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

Seong-Il Jin,Jae-Yeon Jin,Kwangsoo Kim

doi:10.3390/s16020240

Abstract

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.

Highlights

With the development of various embedded computing platforms and low power sensing components, a diverse set of applications using wireless sensor networks (WSN) has been implemented in the past decade
These results indicate that some left nodes are classified as a failed node incorrectly because they reported their last values within the monitoring area
It is clearly understood that the classification between a left node and a failed node is difficult by the fact that it cannot be assured that a mobile node will report its values outside the monitoring area before it is entirely disconnected from the wireless infrastructure

Summary

Introduction

With the development of various embedded computing platforms and low power sensing components, a diverse set of applications using wireless sensor networks (WSN) has been implemented in the past decade. These applications can be divided into two categories based on the type of sensor nodes that they use. Mobile sensor networks are composed of many mobile nodes In these networks, one or more mobile nodes are attached to a mobile object to monitor its movement and current location in real time. Such systems can be applied to healthcare systems [11–13], mobile asset tracking [14–17] and human monitoring applications [18]

Results

Discussion

Conclusion