Coverage Improvement Strategy Based on Voronoi for Directional Sensor Networks

You, Shan; Zhang, Guanglin; Li, Demin

doi:10.1007/978-3-319-52730-7_25

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…According to different perspectives of algorithm optimization, it can be divided into distributed and centralized. Besides, there are also some DSNs that consider multiple coverage [ 11 ], probability coverage [ 12 , 13 , 14 ], graph theory [ 15 , 16 , 17 ], and other factors to achieve area coverage optimization under specific scenarios.…”

Section: Related Workmentioning

confidence: 99%

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Cheng

Huangfu

2021

Sensors

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With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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Section: Related Workmentioning

confidence: 99%

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Cheng

Huangfu

2021

Sensors

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“…In [5,6], Voronoi tessellation is adopted to tackle the coverage problem. In our previous work [7], we utilize the combination of node’s mobility and motility to increase coverage.…”

Section: Introductionmentioning

confidence: 99%

Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks

Zhang

You

Ren

et al. 2016

Sensors

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In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), Rotate Working Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio.

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Coverage Improvement Strategy Based on Voronoi for Directional Sensor Networks

Cited by 2 publications

References 8 publications

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks

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