Multisensor change detection on the basis of big time‐series data and Dempster‐Shafer theory
Multisensor change detection on the basis of big time‐series data and Dempster‐Shafer theory
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DOI：
10.1002/cpe.4026
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
H. Jafari;Xiangfang Li;Lijun Qian;Alexander J. Aved;Timothy S. Kroecker
中科院分区：
文献类型：
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作者：
H. Jafari;Xiangfang Li;Lijun Qian;Alexander J. Aved;Timothy S. Kroecker
随着物联网的普及，部署了许多传感器来监测在许多情况下可以通过底层随机过程建模的现象。目标是以可容忍的误报率检测过程中的变化。在实践中，传感器可能具有不同的精度和灵敏度范围，或者它们沿着时间衰减。因此，所感测的数据将包含不确定性，并且有时它们是冲突的。在这项研究中，我们提出了一个新的框架，以利用Dempster-Shafer理论的不确定性表示能力，以检测变化，并有效地处理互补的假设。具体来说，Kullback-Leibler散度被用作度量，以找到估计分布与变化前和变化后分布之间的距离。基于每个传感器的这些距离值独立地计算质量函数，并且应用Dempster-Shafer组合规则来联合收割机组合所有传感器之间的质量值。在各种传感器读数高度冲突的情况下，应用Dezert-Smarandache组合规则，并获得用于决策的置信度、可验证性和pignistic概率。仿真结果表明了该方法的有效性。
With the proliferation of the Internet of Things, numerous sensors are deployed to monitor a phenomenon that in many cases can be modeled by an underlying stochastic process. The goal is to detect change in the process with tolerable false alarm rate. In practice, sensors may have different accuracy and sensitivity range, or they decay along time. As a result, the sensed data will contain uncertainties and sometimes they are conflicting. In this study, we propose a novel framework to take advantage of Dempster‐Shafer theory's capability of representation of uncertainty to detect change and effectively deal with complementary hypotheses. Specifically, Kullback‐Leibler divergence is used as the metric to find the distances between the estimated distribution with the before and after change distributions. Mass functions are calculated on the basis of those distance values for each sensor independently, and Dempster‐Shafer combination rule is applied to combine the mass values among all sensors. In the case of high conflict in various sensor readings, Dezert‐Smarandache combination rule is applied, and the belief, plausibility, and pignistic probability are obtained for decision making. Simulation results using both synthetic data and real data demonstrate the effectiveness of the proposed schemes.