面向目标覆盖的无线传感器网络确定性部署方法

何欣 1; 2; 桂小林 1; 3; 安健 1

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面向目标覆盖的无线传感器网络确定性部署方法

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- 面向目标覆盖的无线传感器网络确定性部署方法
- A Deterministic Deployment Approach of Nodes in Wireless Sensor Networks for Target Coverage
- 西安交通大学学报 2010年44卷第6期页码：6-9+15
- 作者机构：
  
  1. 西安交通大学计算机科学与技术系,西安,710049
  2. 河南大学计算中心,河南,开封,475001
- 作者简介：
- 基金信息：
  
  : 2009-10-20. 作者简介: 何欣(1974-),男,博士生;桂小林(联系人),男,教授,博士生导师. 基金项目: 国家高技术研究发展计划资助项目(2008AA01Z410);国家自然科学基金资助项目(60873071);陕西省科技攻关资助项目(2007K04-05);西安交通大学重点实验室青年学术骨干培植项目
- DOI：
  中图分类号： TN919.8
- 网络首发：2010-06-10，
  
  纸质出版：2010
- 稿件说明：
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何欣 1, 2, 桂小林 1, 等. 面向目标覆盖的无线传感器网络确定性部署方法[J]. 西安交通大学学报, 2010,44(6):6-9+15.

A Deterministic Deployment Approach of Nodes in Wireless Sensor Networks for Target Coverage[J]. 2010, 44(6): 6-9+15.
何欣 1, 2, 桂小林 1, 等. 面向目标覆盖的无线传感器网络确定性部署方法[J]. 西安交通大学学报, 2010,44(6):6-9+15. DOI：

A Deterministic Deployment Approach of Nodes in Wireless Sensor Networks for Target Coverage[J]. 2010, 44(6): 6-9+15. DOI：

摘要

针对无线传感器网络中随机部署节点集划分法不能保证离散目标点优化部署的问题

利用目标点最多层交叠域及遗传算法设计了一种面向目标覆盖的最优确定性部署方法.该方法通过目标点最多层交叠域寻找监测目标点集的传感器节点候选位置

基于候选位置点并利用遗传算法找出实现目标监测的最少节点数及节点位置.所提算法中候选位置点的选取简化了遗传算法中的编码工作

且与适应度函数相结合加速了算法的收敛

而遗传算法提供了最佳位置点的寻求方式.仿真试验表明

所提方法在满足用户感知需求的基础上具有较少的部署节点数

通常被控制在目标点个数的30%以内

极大地降低了网络部署成本

实现了无线传感器网络空间资源的优化分配.

Abstract

In wireless sensor networks

since the existing method of dividing sensors based on the random deployment of nodes can not guarantee the optimal deployment to target coverage

an optimal deterministic deployment approach of sensor nodes is proposed by using the maximum multi-overlapping domains of target points and the genetic algorithm. Candidate positions where nodes will be placed to cover the target set are calculated using the concept of the maximum multi-overlapping domains of target points

and the genetic algorithm is used to find the least number of nodes to cover the target set and the optimal positions of these nodes from the candidate node positions. The determination of candidate positions simplifies the coding of the genetic algorithm

and accelerates the convergence of the algorithm by combining the effective fitness function. The genetic algorithm provides a way to find the optimal positions. Simulation results show that the proposed approach uses the least number of nodes for deployment

which is usually less than 30% of the number of target points

and guarantees users' sense demand

and that the network deployment cost is significantly reduced. The optimal allocation of space resources is realized in wireless sensor networks.

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references

BALISTER P, KUMAR S R. Deterministic deployment of sensors in the presence of failures and placement errors [C]∥ Proceeding of the IEEE 28th Conference on Computer Communications. Los Alamitos, CA, USA: IEEE Computer Society, 2009:2896-2900.

李善仓,张德运.一种高效节能分布式传感器网络覆盖协议[J].西安交通大学学报, 2007, 41(10):1132-1136.

LI Shancang, ZHANG Deyun. Distributed high efficiency and energy-saving coverage protocol of wireless sensor networks [J]. Journal of Xi'an Jiaotong University, 2007, 41(10):1132-1136.

GALLAIS A, CARLE J, SIMPLOT-RYL D, et al. Localized sensor area coverage with low communication overhead [J].IEEE Transactions on Mobile Computing, 2008,7(5):661-672.

WANG B, KEE C C, SRINIVASAN V, et al. Information coverage in randomly deployed wireless sensor networks [J]. IEEE Transactions on Wireless Communications, 2007, 6(8):2994-3004.

WANG Y C, TSENG Y C. Distributed deployment schemes for mobile wireless sensor networks to ensure multilevel coverage [J]. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(9):1280-1294.

XU X, SAHNI S. Approximation algorithms for sensor deployment [J]. IEEE Transactions on Computers, 2007, 56(12):1681-1695.

HE Xin, YANG Hua, GUI Xiaolin. The maximum coverage set calculated algorithm for WSN area coverage [J]. Journal of Networks, 2010, 5(6):650-657.

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