Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network

Hao, Zhanjun; Qu, Nanjiang; Dang, Xiaochao; Hou, Jiaojiao

doi:10.1177/1550147719869877

Cited by 11 publications

(9 citation statements)

References 29 publications

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“…In [18], the author presented a node optimization coverage method under a link model in the passive monitoring system of a three-dimensional WSN; the particle swarm optimization (PSO) algorithm, which integrates the concept of simulated annealing, was proposed, and the proposed method was found to improve the network coverage, converge quickly, and reduce the network energy consumption. Massive sensor nodes are randomly deployed and remain static after deployment, which will usually cause coverage redundancy or coverage holes.…”

Section: Related Workmentioning

confidence: 99%

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

et al. 2021

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To overcome the problems of coverage blind areas and coverage redundancy when sensor nodes are deployed randomly in heterogeneous wireless sensor networks (HWSNs). An optimal coverage method for HWSNs based on an improved social spider optimization (SSO) algorithm is proposed, which can reduce the energy consumption and improve the network coverage. First, a mathematical model of HWSN coverage is established, which is a complex combinatorial optimization problem. To improve the global convergence speed of the proposed algorithm, a chaotic initialization method is used to generate the initial population. In addition, the SSO algorithm has a poor convergence speed and search ability, which is enhanced by improving the neighborhood search, global search, and matching radius. In the iterative optimization process, the optimal solution is ultimately obtained by simulating the movement law of the spider colony, i.e., according to the cooperation, mutual attraction, and mating process of female and male spiders. An improved SSO algorithm based on chaos, namely the CSSO algorithm, is proposed to apply to the optimal deployment of sensory nodes in HWSNs. On this basis, the optimization goals are to improve the network coverage and reduce network costs. The optimal deployment plan of nodes is searched via the proposed CSSO algorithm, which effectively prevents coverage blind spots and coverage redundancy in the network.

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Section: Related Workmentioning

confidence: 99%

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

et al. 2021

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“…Although the two-phase scheme improves coverage efficiency and energy supply efficiency, it has poor convergence performance. In addition, a method of node optimization coverage for passive monitoring system of 3D-WSN based on a link model is proposed in [15]. The method constructs a three-dimensional WSN link coverage model based on a wireless link-aware area and uses a network coverage based on a cube to describe the network's service quality.…”

Section: Related Workmentioning

confidence: 99%

“…To better understand the crossover process, we can give an example. Before crossover, two individuals can be shown as equations (15) and (16). The crosspoint q can be 1 to 3; if we take q = 2, then the two individuals after crossover can be expressed as equations ( 17) and (18).…”

Section: Before Mutationmentioning

confidence: 99%

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

2021

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In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.

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“…Particle swarm optimization (PSO) [20] is widely used in the process of multi-modal optimization because of its simple parameters, strong optimization ability, fast convergence speed and low time complexity [21]. The particle swarm optimization (PSO) has been used and improved by a number of surveys [22][23][24] to enhance cover effect in WSNs. The authors in [25] combine PSO with Voronoi to optimize the coverage enhancement of WSN, PSO is used to find the best deployment location, and Voronoi is responsible for evaluating the excellence of the scheme.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

Zhao

Cui

Guo

et al. 2020

Sensors

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Sensor nodes perform missions based on the effectual invariable coverage of events, and it is commonly guaranteed by the determinate deployment for sensor nodes who deviate from the optimum site frequently. To reach the optimal coverage effect with the lowest costs is a primary goal of wireless sensor networks. In this paper, by splicing the sensing area optimally with cellular grids, the best deployment location for sensors and the required minimum number of them are revealed. The optimization problem of coverage rate and energy consumption is converted into a task assignment problem, and a dynamic partition algorithm for cellular grids is also proposed to improve the coverage effect when the number of sensors is variable. Furthermore, on the basis of solving the multi-objective problem of reducing and balancing the energy cost of sensors, the vampire bat optimizer is improved by introducing virtual bats and virtual preys, and finally solves the asymmetric assignment problem once the number of cellular grids is not equal to that of sensors. Simulation results indicate that the residual energy of sensors during redeployment is balanced notably by our strategy when compared to three other popular coverage-enhancement algorithms. Additionally, the total energy cost of sensor nodes and coverage rate can be optimized, and it also has a superior robustness when the number of nodes changes.

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Node optimization coverage method under link model in passive monitoring system of three-dimensional wireless sensor network

Cited by 11 publications

References 29 publications

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks

An Energy-Efficient Coverage Enhancement Strategy for Wireless Sensor Networks Based on a Dynamic Partition Algorithm for Cellular Grids and an Improved Vampire Bat Optimizer

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