Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Abstract

Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution. INDEX TERMS Wireless sensor network, three-dimensional surface deployment, perceived blind zone, grid and integration, optimization, grey wolf optimizer, inner-layer encircle.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem

“…In order to verify the applicability and reliability of the AMRFOCS algorithm when deploying 3D wireless sensor networks, two 3D surfaces with different complexities are deployed. At the same time, they are compared with four 3D deployment methods optimized by metaheuristic algorithms, namely MRFO in [ 18 ], GWO in [ 58 ], WOA in [ 16 ] and PSO in [ 59 ]. Considering the fairness of the comparison, the parameter setting refers to the literature in the same field: the population size is 50 and the maximum number of iterations is 300.…”

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

“…• Homogeneous obstacles: Described as opaque objects [46]- [51] that completely hinder the signal transmission. Hence, the deployment solution avoids positioning sensors in the vicinity of obstacles to maximize coverage even further.…”

“…Initialization of parameters: a, A and C Assess the fitness value for each grey wolf X α = the best candidate X β = the second best candidate X δ = the third best candidate while Termination condition is not satisfied do for Each grey wolf do Update position end for Update parameters a, A and C Assess the fitness value for each grey wolf Update X ω , X β , X δ end while Return X α algorithm was applied in several works to deal with the sensors deployment [51], [136]- [138]. Authors in [51] developed an enhanced version of the GWO algorithm to deploy WSN in a 3D environment with the objective of coverage maximization under the connectivity constraint. For the first enhancement, the authors used the Tent map that generates chaotic research sequences, increasing population diversity and promoting algorithm exploration to escape the local optima.…”

Multistrategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization

“…However, the proposed method for judging the 3D perception blind area ignored the limitations where both sensing nodes and monitoring points are located on the terrain surface. Wang and Xie [37] proposed a more comprehensive method for determining the 3D surface perception blind area based on the mentioned limitations of perceptual blind area determination. They also innovatively enhanced the grid method commonly used in 2D WSN coverage to make it applicable to the coverage calculation of 3D surfaces.…”

“…Besides, it is not also necessary to do this division. A surface division based on the grid method was proposed [37]. The method gave a reasonable error condition for the number of grid divisions.…”