Towards data-centric control of sensor networks through Bayesian dynamic linear modelling
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.
Citation
Fang , L & Dobson , S A 2015 , Towards data-centric control of sensor networks through Bayesian dynamic linear modelling . in 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) . IEEE International Conference on Self-Adaptive and Self-Organizing Systems , IEEE Computer Society , pp. 61-70 , Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) , Cambridge, MA , United States , 21/09/15 . https://doi.org/10.1109/SASO.2015.14 conference
Publication
2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
ISSN
1949-3673Type
Conference item
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