Design and development of mobile anchor assisted node localization strategy using a Hybrid Electric-Coyote Optimization

As Wireless Sensor Networks (WSNs) offer essential support for several location-aware applications and protocols, localization is one of the principal methodologies in WSN. In the literature, the mobile anchor node is preferred over the static anchor node to handle the high overhead issue caused by the larger number of anchor nodes. In recent years, the Mobile Anchor Node Assisted Localization (MANAL) problem has received significant attention in various research works, and path-planning techniques for localization using fewer anchor nodes have been implemented. However, recent MANAL breakthroughs in WSNs have provided ample research opportunities. The mobile anchor node is used in this paper to solve the node localization problem in WSN. The locations of the target nodes are determined using a Hybrid Electric-Coyote Optimization Algorithm (HE-COA), the mobile anchor node, and the virtual anchor nodes. The objective of the proposed localization is to minimize the distance error between actual node coordinates and estimated node coordinates. Various path trajectories are investigated in simulation to demonstrate the efficacy of the suggested work. The simulation results show that the proposed algorithm can reduce the localization error compared to the conventional models.