TY - GEN
T1 - Distributed adaptive quantization and estimation for wireless sensor networks
AU - Hongbin, Li
PY - 2007
Y1 - 2007
N2 - We consider distributed parameter estimation in a wireless sensor network, where due to bandwidth constraint, all sensor nodes have to quantize their observations and send quantized data to a fusion center. We consider the case where each sensor can send only one bit of information. In such a case, the achievable estimation performance is critically dependent on the choice of the one-bit quantizer used at the sensor nodes to perform quantization; it is also known that a fixed quantizer does not perform well, in particular when the quantization threshold is away from the unknown parameter to be estimated. In this paper, we propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. We develop the maximum likelihood estimator (MLE) and derive the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme. Numerical results show that our approach does not suffer from the drawback of the fixed quantization approach and outperforms the latter.
AB - We consider distributed parameter estimation in a wireless sensor network, where due to bandwidth constraint, all sensor nodes have to quantize their observations and send quantized data to a fusion center. We consider the case where each sensor can send only one bit of information. In such a case, the achievable estimation performance is critically dependent on the choice of the one-bit quantizer used at the sensor nodes to perform quantization; it is also known that a fixed quantizer does not perform well, in particular when the quantization threshold is away from the unknown parameter to be estimated. In this paper, we propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. We develop the maximum likelihood estimator (MLE) and derive the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme. Numerical results show that our approach does not suffer from the drawback of the fixed quantization approach and outperforms the latter.
KW - Adaptive quantization
KW - Distributed estimation
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=34547515616&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34547515616&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.366590
DO - 10.1109/ICASSP.2007.366590
M3 - Conference contribution
AN - SCOPUS:34547515616
SN - 1424407281
SN - 9781424407286
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - III533-III536
BT - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
T2 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Y2 - 15 April 2007 through 20 April 2007
ER -