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计算机工程 ›› 2008, Vol. 34 ›› Issue (20): 18-20. doi: 10.3969/j.issn.1000-3428.2008.20.007

• 博士论文 • 上一篇    下一篇

群集智能算法在WSN节点定位中的应用

周 晖1,2,徐 晨2,李丹美1,邵世煌1,袁从明2   

  1. (1. 东华大学信息科学与技术学院,上海 201620;2. 南通大学电子信息学院,南通 226019)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-10-20 发布日期:2008-10-20

Application of Swarm Intelligent Algorithm in Wireless Sensor Network Node Location

ZHOU Hui1,2, XU Chen2, LI Dan-mei1, SHAO Shi-huang1, YUAN Cong-ming2   

  1. (1. College of Information Science and Technology, Donghua University, Shanghai 201620; 2. School of Electronics and Information, Nantong University, Nantong 226019)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-10-20 Published:2008-10-20

摘要: 为提高无线传感器网络(WSN)的节点定位的估计精度,提出基于自由搜索优化的智能估计定位算法。自由搜索是一种新的群集智能算法,应用于函数优化。该算法计算量少、收敛速度高、程序实现简洁、需要调整的参数少。利用智能优化算法将参数估计问题转化为非线性函数的优化问题。仿真实验结果显示,与最小二乘估计定位算法相比,新算法的定位精度有所提高。

关键词: 无线传感器网络, 节点定位, 参数估计, 自由搜索, 群集智能

Abstract: This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network(WSN) nodes location based on Free Search(FS) to improve the precision in location estimation. FS is a novel swarm intelligence algorithm which is used for continuous search space. Besides reducing calculation and increasing the convergence speed, FS algorithm is easier to achieve and has less parameters to be adjusted in comparison with other swarm intelligent algorithms. The basic principle and the implementing approaches of the algorithm are introduced. Compared with the least-squares estimation algorithms, the localization accuracy increases significantly, which is verified by the simulation results.

Key words: Wireless Sensor Network(WSN), node location, parameter estimation, Free Search(FS), swarm intelligence

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