Restoring coverage area for WSN through simulated annealing

Abstract

PurposeContinuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.Design/methodology/approachThe authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.FindingsThe experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.Practical implicationsThe proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.Originality/valueThe paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

“…A wide variety of meta-heuristic methods have been applied to the placement problem, ranging from the genetic algorithm (GA) [23], evolution algorithm with specialized operators [24], particle swarm optimization algorithm (PSO) [25], simulated annealing algorithm (SA) [12][13][14][15], virtual force algorithm (VF) [26], and the virtual force oriented particles algorithm [27]. Other algorithms are analyzed in [16] like the artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), and PSO for the sensor deployment problem with the target coverage.…”

“…In addition, to calculate the node placement, composite factors, which are combinations of environmental factors, should be considered such as the relationship between the sensing range, the communication range, and the node deployment type [30]. Our approach is based on the SA algorithm, because, based on [12][13][14][15], it provides a good and implementable response for network design and better energy efficiency by organizing the sensor nodes. Generally, this method is used to determine the initial number of sensor nodes, and based on the results obtained by the sensor deployment method, we can choose to increase or to decrease the number of nodes to deploy.…”

“…Several approaches were developed to improve coverage and connectivity using the SA algorithm [12][13][14][15]. In this article, we propose a hybrid algorithm based on the Gradient method and the SA algorithm to solve the WSN placement problem while defining the optimal number of nodes to deploy, to ensure the required coverage and connectivity in the case of a deterministic deployment, where all sensor nodes positions are well controlled.…”

Soft computing tool for restoring failures within wireless sensor networks

“…The algorithm generated a list of backup base stations for each critical node and upon failure detection, the backup BS initiated the recovery process. The restoration algorithm presented in [9] modeled communication restoration as a variant of the Steiner tree formation problem, which was solved using novel heuristics. In another work on communication restoration, the authors in [10] utilized distributed relay node positioning approach to address the communication issues.…”