无线传感器网络中多边定位的聚类分析改进算法

Improving Multilateration Algorithm by Cluster Analysis in WSN

针对距离偏差对多边定位算法的影响,提出了一种改进的KC-Multilateration算法.将K-means聚类方法引入到无线传感器网络的定位问题中,通过聚类分析对误差较大的距离信息进行筛选.对剩余距离信息使用多边定位法进行定位求解,作为最终结果.仿真实验表明,KC-Multilateration与原多边定位法相比在各种误差环境下均能有效降低定位误差,且定位结果稳定.在由实际节点构成的实验环境中使用RSSI值进行测距的进一步实验表明,在不增加任何通信开销的前提下,改进算法定位误差更小,容错性更高,验证了KC-Multilateration的有效性和实用性.

To reduce the impact of distance information errors,an improved multilateration algorithm named KC-Multilatera- tion is proposed. It is explored that K-means clustering methodology is employed to wireless sensor network localization schemes. By cluster analysis, KC-Multilateration algorithm can figure out the distance data which are far more beyond their true value, and then removes those data from the measured distance data. Then multilateration is adopted with the rest distance data, obtaining the final results. Simulation experiments indicate that the proposed KC-Multilateration can reduce location errors effectively and has more sta- ble location results in a variety of error environment comparing with the original multilateration algorithm. Further experiments based on RSSI indicate that the improved algorithm has smaller location errors and stronger performance of fault tolerance without adding any costs of communication, which verifies effectiveness and practicality of KC-Multilaterafion.