TY - GEN
T1 - Distributed adaptive quantization for wireless sensor networks
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
AU - Fang, Jun
AU - Li, Hongbin
PY - 2008
Y1 - 2008
N2 - We consider the problem of distributed parameter estimation in wireless sensor networks (WSNs), where due to bandwidth/power constraints, each sensor quantizes its local observation into one bit of information that is sent to a fusion center (FC) to form a global estimate. Conventional fixed quantization (FQ) approaches, which utilize a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To overcome this difficulty, we propose a distributed adaptive quantization (AQ) approach, where, under the condition that sensors successively broadcast their quantized data, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors. Specifically, our strategy here is to let each sensor choose its quantization threshold as the maximum likelihood (ML) estimate of the unknown parameter based on the quantized data sent from other sensors. The Cramér-Rao bound (CRB) analysis shows that our proposed one-bit AQ approach asymptotically attains an estimation variance that is only π/2 times that of the clairvoyant sample-mean estimator using unquantized observations.
AB - We consider the problem of distributed parameter estimation in wireless sensor networks (WSNs), where due to bandwidth/power constraints, each sensor quantizes its local observation into one bit of information that is sent to a fusion center (FC) to form a global estimate. Conventional fixed quantization (FQ) approaches, which utilize a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To overcome this difficulty, we propose a distributed adaptive quantization (AQ) approach, where, under the condition that sensors successively broadcast their quantized data, each sensor adaptively adjusts its quantization threshold using prior transmissions from other sensors. Specifically, our strategy here is to let each sensor choose its quantization threshold as the maximum likelihood (ML) estimate of the unknown parameter based on the quantized data sent from other sensors. The Cramér-Rao bound (CRB) analysis shows that our proposed one-bit AQ approach asymptotically attains an estimation variance that is only π/2 times that of the clairvoyant sample-mean estimator using unquantized observations.
KW - Adaptive quantization
KW - Distributed estimation
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=51449083097&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449083097&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4518214
DO - 10.1109/ICASSP.2008.4518214
M3 - Conference contribution
AN - SCOPUS:51449083097
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2733
EP - 2736
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -