TY - GEN
T1 - Distributed estimation in sensor networks with quality feedback
T2 - 2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
AU - Michelusi, Nicolo
AU - Mitra, Urbashi
PY - 2013
Y1 - 2013
N2 - This paper presents a general framework for distributed estimation of a dynamical process in a Wireless Sensor Network (WSN) in which the Sensor Nodes (SNs) communicate their measurements to the Fusion Center (FC) via a single hop wireless channel. The SNs adapt their sensing/transmission strategy based on a minimal feedback message provided by the FC, which informs them on the estimation quality achieved. Intuitively, when the estimation quality is poor, the SNs react by sending accurate measurements, at higher cost. When the estimation quality is good, the SNs remain idle to preserve energy. The sensing/transmission strategy of each SN, the channel access, and the feedback from the FC are jointly optimized, with the objective of minimizing the mean squared error at the FC, given a cost constraint for each SN. It is shown that a performance gain can be achieved by exploiting the estimation quality feedback, over policies that do not exploit such information, while achieving scalability to large WSNs. Moreover, a low complexity myopic policy is provided, which is shown to achieve near-optimal performance.
AB - This paper presents a general framework for distributed estimation of a dynamical process in a Wireless Sensor Network (WSN) in which the Sensor Nodes (SNs) communicate their measurements to the Fusion Center (FC) via a single hop wireless channel. The SNs adapt their sensing/transmission strategy based on a minimal feedback message provided by the FC, which informs them on the estimation quality achieved. Intuitively, when the estimation quality is poor, the SNs react by sending accurate measurements, at higher cost. When the estimation quality is good, the SNs remain idle to preserve energy. The sensing/transmission strategy of each SN, the channel access, and the feedback from the FC are jointly optimized, with the objective of minimizing the mean squared error at the FC, given a cost constraint for each SN. It is shown that a performance gain can be achieved by exploiting the estimation quality feedback, over policies that do not exploit such information, while achieving scalability to large WSNs. Moreover, a low complexity myopic policy is provided, which is shown to achieve near-optimal performance.
UR - http://www.scopus.com/inward/record.url?scp=84897713752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897713752&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6737076
DO - 10.1109/GlobalSIP.2013.6737076
M3 - Conference contribution
AN - SCOPUS:84897713752
SN - 9781479902484
T3 - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
SP - 1057
EP - 1060
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Y2 - 3 December 2013 through 5 December 2013
ER -