Distributed data stream classification for wireless sensor networks

Mohamed Medhat Gaber, Ary Mazharuddin Shiddiqi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

It has been established experimentally that in-network processing in wireless sensor networks is the acceptable mode of operation. However, this solution is faced by resource constraints of the sensor nodes, especially when running traditional data mining techniques that tend to consume the resources rapidly. On the other hand, data stream mining algorithms still fall short with the limited computational capabilities of the nodes. These algorithms need real-time adaptation to availability of resources. Distributed processing is also essential to produce a global model of the data streams emanated from the network. In this paper, we propose a novel distributed data stream classification technique that is able to adapt to availability of resources in wireless sensor networks.

Original languageEnglish
Title of host publicationAPPLIED COMPUTING 2010 - The 25th Annual ACM Symposium on Applied Computing
Pages1629-1630
Number of pages2
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event25th Annual ACM Symposium on Applied Computing, SAC 2010 - Sierre, Switzerland
Duration: 22 Mar 201026 Mar 2010

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference25th Annual ACM Symposium on Applied Computing, SAC 2010
Country/TerritorySwitzerland
CitySierre
Period22/03/1026/03/10

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