Hybrid Metaheuristic Optimization Algorithms for Energy Aware Fine Cluster Head based Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) and their applications reduce energy consumption to extend sensor node lifetime. WSN applications require energy-efficient communication. Resource constraints limit WSN storage, computing, battery power, and communication. Data processing, aggregation, and clustering are energy-intensive, and deployment and discovery are complicated. Selecting Fine Cluster Heads (FCHs) and Cluster Head (CHs) to extend WSN lifetime is still difficult. This study uses bacterial foraging optimization and ant colony optimization to find suitable gateways (FCHs), CH nodes, and routes from sensor nodes (SNs) to base stations (BS). Long-term network utilization requires balancing node energy usage with CHs and FCHs. Ant colony optimization dynamically finds the energy-aware route with lower network overhead. The algorithm assesses node energy consumption, network lifespan, Packet Delivery Ratio (PDR), and throughput. For WSNs like EECABCO, ECEEC, and EECRP-BOACOA, simulation outperformed clustering-based routing. Node energy usage (9.53 mJ), PDR (97.5%), and network lifetime (9920 rounds) are WSN performance outcomes for 300 sensors. The proposed system operates with 94.24% accuracy. The Bacterial Foraging Ant Colony Optimization (BFO-ACO) clustering and routing algorithm outperforms parallel research.