Kim Kwangsoo, Jin Jae-Yeon, Jin Seong-Il
UGS Convergence Research Division, ETRI, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea.
Department of Computer Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea.
Sensors (Basel). 2016 Feb 18;16(2):240. doi: 10.3390/s16020240.
Medical asset tracking systems track a medical device with a mobile node and determine its status as either in or out, because it can leave a monitoring area. Due to a failed node, this system may decide that a mobile asset is outside the area, even though it is within the area. In this paper, an efficient classification method is proposed to separate mobile nodes disconnected from a wireless sensor network between nodes with faults and a node that actually has left the monitoring region. The proposed scheme uses two trends extracted from the neighboring nodes of a disconnected mobile node. First is the trend in a series of the neighbor counts; the second is that of the ratios of the boundary nodes included in the neighbors. Based on such trends, the proposed method separates failed nodes from mobile nodes that are disconnected from a wireless sensor network without failures. The proposed method is evaluated using both real data generated from a medical asset tracking system and also using simulations with the network simulator (ns-2). The experimental results show that the proposed method correctly differentiates between failed nodes and nodes that are no longer in the monitoring region, including the cases that the conventional methods fail to detect.
医疗资产跟踪系统通过移动节点跟踪医疗设备，并确定其处于监测区域内或外的状态，因为该设备可能会离开监测区域。由于节点故障，即使移动资产在区域内，该系统也可能判定其在区域外。本文提出了一种有效的分类方法，用于区分与无线传感器网络断开连接的移动节点，是出现故障的节点还是实际已离开监测区域的节点。所提方案利用从断开连接的移动节点的相邻节点提取的两种趋势。第一种是一系列邻居计数的趋势；第二种是邻居中包含的边界节点的比例趋势。基于这些趋势，所提方法将出现故障的节点与未出现故障而从无线传感器网络断开连接的移动节点区分开来。使用从医疗资产跟踪系统生成的实际数据以及使用网络模拟器（ns-2）进行的模拟对所提方法进行评估。实验结果表明，所提方法能够正确区分出现故障的节点和不再处于监测区域的节点，包括传统方法未能检测到的情况。
