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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
3 Downloads (Pure)

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 self-adaptive, 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)
PublisherIEEE Computer Society
Pages61-70
ISBN (Electronic)9781467375351
DOIs
Publication statusPublished - 29 Oct 2015
EventNinth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) - Boston Marriott Cambridge, Cambridge, MA, United States
Duration: 21 Sept 201525 Sept 2015
http://saso2015.mit.edu/

Publication series

NameIEEE International Conference on Self-Adaptive and Self-Organizing Systems
ISSN (Print)1949-3673
ISSN (Electronic)1949-3681

Conference

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

Keywords

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

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