Energy-Constrained Optimal Quantization for Wireless Sensor Networks

As low power, low cost and longevity of transceivers are major requirements in wireless sensor networks, optimizing their design under energy constraints is of paramount importance. To this end, we develop quantizers under strict energy constraints to effect optimal reconstruction at the fusion center. Propagation, modulation, as well as transmitter and receiver structures are jointly accounted for using a binary symmetric channel model. We first optimize quantization for reconstructing a single sensor's measurement. Optimal number of quantization levels and optimal energy allocation across bits are derived. We then consider multiple sensors collaborating to estimate a deterministic parameter in noise. Similarly, optimum energy allocation and optimum number of quantization bits are derived analytically and tested with simulated examples.

[1]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[2]  Anthony Ephremides,et al.  Energy efficient routing for connection-oriented traffic in ad-hoc wireless networks , 2000, 11th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. PIMRC 2000. Proceedings (Cat. No.00TH8525).

[3]  Andrea J. Goldsmith,et al.  Modulation optimization under energy constraints , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[4]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[5]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[6]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[7]  D. A. Bell,et al.  Information Theory and Reliable Communication , 1969 .

[8]  Y. Chau,et al.  Asymptotically optimal quantization and fusion in multiple sensor systems , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[9]  H. Vincent Poor,et al.  Memoryless quantizer- detectors for constant signals in m-dependent noise , 1980, IEEE Trans. Inf. Theory.

[10]  Andrea J. Goldsmith,et al.  Design challenges for energy-constrained ad hoc wireless networks , 2002, IEEE Wirel. Commun..

[11]  Jan M. Rabaey,et al.  PicoRadio Supports Ad Hoc Ultra-Low Power Wireless Networking , 2000, Computer.

[12]  A. Sripad,et al.  A necessary and sufficient condition for quantization errors to be uniform and white , 1977 .

[13]  Saleem A. Kassam,et al.  Optimum Quantization for Signal Detection , 1977, IEEE Trans. Commun..

[14]  Andrea J. Goldsmith,et al.  Joint estimation in sensor networks under energy constraints , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[15]  P. Willett,et al.  Optimal quantization in the dependent Gaussian problem , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[16]  Zhi-Quan Luo,et al.  Universal decentralized estimation in a bandwidth constrained sensor network , 2005, IEEE Transactions on Information Theory.

[17]  NARIMAN FARVARDIN,et al.  Optimal quantizer design for noisy channels: An approach to combined source - channel coding , 1987, IEEE Trans. Inf. Theory.

[18]  Brian M. Sadler,et al.  Source reconstruction via mobile agents in sensor networks: throughput-distortion characteristics , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[19]  Kannan Ramchandran,et al.  Distributed Sampling for Dense Sensor Networks: A "Bit-Conservation Principle" , 2003, IPSN.

[20]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[21]  K. Ramchandran,et al.  Rate-constrained robust estimation for unreliable sensor networks , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..