Research on optimization deployment of WSNs nodes in barrier environment based on ant colony algorithm

FAN Kuan-gang; YAO Xiao-kang; SU Jian-hua

doi:10.13873/J.1000-9787(2015)05-0029-04

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on optimization deployment of WSNs nodes in barrier environment based on ant colony algorithm

更新时间：2025-09-11

- Research on optimization deployment of WSNs nodes in barrier environment based on ant colony algorithm
- Vol. 34, Issue 5, Pages: 29-32+37(2015)
- 作者机构：
  
  1. 江西理工大学机电工程学院
  2. 中国科学院自动化研究所复杂系统与智能科学国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2015)05-0029-04
  CLC： TN929.5;TP212.9
- Published：2015
- 稿件说明：
移动端阅览
[1]樊宽刚,么晓康,苏建华.基于蚁群算法的WSNs节点有障环境中部署优化研究[J].传感器与微系统,2015,34(05):29-32+37.

FAN Kuan-gang, YAO Xiao-kang, SU Jian-hua. Research on optimization deployment of WSNs nodes in barrier environment based on ant colony algorithm[J]. 2015, 34(5): 29-32+37.
[1]樊宽刚,么晓康,苏建华.基于蚁群算法的WSNs节点有障环境中部署优化研究[J].传感器与微系统,2015,34(05):29-32+37. DOI： 10.13873/J.1000-9787(2015)05-0029-04.

FAN Kuan-gang, YAO Xiao-kang, SU Jian-hua. Research on optimization deployment of WSNs nodes in barrier environment based on ant colony algorithm[J]. 2015, 34(5): 29-32+37. DOI： 10.13873/J.1000-9787(2015)05-0029-04.

摘要

能量限制是无线传感器网络（WSNs）技术中一个关键问题。提出了一种通过无线信道能耗衰减模型利用Dijkstra算法和蚁群算法相结合的WSNs节点静态优化部署策略。通过该策略对两种不同的环境仿真部署验证表明:该策略能更好地优化节点在复杂二维地理环境中的静态部署

并可有效地减少网络的能耗

提高WSNs的使用寿命。

Abstract

Energy constraint is one of key issues for wireless sensor networks（ WSNs）. A strategy for optimizing the placement of static sensor nodes is proposed

and the energy consumption model of wireless channel attenuation is presented for energy efficiency. By this strategy and the model

two simulation experiments using Dijkstra’s algorithm and ant colony algorithm for nodes deployment are executed in the two kinds of different environments

and simulation experiments verify that the strategy can well optimize nodes deployment in complex two-dimensional geographical environment

and can effectively reduce energy consumption and improve service life of WSNs.

关键词

Keywords

references

基于Grid-VFACO的数字化车间WSNs路由算法 [J]. 朱绍文,纪志成,王艳,吴定会. 传感器与微系统 . 2014(01)

大跨桥梁监测无线传感网络节点布置方法研究 [J]. 周广东,李爱群. 振动工程学报 . 2011(04)

大规模农田传感器网络通信能耗模型 [J]. 肖德琴,王景利,罗锡文. 计算机科学 . 2009(08)

层次分簇的无线传感网络多级优化测量 [J]. 王雪,丁梁,王晟,毕道伟. 机械工程学报 . 2009(04)

基于精细化梯度的无线传感器网络汇聚机制及分析 [J]. 朱红松,孙利民,徐勇军,李晓维. 软件学报 . 2007(05)

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots [J]. SLOMAN Aaron. 自动化学报 . 2007(03)

无线通信原理与应用[M]. 电子工业出版社 , (美) 拉帕波特 (Rappaport, 2012

网络信息论[M]. 清华大学出版社 , 樊平毅, 2009

Lifetime maximization in wireless directional sensor network [J] . André Rossi,Alok Singh,Marc Sevaux. European Journal of Operational Research . 2013 (1)

An energy-aware spatio-temporal correlation mechanism to perform efficient data collection in wireless sensor networks [J] . Leandro A. Villas,Azzedine Boukerche,Daniel L. Guidoni,Horacio A.B.F. de Oliveira,Regina Borges de Araujo,Antonio A.F. Loureiro. Computer Communications . 2013 (9)

Views

167

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of improved QGA in WSNs node deployment

Research on improved ACO-based routing for wireless sensor networks

LEACH extremum double cluster head clustering method based on CS algorithm

Research on topology evolution model for WSNs based on weight constraints in mutual selection mechanism

Research on WSNs deployment optimization based on fuzzy PSO algorithm

Related Author

QIAN Cheng

CHEN Shu

WANG Fu-dong

XU Bao-guo

SHANG Xing-hong

QIAN Huan-yan

GAO De-min

WU Hui

Related Institution

School of IOT Engineering, Jiangnan University

School of Computer Science and Technology,Nanjing University of Science and Technology

School of Communication Engineering,Hangzhou Dianzi University

School of Information Engineering,East China Jiaotong University

School of Software,East China Jiaotong University

⁰