Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using improved elephant herd optimization algorithm

Wireless sensor networks (WSNs) currently have numerous applications, especially in tracking and observing non-human activities. Sensor nodes in WSNs are known to have limited lifespans due to continuous sensing, which causes the battery to drain quickly. Therefore, Energy consumption is a significant research issue in WSN-assisted applications. Energy conservation now places a high priority on exact clustering and the choice of the best route from the sensor nodes to the sink. This research paper proposes a fuzzy with adaptive sailfish optimizer (ASFO) for cluster head selection and improved elephant herd optimization approach to find the most efficient shortest path route to preserve energy efficiency in WSNs. The suggested hybrid approach was implemented in MATLAB and achieved results are compared to those of four widely-used techniques, such as improved artificial bee colony optimization-based clustering (IABC-C), genetic algorithms (GA), particle swarm optimization (PSO), and hierarchical clustering-based CH election (HCCHE) approach. The Fuzzy with ASFO technique improves the Quality of Service (QoS) of performance metrics such as energy usage, packet loss ratio, end-to-end delay, packet delivery ratio, network lifetime, and buffer occupancy. The results show that the suggested Fuzzy with SFO has a better packet delivery ratio (99.8%), packet latency (1.12 s), throughput (98 bps), energy usage (10.90 mJ), network lifetime (5400 cycles), and packet loss ratio (0.6%) than the existing methods (PSO, GA, IABC-C, and HCCHE algorithms).