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Several modern applications involve large-scale sensor networks for statistical inference. For example, such sensor networks are of significant interest for Internet of Things applications. In this paper, we consider Bayesian multiple changepoint detection using a sensor network in which a fusion center can receive a data stream from each sensor. Due to communication limitations, the fusion center monitors only a subset of the data streams at each time slot. We propose a detection procedure that handles these limitations by monitoring the sensors with the highest posterior probabilities of change points having occurred. It is shown that the proposed procedure attains an average detection delay that does not increase with the number of sensors, while controlling the false discovery rate. The proposed procedure is also shown to be useful for unveiling the tradeoff between reducing the average detection delay and reducing the average number of observations drawn until discovery.
|Title of host publication||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings|
|Number of pages||5|
|Publication status||Published - May 2020|
|MoE publication type||A4 Article in a conference publication|
|Event||IEEE International Conference on Acoustics, Speech, and Signal Processing - Virtual conference, Barcelona, Spain|
Duration: 4 May 2020 → 8 May 2020
Conference number: 45
|Name||Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing|
|Conference||IEEE International Conference on Acoustics, Speech, and Signal Processing|
|Period||04/05/2020 → 08/05/2020|
- average detection delay
- communication limitations
- false discovery rate
- multiple change-point detection
- Sensor networks
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- 1 Finished
Mozafari, M., Vesselinova, N., Koivunen, V., Hentilä, H., Naseri, H. & Halme, T.
12/04/2017 → 31/12/2019
Project: Academy of Finland: Other research funding