Node deployment strategy of multi-objective optimization for heterogeneous wireless sensor networks

JI Wen-juan; SHI Wei-ren; LI Ming; LI Man

doi:10.13873/j.1000-97872012.03.020

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Node deployment strategy of multi-objective optimization for heterogeneous wireless sensor networks

更新时间：2025-09-11

- Node deployment strategy of multi-objective optimization for heterogeneous wireless sensor networks
- Vol. 31, Issue 3, Pages: 29-31+35(2012)
- 作者机构：
  
  重庆大学自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.03.020
  CLC： TN929.5;TP212.9
- Published：2012
- 稿件说明：
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[1]冀文娟,石为人,李明,李曼.异构无线传感器网络中多目标优化节点部署策略[J].传感器与微系统,2012,31(03):29-31+35.

JI Wen-juan, SHI Wei-ren, LI Ming, et al. Node deployment strategy of multi-objective optimization for heterogeneous wireless sensor networks[J]. 2012, 31(3): 29-31+35.
[1]冀文娟,石为人,李明,李曼.异构无线传感器网络中多目标优化节点部署策略[J].传感器与微系统,2012,31(03):29-31+35. DOI： 10.13873/j.1000-97872012.03.020.

JI Wen-juan, SHI Wei-ren, LI Ming, et al. Node deployment strategy of multi-objective optimization for heterogeneous wireless sensor networks[J]. 2012, 31(3): 29-31+35. DOI： 10.13873/j.1000-97872012.03.020.

摘要

针对异构无线传感器网络节点高密度部署和事件发生存在"热点区域"问题

以区域覆盖率最大和网络能耗最小为优化目标

提出了一种基于多目标优化的二进制粒子群算法

对节点部署进行多目标优化。该算法采用概率感知模型

引入强支配系数使得解分布均匀

结合Pareto最优解选择排序和基于自适应权重的适应度分配

进而获得异构节点部署解。仿真结果表明:该算法能对目标空间进行广泛搜索

与NSGA—Ⅱ算法相比

算法具有良好的收敛性

能有效地提高网络的覆盖率和降低网络能耗。

Abstract

An algorithm based on multi-objective optimization binary particle swarm is proposed to solve the problem of node high-density deployment and events existing "hot spots" in heterogeneous wireless sensor networks.Area coverage is maximum and energy consumption is minimum are the optimization goals.Probability sensing model is used and strong predominance coefficient is introduced to provide a good diversity

both pareto optimum solution sorting and adaptive weight fitness assignment methods are used in this algorithm to get heterogeneous wireless sensor network’s node deployment solutions.Compared with NSGA—Ⅱ

the algorithm has good astringency and effectively improve network coverage and reduce energy consumption.

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references

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基于GA的网络最短路径多目标优化算法研究 [J]. 阎啸天,武穆清. 控制与决策 . 2009(07)

无线传感器网络中基于微粒群算法的优化覆盖机制 [J]. 郭怡婷,王俊年. 计算机与现代化 . 2009(06)

基于粒子群算法的无线传感网络覆盖优化策略 [J]. 林祝亮,冯远静. 计算机仿真 . 2009(04)

无线传感器网络中最优覆盖节点集的求解算法 [J]. 贾杰,陈剑,常桂然,赵林亮. 东北大学学报(自然科学版) . 2007(11)

The Coverage Problem in a Wireless Sensor Network [J] . Chi-Fu Huang,Yu-Chee Tseng. Mobile Networks and Applications . 2005 (4)

Combining Convergence and Diversity in Evolutionary Multiobjective Optimization [J] . Marco Laumanns,Lothar Thiele,Kalyanmoy Deb,Eckart Zitzler. Evolutionary Computation . 2002 (3)

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