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Dynamic power management of wireless sensor networks based on grey model

机译:基于灰色模型的无线传感器网络动态电源管理

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The energy constraint of sensor nodes is the key factor that restrict the life of wireless sensor networks. So an effective method of dynamic power management (DPM) that based on grey model is proposed to make economical use of energy. Historical data collected of sensor node is used to predict the future value in this method, while the parameters are adjusted automatically in the process of prediction to realize the adaptive prediction. Compared with the algorithm of wavelet and AR, the accuracy of prediction is improved. The basic idea is to decide the working pattern of the entire sensor networks by the node of Sink, and in the next period sensor nodes do not send back data if their observed values are not out of threshold. To reduce energy consumption of the entire sensor networks by shortening the working hours and reducing transmitted messages between the nodes. Theory analysis and experiment result show that the method of this paper is effective not only in the predictive accuracy but also in the energy efficiency.
机译:传感器节点的能量约束是限制无线传感器网络寿命的关键因素。因此,提出了一种基于灰色模型的动态电源管理(DPM)有效方法,以实现能源的经济利用。该方法利用传感器节点收集的历史数据来预测未来价值,同时在预测过程中自动调整参数以实现自适应预测。与小波和AR算法相比,提高了预测精度。基本思想是由Sink节点确定整个传感器网络的工作模式,并且在下一个周期中,如果传感器节点的观测值未超出阈值,则不发送回数据。通过缩短工作时间并减少节点之间的传输消息来减少整个传感器网络的能耗。理论分析和实验结果表明,该方法不仅在预测精度上有效,而且在能源效率上也很有效。

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