Predictive learning in sensor networks

• Type: Chapter or Part of a Book
• Date: 2007

Authors

João Gama (Author) FEP; Pedersen, RU (Author) Other

Book

Title: Learning from Data Streams: Processing Techniques in Sensor Networks

Pages: 143-164

ISBN: 9783540736783

Electronic ISBN: 978-3-540-73679-0

Publisher: Springer

Indexing

Scopus - 8 Citations

Other information

Authenticus ID: P-009-CNE

DOI: 10.1007/3-540-73679-4_10

Abstract (EN): Sensor networks act in dynamic environments with distributed sources of continuous data and computing with resource constraints. Learning in these environments is faced with new challenges: the need to continuously maintain a decision model consistent with the most recent data. Desirable properties of learning algorithms include: the ability to maintain an any time model; the ability to modify the decision model whenever new information is available; the ability to forget outdated information; and the ability to detect and react to changes in the underlying process generating data, monitoring the learning process and managing the trade-off between the cost of updating a model and the benefits in performance gains. In this chapter we illustrate these ideas in two learning scenarios - centralized and distributed - and present illustrative algorithms for these contexts. © 2007 Springer-Verlag Berlin Heidelberg.

Language: English

Type (Professor's evaluation): Scientific