Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

中文翻译：

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使用时间序列模型减少WSN中的能源预算

节能对无线传感器网络的设计至关重要，因为它决定了其寿命。降低传输频率是降低成本的一种方法，但是它一定不能篡改在接收器处接收到的数据的可靠性。在本文中，占空比和数据驱动方法已一起使用，以影响用于减少数据传输的预测方法。占空比可确保较长时间处于非活动状态的节点以节省能源，而数据驱动方法可确保用于预测网络在此类非活动时间段所需的数据的数据特征。使用灰色系列模型，提出了改进的滚动GM（1,1），以提高模型的预测精度。