TY - GEN
T1 - Deterministic Multiple Change-Point Detection with Limited Communication
AU - Nitzan, Eyal
AU - Halme, Topi
AU - Poor, H. Vincent
AU - Koivunen, Visa
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - 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.
AB - 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.
KW - Sensor networks
KW - communication limitations
KW - deterministic multiple changepoint detection
KW - false discovery rate
UR - http://www.scopus.com/inward/record.url?scp=85085242832&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085242832&partnerID=8YFLogxK
U2 - 10.1109/CISS48834.2020.1570627514
DO - 10.1109/CISS48834.2020.1570627514
M3 - Conference contribution
AN - SCOPUS:85085242832
T3 - 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
BT - 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th Annual Conference on Information Sciences and Systems, CISS 2020
Y2 - 18 March 2020 through 20 March 2020
ER -