TY - GEN
T1 - Bayesian Multiple Change-Point Detection with Limited Communication
AU - Halme, Topi
AU - Nitzan, Eyal
AU - Poor, H. Vincent
AU - Koivunen, Visa
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Sensor networks
KW - average detection delay
KW - communication limitations
KW - false discovery rate
KW - multiple change-point detection
UR - http://www.scopus.com/inward/record.url?scp=85089245208&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9053654
DO - 10.1109/ICASSP40776.2020.9053654
M3 - Conference contribution
AN - SCOPUS:85089245208
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5490
EP - 5494
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -