Abstract
Wireless sensor networks (WSNs) have fabulous attributes to collect data by sensing the surrounding environment. WSNs have a large number of applications that are facing challenges of routing, security, deployment, prolonged lifetime, data computation, and localization. To achieve the high-level performance of WSNs, many researchers have proposed various computational Intelligence (CI) based algorithms for the above-mentioned challenges. The procedure to determine the location of the target node is called node localization. It is easy to determine the coordinates of static nodes accurately but challenging task for the mobile nodes. Localization accuracy directly affects the WSN’s performance. In this paper, a range-based and distributed method are proposed by using the application of the Salp Swarm Algorithm (SSA), and the simulation results are compared with existing approaches such as Particle swarm Optimization (PSO) and H-best Particle Swarm Optimization (HPSO). In this paper, a single mobile anchor node as a reference node traversing the entire network in the Hilbert path and localize the mobile target nodes that are randomly deployed in the networking area. The primary goal behind selecting the Hilbert trajectory is to reduce the issue of LoS. The simulation results show that the proposed method has low localization error and an approximate double number of localized nodes with less computing time as compared to existing methods.
Providing an Efficient Model for Wireless Sensor Networks Using the Scenario of the Variable Sink Counts Based on the Particle Swarm Algorithm
