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A Mobile Device Prototype Application for the Detection and Prediction of Node Faults in Wireless Sensor Networks

机译:用于无线传感器网络中节点故障检测和预测的移动设备原型应用

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Various implementations of wireless sensor networks (i.e. personal area-, wireless body area- networks) are prone to node and network failures by such characteristics as limited node energy resources and hardware damage incurred from their surrounding environment (i.e. flooding, forest fires, a patient falling). This may jeopardize their reliability to act as early warning systems, monitoring systems for patients and athletes, and industrial and environmental observation networks. Following the current trend and widespread use of hand held, mobile communication devices, we outline an application architecture designed to detect and predict faulty nodes in wireless sensor networks. Furthermore, we implement our design as a proof of concept prototype for Android-based smartphones, which may be extended to develop other applications used for monitoring networked wireless personal area and body sensors used in other capacities. We have conducted several preliminary experiments to demonstrate the use of our design, which is capable of monitoring networks of wireless sensor devices and predicting node faults based on several localized metrics. As attributes of such networks may change over time, any models generated when the application is initialized must be updated periodically such that the applied machine learning algorithm maintains high levels of both accuracy and precision. The application is designed to discover node faults and, once identified, alert the user so that appropriate action may be taken.
机译:无线传感器网络(即个人区域,无线人体区域网络)的各种实现方式容易受到节点和网络故障的影响,例如节点能量资源有限以及其周围环境(例如洪水,森林大火,病人)引起的硬件损坏下降)。这可能会损害其作为预警系统,患者和运动员的监视系统以及工业和环境观察网络的可靠性。随着当前趋势和手持移动通信设备的广泛使用,我们概述了一种旨在检测和预测无线传感器网络中故障节点的应用程序体系结构。此外,我们将我们的设计实现为基于Android的智能手机的概念验证原型,可以扩展以开发用于监视联网的无线个人区域和其他功能的人体传感器的其他应用程序。我们已经进行了一些初步的实验来证明我们设计的使用,该设计能够监视无线传感器设备的网络并基于几个本地化指标预测节点故障。由于此类网络的属性可能会随时间变化,因此必须定期更新在初始化应用程序时生成的任何模型,以使所应用的机器学习算法保持较高的准确性和精度。该应用程序旨在发现节点故障,并在发现故障后提醒用户,以便采取适当的措施。

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