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, 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 Berkeley research lab.

IPSN04 - Distributed regression.pdf
PDF file

In  IPSN '04: Conference on Information Processing in Sensor Networks

Publisher  ACM
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. IPSN’04, April 26–27, 2004, Berkeley, California, USA. Copyright 2004 ACM 1-58113-846-6/04/0004


> Publications > Distributed Regression: an Efficient Framework for Modeling Sensor Network Data