An intelligent algorithm based optimized clustering method for energy harvesting WSN

Energy-harvesting wireless sensor networks (EH-WSNs) require clustering with precise sensing-awareness and awareness in harvested-energy dynamics and communication costs. This paper introduces MCSOC (Modified Cat-Swarm-Optimization based clustering), an approach with a domain-aware, multi-objective fitness for the choice of cluster-heads that co-optimizes: (i) residual energy and (ii) intra-cluster length; and environmental aware optimization in terms of (iii) inter-cluster transmission cost to the sink, and finally, (iv) distances between EH nodes and the sink. In a first-order radio energy model, with static nodes and a single central sink, MCSOC is evaluated on two deployments (200 × 200 m2 and 500 × 500 m2, respectively, with 200 nodes overall) over an average of 30 runs. We compare with NEHCP, ROTEE, SMEOR, and GAPSO-H on lifetime, throughput, residual energy, and stability. Results demonstrate that our MCSOC achieves longer network lifetime, high throughput, and a higher saving proportion of early dead nodes compared with benchmark methods that consider energy harvesting. As a result, MCSOC over GAPSO-H and SMEOR method, simulation results indicate that MCSOC enhances network performance, stability, and throughput by 42.13%, 45.57%, and 48.48% and 63.13%, 62.2%, and 58.68% respectively. These properties enable MCSOC to be used as a practical long-lifetime sensing in precision agriculture, smart-city environmental monitoring, and industrial health deployment scenarios.