基于协同预测的无线传感器网络全移动节点定位算法

节点定位技术是无线传感器网络的关键问题之一,分析了无线电的路径损耗模型,建立了基于信号接收强度（received signal strength indicator,RSSI）和距离的拟合关系模型,提出了一种基于协同预测的无线传感器网络全移动节点定位方法。该方法解决了当能够与未知节点通信的锚节点数量少于三个而不能定位的问题,算法利用未知节点历史时刻的位置信息辅助当前时刻的未知节点定位,即把未知节点历史时刻的位置作为锚节点的位置,速度值作为通信半径对未知节点进行辅助定位。仿真结果表明,与传统RSSI定位算法相比,该算法的定位成功率提高了约30%,每轮的平均相对误差降低了约47%。...

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Bibliographic Details
Published in计算机应用研究 Vol. 34; no. 1; pp. 186 - 188
Main Author 李建坡 穆宝春
Format Journal Article
LanguageChinese
Published 东北电力大学 信息工程学院,吉林 吉林,132012 2017
Subjects
信号接收强度
协同定位
无线传感器网络
移动节点定位
预测定位
预测定位
cooperated localization
RSSI
wireless sensor networks
信号接收强度
协同定位
predicted localization
移动节点定位
moving node localization
无线传感器网络
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ISSN1001-3695
DOI10.3969/j.issn.1001-3695.2017.01.041

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Summary:节点定位技术是无线传感器网络的关键问题之一,分析了无线电的路径损耗模型,建立了基于信号接收强度（received signal strength indicator,RSSI）和距离的拟合关系模型,提出了一种基于协同预测的无线传感器网络全移动节点定位方法。该方法解决了当能够与未知节点通信的锚节点数量少于三个而不能定位的问题,算法利用未知节点历史时刻的位置信息辅助当前时刻的未知节点定位,即把未知节点历史时刻的位置作为锚节点的位置,速度值作为通信半径对未知节点进行辅助定位。仿真结果表明,与传统RSSI定位算法相比,该算法的定位成功率提高了约30%,每轮的平均相对误差降低了约47%。
Bibliography:wireless sensor networks; moving node localization; RSSI; cooperated localization; predicted localization
51-1196/TP
Li Jianpo, Mu Baochun （ School of Information Engineering, Northeast Dianli University, Jilin Jilin 132012, China）
The node localization technology is one of the key issues in wireless sensor networks. This paper analyzed the radio path loss model, established a fitting model between RSSI and distance, proposed moving node localization algorithm based on cooperated prediction. The algorithm solved the problem that the unknown nodes couldn＇ t be located when the number of anchor nodes were less than three. The historical location coordinated of unknown nodes were used as the location of virtual anchor node. The moving speed was used as the virtual communication radius. Simulation results show that, compared with the traditional RSSI localization algorithm, the proposed algorithm＇ s success rate of unknown node localization is improved by about 30% and the average relative error is reduced by about
ISSN:1001-3695
DOI:10.3969/j.issn.1001-3695.2017.01.041