K B and Achyutha Prasad N},
  doi       = {10.14569/IJACSA.2023.0141241},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141241}
}