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Distributed Event Detection in Wireless Sensor Networks for Forest Fires

机译:森林火灾无线传感器网络中的分布式事件检测

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As the technology is advancing, Wireless Sensor Networks (WSN) is a gaining importance in recent research areas as it has proved its usefulness in warning disasters and save lives and assets. When an unusual event is noticed in the networks, an event is detected through the sensor devices placed at distributed locations. This event detection information is passed to the base station and intelligent decision is taken. Various machine learning techniques are used to decide whether the event has occurred or not. In this paper, we proposed an ensemble distributed machine learning approach for detecting events. This approach works in two phases, base phase and meta phase, clustream and Support Vector Machine approach are used for detection and prediction of events. One hop tree is used in this approach in order to minimize the delay in transmitting information.
机译:随着技术的发展,无线传感器网络(WSN)在最近的研究领域中变得越来越重要,因为它已证明在警告灾难,挽救生命和财产方面很有用。当网络中发现异常事件时,将通过放置在分布式位置的传感器设备检测到事件。该事件检测信息被传递给基站,并做出智能决策。使用各种机器学习技术来确定事件是否发生。在本文中,我们提出了一种用于检测事件的整体分布式机器学习方法。这种方法分两个阶段工作,即基础阶段和元阶段,clustream和支持向量机方法用于事件的检测和预测。在该方法中使用一个跃点树,以最小化传输信息的延迟。

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