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Graphical Model Theory for Wireless Sensor Networks.

DE2004833692

Publication Date	2004
Personal Author	Davis, W. B.
Page Count	22
Abstract	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.
Keywords	Information processing Communications networks Algorithms Probability density functions Wireless sensor networks Graphical model theory
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Berkeley National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200515

Graphical Model Theory for Wireless Sensor Networks.

Graphical Model Theory for Wireless Sensor Networks.
DE2004833692

Publication Date	2004
Personal Author	Davis, W. B.
Page Count	22
Abstract	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.
Keywords	Information processing Communications networks Algorithms Probability density functions Wireless sensor networks Graphical model theory
Source Agency	Technical Information Center Oak Ridge Tennessee
Corporate Authors	Lawrence Berkeley National Lab., CA.; Department of Energy, Washington, DC.
Supplemental Notes	Sponsored by Department of Energy, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	200515