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

Research output: ResearchConference contribution


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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.


Original languageEnglish
Title of host publication2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
StatePublished - 21 Sep 2015
EventNinth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) - Boston Marriott Cambridge, Cambridge, MA, United States
Duration: 21 Sep 201525 Sep 2015


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

    Research areas

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

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ID: 203227893