Graphical Model Theory for Wireless Sensor Networks
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
- Research Organization:
- Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US) (US)
- Sponsoring Organization:
- USDOE. Office of Science (US)
- DOE Contract Number:
- AC03-76SF00098
- OSTI ID:
- 833692
- Report Number(s):
- LBNL-53452; R&D Project: RGNVD9 AND RGNVF9; TRN: US0406752
- Resource Relation:
- Other Information: PBD: 8 Dec 2002
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
CLASSIFICATION
COMPRESSION
DISTRIBUTION
EXPERT SYSTEMS
LEARNING
MINIMIZATION
PROBABILITY
PROCESSING
VALIDATION
GRAPHICAL MODEL THEORY SENSOR FUSION JUNCTION TREE ALGORITHM DETECTION ESTIMATION AND CLASSIFICATION DISTRIBUTED SENSING AND CONTROL