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Optimal Sensor and Relay Nodes Power Scheduling for Remote State Estimation with Energy Constraint

机译:具有能量约束的远程状态估计的最优传感器和中继节点功率调度

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摘要

We study the sensor and relay nodes’ power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes’ scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.
机译:我们研究了无线传感器网络(WSN)中传感器和中继节点的功率调度问题,以进行远程状态估计,其中无线节点网络在有限的通信能量下在有限的时间段内具有中继节点。我们还解释了为什么没有最佳的无限时间和能量的情况。先前的工作为WSN中两个连续节点的误差协方差间隙应用了预定义的阈值,以调整能耗和估计精度之间的权衡。但是,我们没有采用权衡取舍的方法,而是采用一种算法来找到最优传感器和中继节点的调度策略,该策略在模型假设下可以在给定的能量限制内实现最小的估计误差。我们的核心思想是通过将前一种更新方式转换为后者,从而将传感器到中继节点的误差协方差更新方法与中继节点到中继节点的方法统一起来。利用分析方法对不同调度序列的误差协方差进行估计,从而找到估计误差最小的策略。通过示例来说明转换的可行性。同时,我们证明了调度算法的最优性。最后,我们使用MATLAB来运行我们的算法并计算最佳策略的平均估计误差协方差。通过将我们的策略与其他策略的平均误差协方差进行比较,我们发现我们的策略在仿真中的性能优于其他策略。

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