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Energy Saving in WSN Using Monitoring Values Prediction

机译:使用监测值预测的WSN节能

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The Wireless Sensor Networks (WSNs) deployment introduces many issues and challenges mainly in terms of energy independence. In this context, we adopted the IBM control loop which is composed of four steps (Monitor, Analyze, Plan and Execute) to manage Quality of Service (QoS)11An extended version of AvroraZ simulator:http://www.redcad.org/members/benhalima/azem/.. This paper focuses on the first step which consists in sending periodically QoS values such as the value of power remaining in the battery of each sensor. We notice that the transmission process is very costly in terms of energy and reduces the battery lifetime. In this work, we propose a probabilistic approach that estimates a part of these QoS monitoring values and therefore economizes their transmission energy. Our approach is based on the hidden Markov chain and the fuzzy logic. It is composed of two steps: (i) learning which allows apprehending the WSNs behavior and (ii) prediction which estimates QoS monitoring values. A WSN application deployed in a datacenter is studied as an illustration. The carried out experiments over AZEM22An extended version of AvroraZ simulator:http://www.redcad.org/members/benhalima/azem/.WSN simulator show that the gain varies from 25% to 75% of the battery energy.
机译:无线传感器网络(WSN)部署主要在能源独立性方面引入了许多问题和挑战。在这种情况下,我们采用了由四个步骤(监视,分析,计划和执行)组成的IBM控制循环来管理服务质量(QoS)11 AvroraZ仿真器的扩展版本:http://www.redcad.org/本文的重点是第一步,即定期发送QoS值,例如每个传感器电池中剩余的电量值。我们注意到,传输过程在能源方面非常昂贵,并且会缩短电池寿命。在这项工作中,我们提出了一种概率方法,该方法可估计这些QoS监视值的一部分,从而节省其传输能量。我们的方法基于隐马尔可夫链和模糊逻辑。它由两个步骤组成:(i)允许理解WSN行为的学习以及(ii)估计QoS监视值的预测。作为示例,研究了部署在数据中心中的WSN应用程序。在AZEM22上进行的实验AvroraZ模拟器的扩展版本:http://www.redcad.org/members/benhalima/azem/.WSN模拟器显示,增益从电池能量的25%到75%不等。

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