Towards data-centric control of sensor networks through Bayesian dynamic linear modelling
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
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.
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.2015.14conference
2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.