Scalable and Efficient Pattern Recognition Classifier for WSN

Nomica Imran; Asad Khan

Mobile and Ubiquitous Systems: Computing, Networking, and Services. 7th International ICST Conference, MobiQuitous 2010, Sydeny, Australia, December 6-9, 2010, Revised Selected Papers

Research Article

Scalable and Efficient Pattern Recognition Classifier for WSN

Download

409 downloads

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-642-29154-8_26,
    author={Nomica Imran and Asad Khan},
    title={Scalable and Efficient Pattern Recognition Classifier for WSN},
    proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 7th International ICST Conference, MobiQuitous 2010, Sydeny, Australia, December 6-9, 2010, Revised Selected Papers},
    proceedings_a={MOBIQUITOUS},
    year={2012},
    month={10},
    keywords={},
    doi={10.1007/978-3-642-29154-8_26}
}

Nomica Imran
Asad Khan
Year: 2012
Scalable and Efficient Pattern Recognition Classifier for WSN
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-642-29154-8_26

Nomica Imran¹^,*, Asad Khan¹^,*

1: Monash University

*Contact email: nomicac@infotech.monash.edu.au, asad.khan@infotech.monash.edu.au

Abstract

We present a light-weight event classification scheme, called Identifier based Graph Neuron (IGN). This scheme is based on highly distributed associative memory. The local state of an event is recognize through assigned identifiers. These nodes run an iterative algorithm to coordinate with other nodes to reach a consensus about the global state of the event. The proposed approach not only conserves the power resources of sensor nodes but is also effectively scalable to large scale WSNs.

Published: 2012-10-10

: http://dx.doi.org/10.1007/978-3-642-29154-8_26