Method for Optimal Sensor Deployment on 3D Terrains Utilizing a Steady State Genetic Algorithm with a Guided Walk Mutation Operator Based on the Wavelet Transform

Abstract

One of the most critical issues of Wireless Sensor Networks (WSNs) is the deployment of a limited number of sensors in order to achieve maximum coverage on a terrain. The optimal sensor deployment which enables one to minimize the consumed energy, communication time and manpower for the maintenance of the network has attracted interest with the increased number of studies conducted on the subject in the last decade. Most of the studies in the literature today are proposed for two dimensional (2D) surfaces; however, real world sensor deployments often arise on three dimensional (3D) environments. In this paper, a guided wavelet transform (WT) based deployment strategy (WTDS) for 3D terrains, in which the sensor movements are carried out within the mutation phase of the genetic algorithms (GAs) is proposed. The proposed algorithm aims to maximize the Quality of Coverage (QoC) of a WSN via deploying a limited number of sensors on a 3D surface by utilizing a probabilistic sensing model and the Bresenham's line of sight (LOS) algorithm. In addition, the method followed in this paper is novel to the literature and the performance of the proposed algorithm is compared with the Delaunay Triangulation (DT) method as well as a standard genetic algorithm based method and the results reveal that the proposed method is a more powerful and more successful method for sensor deployment on 3D terrains.

On Coverage of 3D Terrains by Wireless Sensor Networks

“…The sensing range of s j is a reference distance r s from which we can pronounce on the coverage of p i by s j in function of their distance d(p i , s j ) [5], [6], [7], [8]. The influence of this factor on C(p i , s j ), modeled by the function [9], [10], [7], where C(p i , s j ) degrades with respect to d(p i , s j ), and it becomes null when the point p i is outside the sensing range of s j ; (iii) Hybrid impact [11], [12], [13], by considering that s j has two sensing ranges, the first is "with certitude", noted r 1 , and the second is "without certitude", noted r 2 , where r 2 > r 1 . Thus, C(p i , s j ) is constant with respect to d(p i , s j ), as long as p i is in the sensing range "with certitude" of s j ; it is null when p i is outside the sensing range "without certitude" of s j , and it degrades with [14], [6], [15], which means that…”

The 3D indoor deployment in DL-IoT with experimental validation using a particle swarm algorithm based on the dialects of songs

“…However, this study lacks a mathematical modeling that explains the details of the problem. Unaldi et al [6] propose a GA based on a guided wavelet transform and a random mutation for the probabilistic deployment of WSN nodes in the context of 3D terrains. This study aims to minimize the number of sensors and maximize the quality of coverage.…”

Optimization for the sensor placement problem in 3D environments

“…Topcuoglu et al [11] proposed a new formulation for the deployment of sensors in 3D environments. Unaldi et al [12] proposed an algorithm based on a probabilistic sensing model, the Bresenham's line of sight (LoS) algorithm and a guided wavelet transform (WT) in which the sensor movements are carried out within the mutation phase of the genetic algorithms. Zhao et al [18] proposed a new coverage model called surface coverage in which the targeted Field of Interest is a complex surface in 3D space and sensors can be deployed only on the surface.…”

“…This section presents the optimization method to determine the optimized solutions of the 3D sensing model (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12) by Particle Swarm Optimization (PSO) [7]. PSO is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.…”