用于监测分布式传感器网络的通信效率高的在线变化点检测方法

IF 1.6 2区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS Statistics and Computing Pub Date : 2024-04-14 DOI:10.1007/s11222-024-10428-2
Ziyang Yang, Idris A. Eckley, Paul Fearnhead
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引用次数: 0

摘要

我们考虑了在传感器网络内有效检测变化的挑战,在这种情况下,我们还需要尽量减少传感器与云之间的通信。我们提出了一种在线、通信效率高的方法来检测此类变化。该方法在每个时间点进行似然比检验,并选择两个阈值分别用于过滤不重要的检验统计量和根据综合检验统计量做出决策。我们提供了有关一致性的渐近理论以及没有变化时的渐近分布。仿真结果表明,我们的方法可以达到与理想化设置类似的性能,在理想化设置中,我们对传感器之间的通信没有任何限制,但却大大降低了传输成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A communication-efficient, online changepoint detection method for monitoring distributed sensor networks

We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an online, communication-efficient method to detect such changes. The procedure works by performing likelihood ratio tests at each time point, and two thresholds are chosen to filter unimportant test statistics and make decisions based on the aggregated test statistics respectively. We provide asymptotic theory concerning consistency and the asymptotic distribution if there are no changes. Simulation results suggest that our method can achieve similar performance to the idealised setting, where we have no constraints on communication between sensors, but substantially reduce the transmission costs.

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来源期刊
Statistics and Computing
Statistics and Computing 数学-计算机:理论方法
CiteScore
3.20
自引率
4.50%
发文量
93
审稿时长
6-12 weeks
期刊介绍: Statistics and Computing is a bi-monthly refereed journal which publishes papers covering the range of the interface between the statistical and computing sciences. In particular, it addresses the use of statistical concepts in computing science, for example in machine learning, computer vision and data analytics, as well as the use of computers in data modelling, prediction and analysis. Specific topics which are covered include: techniques for evaluating analytically intractable problems such as bootstrap resampling, Markov chain Monte Carlo, sequential Monte Carlo, approximate Bayesian computation, search and optimization methods, stochastic simulation and Monte Carlo, graphics, computer environments, statistical approaches to software errors, information retrieval, machine learning, statistics of databases and database technology, huge data sets and big data analytics, computer algebra, graphical models, image processing, tomography, inverse problems and uncertainty quantification. In addition, the journal contains original research reports, authoritative review papers, discussed papers, and occasional special issues on particular topics or carrying proceedings of relevant conferences. Statistics and Computing also publishes book review and software review sections.
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