A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes' optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets.

Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

“…Assuming that the actual location of the prey is unknown to individual grey wolves during the hunting process, the wolves with the highest to lowest ranks of  ,  and  are closest to the prey, so the wolves in the  layer can surround the prey according to the positions of the wolves in the  ,  and  layers, and keep approaching the prey and finally find the prey.  The distances between the wolves in the, and    layers are shown in equation (5).…”

“…So far, in order to improve the coverage effect of wireless sensor networks, a variety of different types of intelligent algorithms have been used as optimization tools. Among them, the particle swarm optimization algorithm, as an algorithm with strong practicability and robustness, has also received many adaptive improvements in application [4][5]. However, particle swarm optimization (PSO) itself has certain flaws.…”