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Information-Driven Collaborative Processing for Diffusive Source Estimation in Wireless Sensor Networks

机译:信息驱动的协同处理,用于无线传感器网络中的扩散源估计

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This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.
机译:本文讨论了一种用于扩散源定位的精确分布式算法,同时保持了无线传感器网络中传感器节点的低能耗。在该算法中,使用了基于信息效用度量的传感器选择方案。为了更新每个选定节点中的估计,定义了等于传感器节点通信范围的邻域半径,并且位于邻域圆中的所有传感器(其半径等于邻域半径且选定节点为其中心)与它们的协作信息。为了减少能量消耗,基于估计的误差协方差值逐渐减小邻域半径。此外,本文还提供了一种新的初始点计算方法,该方法对于无线传感器网络中分布式算法的递归方法很重要。数值示例用于研究算法的性能。仿真结果表明,该算法在能量消耗足够低的情况下,精度更高。

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