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Towards data-centric control of sensor networks through Bayesian dynamic linear modelling

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Abstract

Wireless sensor networks usually operate in dynamic, stochastic environments. While the behaviour of individual nodes is important, they are better seen as contributors to a larger mission, and managing the sensing quality and
performance of these missions requires a range of online decisions to adapt to changing conditions. In this paper we propose an selfadaptive, self-managing and self-optimising sensing framework grounded in Bayesian dynamic linear models. Experimental results show that this solution can make sound scheduling
decisions while also minimising energy usage.
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Original languageEnglish
Title of host publication2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
PublisherIEEE
Pages61-70
DOIs
StatePublished - 21 Sep 2015
EventNinth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) - Cambridge, MA, United States

Conference

ConferenceNinth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015)
CountryUnited States
CityCambridge, MA
Period21/09/1525/09/15
Internet address

    Research areas

  • Self management, Adaptive sampling, Sensor networks, Machine learning, Energy efficiency

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