Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks

Zhang, Guanglin; You, Shan; Ren, Jiajie; Li, Demin; Wang, Lin

doi:10.3390/s16122183

Cited by 30 publications

(20 citation statements)

References 16 publications

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“…Due to the typically large search space and the density of local extrema of the cost function, area coverage problems challenge traditional optimization schemes. Methods found in the literature range for instance from connected subgraphs [17], a generalized virtual field approach [18], and repulsive potential fields [19] to Voronoi diagrams [14]. To address the DSN placement problem in a numerical way, genetic algorithms (GA) [20] have emerged as a particularly useful tool [12], [13].…”

Section: Related Workmentioning

confidence: 99%

Optimized sensor placement for dependable roadside infrastructures

Geißler

Graefe²

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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We present a multi-stage optimization method for efficient sensor deployment in traffic surveillance scenarios. Based on a genetic optimization scheme, our algorithm places an optimal number of roadside sensors to obtain full road coverage in the presence of obstacles and dynamic occlusions. The efficiency of the procedure is demonstrated for selected, realistic road sections. Our analysis helps to leverage the economic feasibility of distributed infrastructure sensor networks with high perception quality.

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Section: Related Workmentioning

confidence: 99%

Optimized sensor placement for dependable roadside infrastructures

Geißler

Graefe²

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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“…A Voronoi diagram is also known as a Thiessen polygon or Dirichlet tessellation, which offers an effective solution for partitioning a plane into a few regions [ 38 ]. It has been used in various fields like local coverage optimization of wireless sensor network [ 39 ], interferometric synthetic aperture radar (InSAR) fine registration [ 40 ], and optimal allocation of dynamic reactive power sources [ 41 ]. The general idea of the Voronoi diagram is to partition the plane into regions according to the locations of a set of sites.…”

Section: Proposed Localization Systemmentioning

confidence: 99%

Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN

Sun

Meng

et al. 2018

Sensors

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In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance.

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“…So the optimization of large-scale sensor deployment is limited. Zhang et al [11] and Sung and Yang [12] exploit the Voronoi diagram to transform the network area coverage problem into cell coverage problems, which reduces the variable number and the calculation complexity in local area, but it is based on a relatively uniform distribution of the sensors at the initial moment, which is difficult to realize because of the random distribution by aircraft.…”

Section: Introductionmentioning

confidence: 99%

Novel RPSO Based Strategy for Optimizing the Placement and Charging of a Large-Scale Camera Network in Proximity Service

Wang

Zhang

et al. 2019

IEEE Access

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Sensor placement and charging in proximity service are becoming critical issues. In this paper, novel methods are proposed to address the coverage optimization problem and charging problem of camera networks with mobile nodes. Because the sensing angle of a camera is limited, the placement of a camera network is more complicated compared with an omnidirectional sensor network. Aiming at finding the best positions and working angles of all camera nodes in the coverage optimization problem, we propose a novel resampling particle swarm optimization (RPSO)-based process. First, the RPSO is introduced, which has better precision and efficiency than the PSO and the genetic algorithm. Second, because of the huge number of variables in the problem, we propose a hierarchical strategy, which divides the whole optimization process into several sub-processes in consideration with overlay redundancy and coverage area of all camera nodes, reducing the variables at each step. In this way, the precision of optimization results could be improved significantly. After deploying the camera network, we consider charging it to extend its lifetime. Fuzzy c-means, a fuzzy clustering algorithm, is used to classify the sensor nodes according to their positions. Then, a charging station is set in each cluster, the position of which is determined based on the RPSO to minimize the distance from it to all of the sensor nodes in the cluster. So, the charging efficiency is improved than before. The experimental results show that the proposed methods achieve the goal of maximizing the coverage rate and improving the charging efficiency of the camera network in comparison with the traditional methods. INDEX TERMS Smart world, sensor placement and charging, hierarchical strategy, resampling particle swarm optimization, fuzzy clustering.

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Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks

Cited by 30 publications

References 16 publications

Optimized sensor placement for dependable roadside infrastructures

Optimized sensor placement for dependable roadside infrastructures

Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN

Novel RPSO Based Strategy for Optimizing the Placement and Charging of a Large-Scale Camera Network in Proximity Service

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