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Reasoning in Event-Based Distributed Systems pp 195–216Cite as

Pattern Detection in Extremely Resource-Constrained Devices

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 347))

Abstract

Pervasive computing anticipates a future with billions of data producing devices of varying capabilities integrated into everyday objects or deployed in the physical world. In event-based systems, such devices are required to make timely autonomous decisions in response to occurrences, situations or states. Purely decentralised pattern detection in systems that lack time synchronisation, reliable communication links and continuous power remains an active and open research area. We review challenges and solutions for pattern detection in distributed networked sensing systems without a reliable core infrastructure. Specifically, we discuss localised pattern detection in resource-constrained devices that compriseWireless Sensor and Actuator Networks. We focus on online data mining, statistical and machine learning approaches that aim to augment decentralised pattern detection and illustrate the properties of this new computing paradigm that requires stability and robustness while accommodating severe resource limitations and frequent failures.

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Authors and Affiliations

  1. Birkbeck College, University of London, WC1N 3QS, UK

    Michael Zoumboulakis & George Roussos

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  1. Michael Zoumboulakis
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  2. George Roussos
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Editors and Affiliations

  1. Dept. of Computer Science and Inf. Systems, University of London, Birkbeck Malet Street, WC1E 7HX, London, UK

    Sven Helmer

  2. London Knowledge Lab, University of London , Birkbeck 23-29 Emerald Street, WC1N 3QS, London, UK

    Alexandra Poulovassilis  & Fatos Xhafa  & 

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Zoumboulakis, M., Roussos, G. (2011). Pattern Detection in Extremely Resource-Constrained Devices. In: Helmer, S., Poulovassilis, A., Xhafa, F. (eds) Reasoning in Event-Based Distributed Systems. Studies in Computational Intelligence, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19724-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-19724-6_9

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  • Online ISBN: 978-3-642-19724-6

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