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Sensors 2016, 16(2), 240; doi:10.3390/s16020240

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

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
Author to whom correspondence should be addressed.
Academic Editors: Neal N. Xiong and Xuefeng Liang
Received: 12 December 2015 / Revised: 31 January 2016 / Accepted: 4 February 2016 / Published: 18 February 2016
(This article belongs to the Special Issue Mobile Sensor Computing: Theory and Applications)
View Full-Text   |   Download PDF [1096 KB, uploaded 18 February 2016]   |  


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. View Full-Text
Keywords: wireless sensor network; mobile node; medical asset; node classification; failure detection wireless sensor network; mobile node; medical asset; node classification; failure detection

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kim, K.; Jin, J.-Y.; Jin, S.-I. Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems †. Sensors 2016, 16, 240.

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