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A mobile asset with a sensor node in a mobile asset tracking system moves around a monitoring area, leaves it, and then returns to the region repeatedly. The system monitors the in/out status of the mobile asset. Due to the continuous movement of the mobile asset, the system may generate an error for the in/out status of the mobile asset. When the...
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... As the sink node cannot hear any messages from all mobile nodes that normally leave the WSN, Sympathy incorrectly considers them to be failed. Kim and Chung [24] proposed a failure detection method based on the connection state of a mobile node and its battery lifetime. It is used to detect faulty mobile nodes in mobile asset tracking systems. ...
... This section formalizes the system architecture of a medical asset tracking system developed in this work and the features of each component composing the system. The system architecture is shown inFigure 2. This system is composed of four-tuple S = (T, G, A, M), where T is the asset tracking application, G is the gateway, A = a 1 , a 2 , ..., a m is the set of anchor nodes and M = m 1 , m 2 , ..., m n is the set of mobile nodes [24]. A wireless sensor network is developed based on the ZigBee specifications and on IEEE 802.15.4 [26]. ...
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.
Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.
The upsurge in Cyber Physical Systems (CPSs) has made researchers conclude that these systems have the potential of rivalling the contribution of the Internet. Driving this wave is the emergence of miniaturized, cheaper and readily available location-based hardware devices. One of the main applications of CPSs is mobile asset tracking system whose roles are to monitor movements of a mobile asset and to track the object’s current position. Localization accuracy of these systems is one of the key performance indicators. This is usually maximised through the introduction of extra hardware devices. The drawbacks with this approach include restriction of the system’s application only to one domain, introduction of extra cost to the overall system and introduction of a single point of failure. Conversely, the Internet of Things (IoT) paradigm facilitates coalescing of diverse technologies through which locus data can be extracted in cost-effective and robust way. The challenge is the lack of a dependable and responsive middleware that is capable of handling and servicing such devices. We present a solution to this problem; a middleware designed around In-lining approach that acts as an insulator for hiding the internal workings of the system by providing homogenous and abstract environment to the higher layers. The evaluation of laptop tracking and monitoring system prototype was carried out through implementation of a middleware that integrates diverse IoT components in a university environment.
A medical asset tracking system monitors the in/out status of a medical asset with a mobile node which moves around a monitoring area, leaves it, and then returns to the region repeatedly. Due to failure of the mobile nodes, the system may determine that a mobile asset is outside the region despite the fact that the mobile asset is inside. In this paper, a novel failure detection method is proposed to resolve this problem. It uses two properties of the neighboring nodes of a mobile node disconnected from a sensor network. One is the trend of the neighbor counts; the other is that of the ratios of the boundary nodes contained in the neighbors. With these trends, the proposed method detects failed nodes from mobile nodes disconnected from a sensor network. The experimental results show that the proposed method can detect most failures of mobile nodes, including those that the conventional approaches fail to observe.
