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

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.