Deterministic Multiple Change-Point Detection with Limited Communication

Eyal Nitzan, Topi Halme, H. Vincent Poor, Visa Koivunen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)


Large-scale sensor networks are used in modern applications to perform statistical inference. In particular, multiple change-point detection using a sensor network is of interest in applications, such as Internet of Things and environmental monitoring. In this paper, we consider deterministic multiple change-point detection using a sensor network, in which each sensor observes a different data stream and communicates with a fusion center (FC). Due to communication limitations, the fusion center monitors only a subset of the sensors at each time slot. We propose a detection procedure that takes into account these limitations. In this procedure, the FC monitors the sensors with the highest cumulative sum values under the communication limitations. It is shown that the proposed procedure is scalable in the sense that it attains an average detection delay (ADD) that does not increase with the number of sensors, while controlling the false discovery rate. Using the proposed procedure, we identify and analyze the tradeoff between reducing the ADD and reducing the average number of observations drawn until the change-points are declared.

Original languageEnglish
Title of host publication2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
ISBN (Electronic)9781728140841
Publication statusPublished - Mar 2020
MoE publication typeA4 Article in a conference publication
EventConference on Information Sciences and Systems - Princeton, United States
Duration: 18 Mar 202020 Mar 2020
Conference number: 54


ConferenceConference on Information Sciences and Systems
Abbreviated titleCISS
Country/TerritoryUnited States


  • communication limitations
  • deterministic multiple changepoint detection
  • false discovery rate
  • Sensor networks


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