Metaheuristic-Optimized Clustering for Improving QoS in IoT-Enabled Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have grown significantly in recent years. The initial step in this approach was the deployment of smaller WSNs; later, larger WSNs based on the Internet of Things (IoT) with an increased focus on energy efficiency were deployed. WSNs can be made more energy efficient by using network clustering. In networks, clustering involves dividing nodes into smaller groups and then choosing Cluster Heads (CHs) from those groups. Normal nodes in a clustered WSN are responsible for identifying their surroundings and transmitting that data to the CH, which collects the data and sends it to the base station. Some of the benefits of node clustering in WSNs include reduced routing latency and greater energy efficiency. This study aims to improve the Quality of Service (QoS) of WSN by using metaheuristic optimization. Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Cat Swarm Optimization (CSO) are the optimization techniques used for the best CH selection. The NS-2.34 software is used for the experiments. Simulation is used to test clustering technique optimization approaches under a variety of nodes. This study compares three optimization methods based on throughput, energy efficiency, and E2E delay. Simulation data indicates that the CSO outperforms the other two techniques.