Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (10): 96-100.DOI: 10.3778/j.issn.1002-8331.1512-0130

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Improved Monte Carlo Box localization algorithm for mobile nodes in Wireless Sensor Network

LU Ying   

  1. College of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2017-05-15 Published:2017-05-31


陆  颖   

  1. 江南大学 物联网工程学院,江苏 无锡 214122

Abstract: With the consideration of the problem that the Monte Carlo Box algorithm used for mobile anchor node has the drawback of low accuracy, this paper proposes an improved Monte Carlo Box Localization Algorithm which is based on RSSI. Firstly, this algorithm reduces the size of anchor box according to the RSSI value received by nodes. Secondly, in order to have a valid sample area furtherly, two Newton interpolation method is used for predicting node motion and moving speed. Thirdly, the number of samples required on the basis of the size of the sampling box is adaptively determined to avoid the collection of redundant sample. Finally, the idea of crossover and mutation in genetic algorithm is used to optimize the sampling process.Compared with the traditional MCB algorithm, experimental results show that under the situation of different anchor node density, the positioning accuracy of the improved scheme is always higher.

Key words: Received Signal Strength Indication(RSSI), Newton interpolation, Monte Carlo Box(MCB), genetic algorithm

摘要: 考虑到蒙特卡洛盒移动节点定位算法中存在的定位精度低的缺陷,提出一种改进的基于RSSI的MCB定位算法。该算法依据节点接收的RSSI值缩小锚盒子区域;利用二次牛顿插值法预测节点运动轨迹,估算移动速度,进一步获取有效采样区域;然后依据采样盒大小自适应确定采样个数,避免多余样本的采集;最后借鉴遗传算法中交叉和变异思想优化采集过程。实验仿真结果表明在不同锚节点密度情况下,改进后的移动节点算法的定位精度始终优于传统MCB算法。

关键词: 接收的信号强度指示(RSSI), 牛顿插值法, 蒙特卡洛盒, 遗传算法