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Optimal sensor hop selection : sensor energy minimization and network lifetime maximization with guaranteed system performance

机译:传感器跳的最佳选择:传感器能量最小化和网络寿命最大化,并保证系统性能

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摘要

In this paper we consider state estimation carried over a sensor network. A fusion center forms a local multi-hop tree of sensors and gateways and fuses the data into a state estimate. It is shown that the optimal estimator over a sensor tree is given by a Kalman filter of certain structure. The number of hops that the sensors use to communicate data with the fusion center is optimized such that either the overall transmission energy is minimized or the network lifetime is maximized. In both cases the fusion center provides a specified level of estimation accuracy. Some heuristic algorithms are proposed which lead to suboptimal solutions in the energy minimization problem, while an algorithm that leads to the global optimal solution is proposed in the lifetime maximization problem. In both cases, the algorithms are shown to have low computational complexity. Examples are provided to demonstrate the theory and algorithms.
机译:在本文中,我们考虑通过传感器网络进行的状态估计。融合中心形成传感器和网关的本地多跳树,并将数据融合为状态估计值。结果表明,传感器树上的最优估计量由某种结构的卡尔曼滤波器给出。传感器用于与融合中心进行数据通信的跃点数已得到优化,以使总传输能量最小化或网络寿命最大化。在这两种情况下,融合中心均提供指定级别的估计准确性。提出了一些启发式算法,这些算法在能量最小化问题中导致次优解,而在寿命最大化问题中,提出了一种导致全局最优解的算法。在这两种情况下,算法均显示出较低的计算复杂度。提供了示例来说明理论和算法。

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