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Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems

机译：移动资产跟踪系统中故障节点和左节点之间的分类

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摘要

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.
机译：医疗资产跟踪系统会跟踪带有移动节点的医疗设备，并确定其状态为进入还是退出，因为它可以离开监视区域。由于节点故障，该系统可能会确定移动资产在该区域之外，即使它在该区域内也是如此。在本文中，提出了一种有效的分类方法，该方法可以在故障节点和实际离开监视区域的节点之间分离与无线传感器网络断开连接的移动节点。所提出的方案使用从断开的移动节点的相邻节点提取的两个趋势。首先是一系列邻居数量的趋势；第二个是邻居中包含的边界节点的比率。基于这样的趋势，所提出的方法将故障节点与与无线传感器网络断开连接且没有故障的移动节点分离。既可以使用从医疗资产跟踪系统生成的真实数据，也可以使用网络模拟器（ns-2）进行的仿真来评估所提出的方法。实验结果表明，所提出的方法能够正确地区分失效节点和不再处于监视区域的节点，包括常规方法无法检测到的情况。

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