Distributed regression an efficient framework for modeling sensor network data

We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, w...

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Vydáno v:IPSN 2004 : Third International Symposium on Information Processing in Sensor Networks, April 26-27, 2004, Berkeley, California, USA s. 1 - 10
Hlavní autoři: Guestrin, Carlos, Bodik, Peter, Thibaux, Romain, Paskin, Mark, Madden, Samuel
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 26.04.2004
IEEE
Edice:ACM Conferences
Témata:
Artificial intelligence
Computer architecture
Computer networks
Computer systems organization > Architectures > Distributed architectures
Computer systems organization > Architectures > Other architectures > Special purpose systems
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Cooperation and coordination
Computing methodologies > Machine learning > Learning paradigms
Computing methodologies > Machine learning > Machine learning approaches > Factorization methods > Canonical correlation analysis
Computing methodologies > Machine learning > Machine learning approaches > Markov decision processes
Concurrent computing
Distributed computing
Machine learning
Machine learning algorithms
Mathematics of computing > Probability and statistics > Multivariate statistics
Mathematics of computing > Probability and statistics > Statistical paradigms > Regression analysis
Permission
Remote monitoring
Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles > Organizing principles for web applications
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
Wireless sensor networks
wireless sensor networks
machine learning
distributed algorithms
regression
ISBN:1581138466, 9781581138467
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Popis
Shrnutí:We present distributed regression, an efficient and general framework for in-network modeling of sensor data. In this framework, the nodes of the sensor network collaborate to optimally fit a global function to each of their local measurements. The algorithm is based upon kernel linear regression, where the model takes the form of a weighted sum of local basis functions; this provides an expressive yet tractable class of models for sensor network data. Rather than transmitting data to one another or outside the network, nodes communicate constraints on the model parameters, drastically reducing the communication required. After the algorithm is run, each node can answer queries for its local region, or the nodes can efficiently transmit the parameters of the model to a user outside the network. We present an evaluation of the algorithm based upon data from a 48-node sensor network deployment at the Intel Research - Berkeley Lab, demonstrating that our distributed algorithm converges to the optimal solution at a fast rate and is very robust to packet losses.
Bibliografie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:1581138466
9781581138467
DOI:10.1145/984622.984624