Abstract: The complexity of forest environment leads to the large location error of the Received Signal Strength Indication(RSSI) of sensor network,and the current RSSI path loss model cannot meet the location requirement of sensor node in forest.Aiming at this problem,this paper proposes a Wireless Sensor Network(WSN) forest location algorithm.According to the discrete coefficient division location area of RSSI in different regions,the RSSI path loss model is established for different regions.A new model which is more suitable for the actual environment is built by using combination of log path loss model and piecewise fitting model.The location error is eliminated by the sub-region range location and K-means clustering algorithm.Experimental results show that the proposed algorithm can effectively improve the position accuracy.

Key words: Wireless Sensor Network(WSN), forest location, fusion model, sub-region location, K-means clustering

摘要： 由于森林环境的复杂性导致传感器网络接收信号强度指示(RSSI)的定位误差较大,而目前的RSSI路径损耗模型不能满足森林中传感器节点定位的需求。针对该问题,提出一种无线传感器网络(WSN)森林定位算法。根据RSSI在不同区域的离散系数划分定位区域,对不同区域分别建立RSSI路径损耗模型,并利用对数路径损耗模型与分段拟合模型进行融合,建立更符合实际环境的新模型,通过分区域测距定位和K-means聚类算法排除定位误差。实验结果表明,该算法能有效提高定位精度。

关键词: 无线传感器网络, 森林定位, 融合模型, 分区域定位, K-means聚类

CLC Number: 

• TP301.6