Application of a multi-objective coverage optimization strategy in WSNs

CHEN Shu; QIAN Cheng

doi:10.13873/J.1000-9787(2014)10-0151-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Application of a multi-objective coverage optimization strategy in WSNs

更新时间：2025-09-11

- Application of a multi-objective coverage optimization strategy in WSNs
- Vol. 33, Issue 10, Pages: 151-154(2014)
- 作者机构：
  
  江南大学物联网工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2014)10-0151-04
  CLC： TP18;TN915.04;TP212.9
- Published：2014
- 稿件说明：
移动端阅览
[1]陈树,钱成.一种多目标的覆盖优化策略在WSNs中的应用[J].传感器与微系统,2014,33(10):151-154.

CHEN Shu, QIAN Cheng. Application of a multi-objective coverage optimization strategy in WSNs[J]. 2014, 33(10): 151-154.
[1]陈树,钱成.一种多目标的覆盖优化策略在WSNs中的应用[J].传感器与微系统,2014,33(10):151-154. DOI： 10.13873/J.1000-9787(2014)10-0151-04.

CHEN Shu, QIAN Cheng. Application of a multi-objective coverage optimization strategy in WSNs[J]. 2014, 33(10): 151-154. DOI： 10.13873/J.1000-9787(2014)10-0151-04.

摘要

针对目前无线传感器网络（WSNs）能量均衡覆盖策略大都基于节点静态感知能耗的不足

提出一种基于节点的动态能耗和网络覆盖率的多目标覆盖优化策略。该优化覆盖策略将动态路由协议引入到覆盖控制优化中

计算覆盖区域在不同节点分布下的动态通信能耗和网络的剩余能量

再结合区域覆盖率构成对覆盖和能量综合指数评价的优化函数。最后利用改进差分进化算法和差分进化算法对优化函数进行仿真

并利用覆盖结果验证策略的有效性。仿真结果表明:提出的覆盖优化策略既能使网络达到较高覆盖率

同时又能保证网络的能耗动态均衡

并将改进差分进化算法与常规差分进化算法比较

结果表明:前者克服了早熟现象

覆盖和能量的综合优化函数值更高

达到了6.184。

Abstract

Aiming at most energy equilibrium coverage strategy is based on insufficiency of energy consumption of static perception of node mostly in WSNs

a multi-target coverage optimization strategy is brought forward based on dynamic energy consumption and network coverage rate based on node.The strategy introduces dynamic routing protocol to coverage control optimization compute the dynamic energy consumption and the residual energy of the network

establish optimal function which evaiuate on coverage and energy comprehensive index combined with area coverage rate.Improved differential evolution algorithm and differential evolution algorithm are used for simulation and use result of coverage result to verify effectiveness of strategy.Simulation result shows that the coverage optimization strategy can achieve high coverage rate and guarantee dynamic balance of network energy consumption at the same time.In addition

in comparison with the improved differential evolution algorithm and the conventional differential evolution algorithm

the improved differential evolution algorithm overcomes prematurity value of comprehensive optimization of coverage and energy is more higher

which reaches 6.184 function.

关键词

Keywords

references

基于差分进化的鱼群算法及其函数优化应用 [J]. 张大斌,杨添柔,温梅,孙莹,周茜. 计算机工程 . 2013(05)

受启发的人工蜂群算法在全局优化问题中的应用 [J]. 高卫峰,刘三阳,黄玲玲. 电子学报 . 2012(12)

具有人工蜂群搜索策略的差分进化算法 [J]. 黄玲玲,刘三阳,高卫峰. 控制与决策 . 2012(11)

基于人工鱼群算法的无线传感器网络覆盖优化 [J]. 黄瑜岳,李克清. 计算机应用研究 . 2013(02)

群混合算法应用于异构传感网络节点的优化部署 [J]. 张斌,毛剑琳,李海平,陈波. 计算机应用 . 2012(05)

基于混沌粒子群算法的无线传感器网络覆盖优化 [J]. 刘维亭,范洲远. 计算机应用 . 2011(02)

基于差分进化算法的移动传感器网络节点的分布优化 [J]. 靳立忠,常桂然,贾杰. 控制与决策 . 2010(12)

确定性空间的无线传感器网络节点部署策略研究 [J]. 张云洲,吴成东,程龙,纪鹏. 控制与决策 . 2010(11)

无线传感器网络的多目标覆盖控制策略 [J]. 梁天,周晖,谢静,王坤赤. 传感技术学报 . 2010(07)

差分进化算法及其应用 [D]. 余兵. 西安工程大学 2007

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Node energy balanced coverage strategy in WSNs based on improved PSO algorithm

Improved routing algorithm for WSNs

WSNs coverage optimization based on CVT model

Design and realization of WSNs routing protocol based on virtual gravity

Design of hospital environment monitoring system based on WSNs

Related Author

XU Yiming

PENG Yong

ZHENG Chuhong

LIAO Yi

LI Li

LI Xue-mei

LI Xiu-ying

XIANG Xin-yi

Related Institution

School of Internet of Things Engineering,Jiangnan University

Engineering Research Center of Internet of Things Technology Applications,Ministry of Education,Jiangnan University

Department of Electronic Engineering,Beijing Electronic Science and Technology Institute

College of Information Science and Engineering,Ningbo University

College of Computer Science and Technology,Zhejiang University of Technology

⁰