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

School Of Mathematical Sciences

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https://doi.org/10.1007/s11222-024-10428-2
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Available under license: CC BY: Creative Commons Attribution 4.0 International License

Keywords

Internet of Things, Distributed computing, Changepoints, MOSUM, Real-time analysis, Online

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A communication-efficient, online changepoint detection method for monitoring distributed sensor networks. / Yang, Ziyang ; Eckley, Idris A.; Fearnhead, Paul.
In: Statistics and Computing, Vol. 34, No. 3, 115, 30.06.2024.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{69e0f07eda4843eba6b33e33453db49b,

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

abstract = "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.",

keywords = "Internet of Things, Distributed computing, Changepoints, MOSUM, Real-time analysis, Online",

author = "Ziyang Yang and Eckley, {Idris A.} and Paul Fearnhead",

year = "2024",

month = jun,

day = "30",

doi = "10.1007/s11222-024-10428-2",

language = "English",

volume = "34",

journal = "Statistics and Computing",

issn = "0960-3174",

publisher = "Springer Netherlands",

number = "3",

}

RIS

TY - JOUR

T1 - A communication-efficient, online changepoint detection method for monitoring distributed sensor networks

AU - Yang, Ziyang

AU - Eckley, Idris A.

AU - Fearnhead, Paul

PY - 2024/6/30

Y1 - 2024/6/30

N2 - 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.

AB - 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.

KW - Internet of Things

KW - Distributed computing

KW - Changepoints

KW - MOSUM

KW - Real-time analysis

KW - Online

U2 - 10.1007/s11222-024-10428-2

DO - 10.1007/s11222-024-10428-2

M3 - Journal article

VL - 34

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 3

M1 - 115

ER -

Research

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