Swarm Optimization Algorithm-Enhanced Clustering Techniques for Reliable Wireless Sensor Networks Communication

The Wireless Sensor Networks (WSNs) are ubiquitous in the smart city, industrial surveillance, and environmental research literature but continue to have the fundamental challenges of inconsistent power supply, ineffective data management, and connectivity issues. The clustering of WSNs is a robust approach to control the topology of networks, allocate loads in the similar way, and extend the life of the networks. However, it is NP-hard to find a good cluster and the conventional approaches cannot always provide a good balance between power consumption and network survivability. This study presents a Hybrid Ant Colony Optimization-Particle Swarm Optimization (ACO-PSO) model that not only identifies Cluster Heads (CHs) in an intelligent manner, but also identifies optimal routing paths, and thus guarantees efficient energy management. The proposed ACO-PSO algorithm used very low energy (EC) of 3.920 J and high packet delivery ratio (PDR) of 97.94 when it was used through extensive simulations (50 nodes). The effectiveness of this approach is much higher compared to existing approaches, including ACO, PSO-ECSM, SSA, and MIGJOA. The framework offers a uniform coverage at various magnitude levels of the network, end-to-end latency is minimized substantially, and the scalability and reliability of swarm optimization are demonstrated in prospective resource-constrained wireless sensor network utilizations.