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Spatio-temporal estimation with Bayesian maximum entropy and compressive sensing in communication constrained networks
conference contribution
posted on 2014-01-01, 00:00 authored by Sutharshan RajasegararSutharshan Rajasegarar, C Leckie, M PalaniswamiLarge scale monitoring applications require large numbers of sensors deployed in a region for accurate and high resolution spatio-temporal measurements and estimation. This can be achieved practically by deploying a mix of high capacity, high precision, expensive and low capacity, low precision, cheap sensors in the monitored region. However, the resource constrained nature of low-capacity sensors, and the availability of limited numbers of high-capacity sensors are a challenge to achieving highly accurate estimations. In this paper we propose a framework combining Bayesian compressive sensing and a robust Bayesian maximum entropy based spatio-temporal estimation technique to address this important problem. Evaluation on real wireless sensor network data reveals the trade-off between the spatio-temporal estimation accuracy and the communication overhead incurred in the network, and provides a mechanism to choose the right compressive ratios, such that a given estimation accuracy is achieved for a known communication overhead in the network.
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Event
IEEE Communications Society. Conference (2014 : Sydney, New South Wales)Series
IEEE Communications Society ConferencePagination
4536 - 4541Publisher
Institute of Electrical and Electronics EngineersLocation
Sydney, N.S.W.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2014-06-10End date
2014-06-14ISSN
1550-3607eISSN
1938-1883ISBN-13
9781479920037Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IEEE ICC 2014 : Communications: the centrepoint of the digital economy : Proceedings of the 2014 IEEE International Conference on CommunicationsUsage metrics
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