Graphical Model Theory for Wireless Sensor Networks
- LBNL Library
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
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS
CLASSIFICATION
COMPRESSION
DISTRIBUTION
EXPERT SYSTEMS
GRAPHICAL MODEL THEORY SENSOR FUSION JUNCTION TREE ALGORITHM DETECTION ESTIMATION AND CLASSIFICATION DISTRIBUTED SENSING AND CONTROL
LEARNING
MINIMIZATION
PROBABILITY
PROCESSING
VALIDATION