Coverage-Enhancing Algorithm for Directional Sensor Networks

Tao, Dan; Ma, Huadóng; Liu, Liang

doi:10.1007/11943952_22

Cited by 72 publications

(51 citation statements)

References 12 publications

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“…We set the threshold , and when > , the target is perceived, whereas the target is perceived loss. Here is set to the initial probability 0 [23,24]:…”

Section: Coverage Modelmentioning

confidence: 99%

Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization

Zhang

Lei

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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Node deployment is the key problem of wireless sensor network technology. For a directional sensor network, the perceived probability model reflects the quality of the network. The problem of the probability node deployment is too little of the distribution of the nodes asymmetrical. In this paper, we study the probability model of directional perceived nodes and propose an improved deterministic deployment algorithm based on particle swarm optimization to increase perceived probability. By analyzing the coverage probability of the monitoring area with different deployment models to obtain more serviceable environmental data of the monitoring areas, experimental results demonstrate that, compared with random deployment, sixteen percent is improved by the proposed algorithm.

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“…We set the threshold , and when > , the target is perceived, whereas the target is perceived loss. Here is set to the initial probability 0 [23,24]:…”

Section: Coverage Modelmentioning

confidence: 99%

Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization

Zhang

Lei

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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“…If the relationship between the nodes meet conditions in (8) and (9), there will exist repulsion between nodes, and it will be defined as…”

Section: The Study Of the Visual Forcementioning

confidence: 99%

Probability Model Based Coverage-Enhancing Algorithm for WSNs of Nodes' Adjustable Movement Pattern

Huang

Sun

et al. 2012

International Journal of Distributed Sensor Networks

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Aimed at the defects of current coverage-enhancing algorithms, firstly the probability model is introduced into the coverageenhancing algorithm of the directional sensor network based on the virtual potential field, and the probability model would have an impact on the location of the centroid. Secondly, to the boundary coverage, through the simulation of typical algorithms which consider the boundary repulsion, it finds that the coverage rates of the corners are much lower than the overall coverage. To add a vertex force on the corner is essential and to some extent would reduce the gap between them. Finally, based on the random deployment of sensor nodes, a novel algorithm is proposed which determines whether the sensor node rotates or moves along fixed direction according to the coverage effect, named as PRMCA (probability model based rotate or move along fixed direction coverage-enhancing algorithm). A set of simulation experiments verify the performance of the proposed algorithm.

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“…The authors of [24] focused on the placement of camera sensors with respect to maximizing coverage or achieving coverage at a certain resolution. In [21], [22], we extended the directional sensing model for camera sensor by taking the rotatable direction into account, and then we proposed two effective coverage enhancement algorithms by using convex hull theory and virtual potential field theory, respectively.…”

Section: Related Workmentioning

confidence: 99%

Localization-Oriented Coverage in Wireless Camera Sensor Networks

Liu

Zhang

2011

IEEE Trans. Wireless Commun.

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Abstract-In this paper, we investigate the coverage problem from the perspective of target localization for wireless camera sensor networks. We first propose a novel localization-oriented sensing model based on the perspective projection of camera sensors. Based on the sensing model, we propose a new notion of coverage, Localization-oriented coverage (L-coverage for short), by using Bayesian estimation theory. Furthermore, we analyze the relationship between the density of camera sensors and the L-coverage probability under random deployment where camera sensors are deployed according to a 2-dimensional Poisson process. According to the relationship between the density of camera sensors and the L-coverage probability, we derive the density requirements for an expected L-coverage probability. We validate and evaluate our proposed models and schemes by simulations.

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Coverage-Enhancing Algorithm for Directional Sensor Networks

Cited by 72 publications

References 12 publications

Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization

Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization

Probability Model Based Coverage-Enhancing Algorithm for WSNs of Nodes' Adjustable Movement Pattern

Localization-Oriented Coverage in Wireless Camera Sensor Networks

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