Towards data-centric control of sensor networks through Bayesian dynamic linear modelling
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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.
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 , pp. 61-70 , Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) , Cambridge, MA , United States , 21-25 September . DOI: 10.1109/SASO.2015.14conference
2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
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