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Towards data-centric control of sensor networks through Bayesian dynamic linear modelling
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dc.contributor.author | Fang, Lei | |
dc.contributor.author | Dobson, Simon Andrew | |
dc.date.accessioned | 2015-09-27T23:11:07Z | |
dc.date.available | 2015-09-27T23:11:07Z | |
dc.date.issued | 2015-10-29 | |
dc.identifier | 203227893 | |
dc.identifier | 52d5aec4-b8e2-4f45-a4d9-e7ed58a2dd2c | |
dc.identifier | 84959288689 | |
dc.identifier | 000377090600007 | |
dc.identifier.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 | en |
dc.identifier.citation | conference | en |
dc.identifier.isbn | 9781467375351 | |
dc.identifier.issn | 1949-3673 | |
dc.identifier.other | ORCID: /0000-0001-9633-2103/work/70234200 | |
dc.identifier.uri | https://hdl.handle.net/10023/7546 | |
dc.description.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. | |
dc.format.extent | 1407647 | |
dc.language.iso | eng | |
dc.publisher | IEEE Computer Society | |
dc.relation.ispartof | 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) | en |
dc.relation.ispartofseries | IEEE International Conference on Self-Adaptive and Self-Organizing Systems | en |
dc.subject | Self management | en |
dc.subject | Adaptive sampling | en |
dc.subject | Sensor networks | en |
dc.subject | Machine learning | en |
dc.subject | Energy efficiency | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | NDAS | en |
dc.subject | BDC | en |
dc.subject | R2C | en |
dc.subject | ~DC~ | en |
dc.subject | SDG 7 - Affordable and Clean Energy | en |
dc.subject.lcc | QA75 | en |
dc.title | Towards data-centric control of sensor networks through Bayesian dynamic linear modelling | en |
dc.type | Conference item | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.identifier.doi | 10.1109/SASO.2015.14 | |
dc.date.embargoedUntil | 2015-09-28 |
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