DocumentCode
3116620
Title
A Bayesian network approach to energy-aware distributed sensing
Author
Yan, Ruqiang ; Ball, Daniel ; Deshmukh, Abhijit ; Gao, Robert X.
Author_Institution
Dept. of Mech. & Ind. Eng., Massachusetts Univ., Amherst, MA, USA
fYear
2004
fDate
24-27 Oct. 2004
Firstpage
44
Abstract
This paper presents a strategy for the design and implementation of an energy-efficient multi-sensor network, based on the structure of sectioned Bayesian networks. A key issue in the design of Bayesian networks for monitoring engineering systems is to ensure that a reliable inference scheme about the health of the system can be made by combining information acquired from each sensor in the system into a single Bayesian network. However, as the size of the network rapidly grows, aggregating information made by all the sensors becomes computationally intractable. Hence, sectioning of the Bayesian network based on functional or logical constraints allows for improved computational efficiency in aggregating information while reducing the overall communication requirements. This ultimately leads to a reduction of the energy cost which is critical to effective operation of the sensor network.
Keywords
belief networks; distributed sensors; monitoring; sensor fusion; Bayesian network design; communication requirements; computational efficiency; computationally intractable task; energy cost reduction; energy-aware distributed sensing; energy-efficient multi-sensor network; engineering systems monitoring; functional constraints; information aggregation; logical constraints; network size; reliable inference scheme; sectioned Bayesian network structure; sensor information combining; system health; Bayesian methods; Computational efficiency; Computer networks; Design engineering; Energy efficiency; Monitoring; Power engineering and energy; Reliability engineering; Sensor systems; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Sensors, 2004. Proceedings of IEEE
Print_ISBN
0-7803-8692-2
Type
conf
DOI
10.1109/ICSENS.2004.1426095
Filename
1426095
Link To Document