TY - GEN
T1 - Adaptive distributed compressed sensing for dynamic high-dimensional hypothesis testing
AU - Michelusi, Nicolò
AU - Mitra, Urbashi
PY - 2014
Y1 - 2014
N2 - In this paper, a framework for dynamic high-dimensional hypothesis testing in wireless sensor networks is presented. The sensor nodes (SNs) collect and transmit to a fusion center (FC), in a distributed fashion, compressed measurements of a time-correlated hypothesis vector. The FC, based on the measurements collected, tracks the hypothesis vector, and feeds back minimal information about the uncertainty in the current estimate, which enables adaptation of the SNs' data collection and transmission strategy. The policy of the SNs is optimized with the overall objective of minimizing the detection error probability, under sensing and transmission cost constraints incurred by each SN. A Bernoulli approximation on the detection error is employed, which enables a significant reduction in the optimization complexity and the design of scalable estimators based on sparse approximation recovery algorithms. Simulation results demonstrate that, for a target 5% detection error, the adaptive scheme attains 90% and 50% cost savings with respect to a memoryless scheme which does not exploit the time-correlation and a non-adaptive one, respectively.
AB - In this paper, a framework for dynamic high-dimensional hypothesis testing in wireless sensor networks is presented. The sensor nodes (SNs) collect and transmit to a fusion center (FC), in a distributed fashion, compressed measurements of a time-correlated hypothesis vector. The FC, based on the measurements collected, tracks the hypothesis vector, and feeds back minimal information about the uncertainty in the current estimate, which enables adaptation of the SNs' data collection and transmission strategy. The policy of the SNs is optimized with the overall objective of minimizing the detection error probability, under sensing and transmission cost constraints incurred by each SN. A Bernoulli approximation on the detection error is employed, which enables a significant reduction in the optimization complexity and the design of scalable estimators based on sparse approximation recovery algorithms. Simulation results demonstrate that, for a target 5% detection error, the adaptive scheme attains 90% and 50% cost savings with respect to a memoryless scheme which does not exploit the time-correlation and a non-adaptive one, respectively.
KW - distributed systems
KW - Hypothesis testing
KW - sensor networks
KW - stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=84905269807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905269807&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854845
DO - 10.1109/ICASSP.2014.6854845
M3 - Conference contribution
AN - SCOPUS:84905269807
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6444
EP - 6448
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -