Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization

Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization

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,...

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Bibliographic Details
Main Authors: Junguo Zhang, Yutong Lei, Chen Chen, Fantao Lin
Format: Article
Language:English
Published: SAGE Publishing 2016-04-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2016/2046392
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spelling doaj-681d909567cc455fa46c6ce552fa4b3a2020-11-25T03:39:18ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772016-04-011210.1155/2016/2046392Directional Probability Perceived Nodes Deployment Based on Particle Swarm OptimizationJunguo Zhang0Yutong Lei1Chen Chen2Fantao Lin3 School of Technology, Beijing Forestry University, Beijing 100083, China School of Technology, Beijing Forestry University, Beijing 100083, China Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA China Electric Power Research Institute, Beijing 100192, ChinaNode 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.https://doi.org/10.1155/2016/2046392
collection DOAJ
language English
format Article
sources DOAJ
author Junguo Zhang
Yutong Lei
Chen Chen
Fantao Lin
spellingShingle Junguo Zhang
Yutong Lei
Chen Chen
Fantao Lin
Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
International Journal of Distributed Sensor Networks
author_facet Junguo Zhang
Yutong Lei
Chen Chen
Fantao Lin
author_sort Junguo Zhang
title Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
title_short Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
title_full Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
title_fullStr Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
title_full_unstemmed Directional Probability Perceived Nodes Deployment Based on Particle Swarm Optimization
title_sort directional probability perceived nodes deployment based on particle swarm optimization
publisher SAGE Publishing
series International Journal of Distributed Sensor Networks
issn 1550-1477
publishDate 2016-04-01
description 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.
url https://doi.org/10.1155/2016/2046392
work_keys_str_mv AT junguozhang directionalprobabilityperceivednodesdeploymentbasedonparticleswarmoptimization
AT yutonglei directionalprobabilityperceivednodesdeploymentbasedonparticleswarmoptimization
AT chenchen directionalprobabilityperceivednodesdeploymentbasedonparticleswarmoptimization
AT fantaolin directionalprobabilityperceivednodesdeploymentbasedonparticleswarmoptimization
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