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Distributed Estimation in Wireless Sensor Networks: Robust Nonparametric and Energy Efficient Environment Monitoring

机译:无线传感器网络中的分布式估计:鲁棒的非参数和节能环境监控

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摘要

Wireless sensor networks estimate some parameters of interest associated with the environment by processing the spatio-temporal data. In classical methods the dataudcollected at different sensor nodes are combined at the fusion center(FC) through multihop communications and the desired parameter is estimated. However, this requires a large number of communications which leads to a fast decay of energy at the sensor nodes. Different distributed strategies have been reported in literature which use the computational capability of the sensor nodes and the estimated local parameters of the neighborhood nodes to achieve the global parameters of interest. However all these distributed strategies are based on the least square error cost function which is sensitive to the outliers such as impulse noise and interference present in the desired and/or input data. Therefore there is need of finding the proper robust cost functions which would be suitable for wireless sensor network in terms of communication and computational complexities. This dissertation deals with the development of a number of robust distributed algorithms based on the notion of rank based nonparametric robust cost functions to handle outliers in the (i) desired data; (ii) input data; (iii) in both input and desired data; and (iv) desired data in case of highly colored input data. Exhaustive simulation studies show that the proposed methods are robust against 50% outliers in the data, provide better convergence and low mean square deviation. Further this thesis deals with a real world application of energy efficient environment monitoring. A minimum volume ellipsoid is formed using distributed strategy covering those sensor nodes which indicate the event of interest. In addition a novel technique is proposed for finding the incremental path for regularly placed sensor nodes. It is shown mathematically that the proposed distributed strategy enhances the lifetime of the entire network drastically.
机译:无线传感器网络通过处理时空数据来估计一些与环境相关的参数。在经典方法中,通过多跳通信在融合中心(FC)上合并在不同传感器节点处收集的数据,并估计所需的参数。然而,这需要大量的通信,这导致传感器节点处的能量快速衰减。在文献中已经报道了不同的​​分布式策略,这些策略使用传感器节点的计算能力和邻域节点的估计局部参数来实现感兴趣的全局参数。然而,所有这些分布式策略都是基于最小二乘误差成本函数,该函数对异常值敏感,例如期望的和/或输入数据中存在的脉冲噪声和干扰。因此,需要找到在通信和计算复杂度方面适合于无线传感器网络的适当的鲁棒成本函数。本文基于基于秩的非参数鲁棒成本函数的概念,研究了多种鲁棒的分布式算法,以处理(i)所需数据中的离群值; (ii)输入数据; (iii)输入数据和所需数据; (iv)在输入数据高度着色的情况下所需的数据。详尽的仿真研究表明,所提出的方法对数据中50%的异常值具有鲁棒性,可提供较好的收敛性和较低的均方差。此外,本文涉及节能环境监测的实际应用。使用覆盖了表示感兴趣事件的那些传感器节点的分布式策略来形成最小体积的椭球。另外,提出了一种新颖的技术,用于找到规则放置的传感器节点的增量路径。从数学上表明,所提出的分布式策略可以极大地延长整个网络的寿命。

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    Sahoo Upendra Kumar;

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