Method for the Optimal Sensor Deployment of WSNs in 3D Terrain Based on the DPSOVF Algorithm

Abstract

Maximizing coverage and maintaining connectivity are two major objectives in designing and deploying wireless sensor networks (WSNs). In this paper, a novel approach is proposed to obtain better sensor deployment in three-dimensional (3D) terrain in terms of coverage and connectivity. The proposed approach is based on a combination of the distributed particle swarm optimization (DPSO) algorithm and a proposed 3D virtual force (VF) algorithm. The communication limit (CL) of the sensor nodes (SNs) is taken into consideration. A heuristic algorithm that is suitable for a limited communication environment is proposed to cluster the SNs. To effectively guide the SN deployment optimization and to speed up convergence, the addition of a VF term in the velocity update equation of each particle is adopted. To improve the efficiency of increase of coverage and connectivity of the WSN, the selected redundant sensor nodes (RSNs) move toward certain selected areas. In addition, measures are taken to guarantee population diversity in the early stage and the convergence speed of the proposed algorithm in a later stage. Finally, to verify the effectiveness of the proposed algorithm, some comparative experiments are performed. The experimental results show that the proposed algorithm performs well against other algorithms in deploying sensors in 3D terrain when considering CL.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

“…About 35% of the reviewed studies worked on an update to swarm intelligence optimization algorithms [ 40 , 44 , 46 ] such as particle swarm optimization (PSO) [ 42 , 58 , 66 , 68 , 69 , 74 , 75 , 76 , 77 , 81 , 88 , 97 , 99 ], ant colony optimization (ACO) [ 33 ], and bee colony optimization (BCO) [ 48 , 65 ], due to their ability to solve complex problems and provide a satisfactory solution in a feasible time [ 90 ]. These algorithms are applied to enhance network performance by combining them with other approaches and then comparing the obtained results with other algorithms, such as the genetic, greedy, and multi-objective evolutionary algorithms.…”

“…This can be done either by proposing new algorithms or updating existing ones or by a combination of two different types of optimization algorithms, such as a hybrid algorithm between classical and meta-heuristic algorithms or meta-heuristic and artificial intelligence algorithms [11]. About 35% of the reviewed studies worked on an update to swarm intelligence optimization algorithms [40,44,46] such as particle swarm optimization (PSO) [42,58,66,68,69,[74][75][76][77]81,88,97,99], ant colony optimization (ACO) [33], and bee colony optimization (BCO) [48,65], due to their ability to solve complex problems and provide a satisfactory solution in a feasible time [90]. These algorithms are applied to enhance network performance by combining them with other approaches and then comparing the obtained results with other algorithms, such as the genetic, greedy, and multi-objective evolutionary algorithms.…”

A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm

“…But, in this type of deployment, there is no guarantee of complete coverage and connectivity. Sensor nodes may cluster in some regions leaving out coverage holes and disconnected networks in other regions [10]. These coverage holes and disconnected networks result in missing out on important events and hence reduces the quality of the network [11].…”

Multiobjective Optimization Strategy of WSN Coverage Based on IPSO-IRCD

“…Many researchers have carried out researches in the field of WSN coverage, and some typical intelligent algorithms are widely used to solve the node coverage problem in WSN, such as simulated annealing algorithm, genetic algorithm, standard particle swarm optimization algorithm (SPSO), and sparrow search algorithm (SSA) [8,9]. For WSN coverage optimization problem in different scenarios, the work in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] adopted the improved intelligent algorithms to optimize the node coverage with the goal of network coverage rate. Aiming to improve the algorithm convergence and maximize the coverage rate, an enhanced sparrow search algorithm (ESSA) for WSN coverage research was proposed in [10], where the convergence factor, Cauchy operator, and cross-border processing method were introduced to modify the basic SSA, so that the performance of the algorithm could be further improved.…”