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Active wireless sensing for rapid information retrieval in sensor networks
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Source Information Processing In Sensor Networks archive
Proceedings of the 5th international conference on Information processing in sensor networks table of contents
Nashville, Tennessee, USA
SESSION: Main track--sensor tasking and data retrieval table of contents
Pages: 85 - 92  
Year of Publication: 2006
ISBN:1-59593-334-4
Authors
Thiagarajan Sivanadyan  University of Wisconsin, Madison, WI
Akbar Sayeed  University of Wisconsin, Madison, WI
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Most existing information extraction schemes in sensor networks rely on in-network processing that requires information routing and coordination between sensor nodes and incurs a corresponding overhead in delay and energy consumption. In this paper, we propose a viable alternative – Active Wireless Sensing (AWS) – in which a Wireless Information Retriever (WIR)queries a select ensemble of nodes to obtain desired information in a rapid and energy-efficient manner. AWS has two primary attributes: i) the sensor nodes are "dumb" in that they have limited computational ability, and ii) the WIR is computationally powerful, is equipped with an antenna array, and directly interrogates the sensor ensemble with wideband space-time waveforms. AWS is inspired by an intimate connection with communication over multipath channels: the sensor nodes act as active scatterers and produce a multipath response to the WIR's interrogation signals. We develop the basic communication architecture in AWS and explore various signaling and reception strategies at the WIR, and encoding strategies at the sensors. The basic communication architecture is quite flexible and can cater to a variety of information retrieval tasks. In particular, we illustrate the framework in two extreme information retrieval tasks: high-rate information retrieval corresponding to distributed independent sensor measurements, and low-rate retrieval corresponding to localized correlated measurements. A low-complexity interference suppression technique is proposed for significantly increasing the capacity and reliability of high-rate information retrieval. Performance analysis reveals a fundamental rate versus reliability tradeoff in AWS and is illustrated with accompanying simulation results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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D. Estrin, L. Girod, G. Pottie and M. Srivastava, "Instrumenting the world with wireless sensor networks," Proc. ICASSP 2001.
 
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IEEE J. Select. Areas Commun., Special Issues on Sensor Networks, August 2004, April 2005.
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B. Ananthasubramaniam and U. Madhow, "Detection and Localization of Events in Imaging Sensor Nets", Proc. IEEE ISIT 2005.
 
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A. Sayeed, "Deconstructing Multi-antenna Fading Channels", IEEE Trans. on Signal Processing, Oct. 2002.
 
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A. Sayeed, "A Virtual Representation for Time- and Frequency-Selective Correlated MIMO Channels," Proc. IEEE ICASSP 2003.
 
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A. Sayeed, V. Raghavan, and J. Kotecha, "Capacity of Space-Time Wireless Channels: A Physical Perspective," Proc. IEEE ITW 2004.
 
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J. Proakis, Digital Communications, 3rd Ed., Prentice Hall.
 
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Collaborative Colleagues:
Thiagarajan Sivanadyan: colleagues
Akbar Sayeed: colleagues