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

Lei Fang; Simon Andrew Dobson

doi:10.1109/SASO.2015.14

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

Lei Fang, Simon Andrew Dobson

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)
Publisher	IEEE Computer Society
Pages	61-70
ISBN (Electronic)	9781467375351
DOIs	https://doi.org/10.1109/SASO.2015.14
Publication status	Published - 29 Oct 2015
Event	Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) - Boston Marriott Cambridge, Cambridge, MA, United States Duration: 21 Sept 2015 → 25 Sept 2015 http://saso2015.mit.edu/

Publication series

Name	IEEE International Conference on Self-Adaptive and Self-Organizing Systems
ISSN (Print)	1949-3673
ISSN (Electronic)	1949-3681

Conference

Conference	Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015)
Country/Territory	United States
City	Cambridge, MA
Period	21/09/15 → 25/09/15
Internet address	http://saso2015.mit.edu/

Keywords

Self management
Adaptive sampling
Sensor networks
Machine learning
Energy efficiency

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SASO.2015.14

dlm-saso15
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.
Accepted author manuscript, 1.34 MB

Simon Andrew Dobson, FRSE
- Simon.Dobsonst-andrews.acuk
- School of Computer Science - Deputy Head of School, Professor
- Sir James Mackenzie Institute for Early Diagnosis
- Centre for Interdisciplinary Research in Computational Algebra
- Centre for Research into Ecological & Environmental Modelling
Person: Academic

Cite this

@inproceedings{52d5aec4b8e24f45a4d9e7ed58a2dd2c,

title = "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.",

keywords = "Self management, Adaptive sampling, Sensor networks, Machine learning, Energy efficiency",

author = "Lei Fang and Dobson, {Simon Andrew}",

year = "2015",

month = oct,

day = "29",

doi = "10.1109/SASO.2015.14",

language = "English",

series = "IEEE International Conference on Self-Adaptive and Self-Organizing Systems",

publisher = "IEEE Computer Society",

pages = "61--70",

booktitle = "2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)",

address = "United States",

note = "Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015) ; Conference date: 21-09-2015 Through 25-09-2015",

url = "http://saso2015.mit.edu/",

}

Fang, L & Dobson, SA 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

Towards data-centric control of sensor networks through Bayesian dynamic linear modelling. / Fang, Lei ; Dobson, Simon Andrew.
2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). IEEE Computer Society, 2015. p. 61-70 (IEEE International Conference on Self-Adaptive and Self-Organizing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Towards data-centric control of sensor networks through Bayesian dynamic linear modelling

AU - Fang, Lei

AU - Dobson, Simon Andrew

PY - 2015/10/29

Y1 - 2015/10/29

N2 - 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.

AB - 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.

KW - Self management

KW - Adaptive sampling

KW - Sensor networks

KW - Machine learning

KW - Energy efficiency

U2 - 10.1109/SASO.2015.14

DO - 10.1109/SASO.2015.14

M3 - Conference contribution

T3 - IEEE International Conference on Self-Adaptive and Self-Organizing Systems

SP - 61

EP - 70

BT - 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

PB - IEEE Computer Society

T2 - Ninth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2015)

Y2 - 21 September 2015 through 25 September 2015

ER -

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

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Profiles

Simon Andrew Dobson, FRSE

Cite this