Energy and Distance Aware Multi-Objective Firebug Swarm Optimization Based Clustering and Routing in Wireless Sensor Networks

Abstract

In recent years, Wireless Sensor Networks (WSNs) have received increased international attention due to advancements in the communication, electronics, and information fields. These sensor networks integrate a huge number of sensor nodes to track rapidly changing physical events. The key improvement is that these nodes can be easily positioned in any environment due to their small size. Therefore, maintaining network connectivity is crucial in WSNs. If some nodes become unavailable, the connectivity of the routing path fails, resulting in significant packet loss in the WSN. Therefore, many types of research in WSNs have focused on energy efficiency, where energy consumption is minimized to improve the network lifetime. Here, Energy and Distance-aware Multi-Objective Firebug Swarm Optimization (ED-MOFSO) is projected to achieve an energy efficient process. Furthermore, ED-MOFSO minimizes delays to enhance performance measures. From the overall simulation, it shows that ED-MOFSO achieves improved metrics, including residual energy (14.29 J), delay (11.1 ms), packet delivery ratio (0.994), routing overhead (0.10), and throughput (1.233 Mbps) when compared to conventional Elephant Herding Optimization (EHO) greedy and Ant Colony Optimization Integrated Glow-worm Swarm Optimization (ACI-GSO).