TY - GEN
T1 - Communication-estimation tradeoffs in wireless sensor networks
AU - Son, S. H.
AU - Kulkarni, S. R.
AU - Schwartz, S. C.
AU - Roan, M.
PY - 2005
Y1 - 2005
N2 - The distributed nature of wireless sensor networks illustrates well classical engineering tradeoffs: how to minimize communication (and possibly computation) cost, and thus energy dissipation, while maintaining acceptable performance levels in estimation and inference applications. We study a simple sensor network under dependent Gaussian noise and develop strategies for parameter estimation in a variety of communication scenarios. From an energy point of view, sending all data to a fusion center is the most costly, but leads to optimum performance results. Processing data at each sensor and sending parameter estimates and associated quality measures is a reasonable communication saving procedure and yet, in some cases, may lead to performance equivalent to sending all data to the fusion center. A sequential procedure is most parsimonious in terms of communication cost and especially effective in large wireless sensor networks. We explore those conditions for which little, or no loss in performance is encountered with this sequential procedure. Specifically, we provide analytical expressions for the maximum likelihood estimator under "geometric" dependent noise. We show, by means of analysis and simulations, that the performance is only marginally degraded when the noise is assumed to be independent.
AB - The distributed nature of wireless sensor networks illustrates well classical engineering tradeoffs: how to minimize communication (and possibly computation) cost, and thus energy dissipation, while maintaining acceptable performance levels in estimation and inference applications. We study a simple sensor network under dependent Gaussian noise and develop strategies for parameter estimation in a variety of communication scenarios. From an energy point of view, sending all data to a fusion center is the most costly, but leads to optimum performance results. Processing data at each sensor and sending parameter estimates and associated quality measures is a reasonable communication saving procedure and yet, in some cases, may lead to performance equivalent to sending all data to the fusion center. A sequential procedure is most parsimonious in terms of communication cost and especially effective in large wireless sensor networks. We explore those conditions for which little, or no loss in performance is encountered with this sequential procedure. Specifically, we provide analytical expressions for the maximum likelihood estimator under "geometric" dependent noise. We show, by means of analysis and simulations, that the performance is only marginally degraded when the noise is assumed to be independent.
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U2 - 10.1109/ICASSP.2005.1416491
DO - 10.1109/ICASSP.2005.1416491
M3 - Conference contribution
AN - SCOPUS:33646777461
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - V1065-V1068
BT - 2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Education, Spec. Sessions
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -