Scalable and efficient pattern recognition classifier for WSN

Nomica Imran; Asad I. Khan

doi:10.1007/978-3-642-29154-8_26

Scalable and efficient pattern recognition classifier for WSN

Nomica Imran, Asad I. Khan

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Other › peer-review

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 locally 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.

Original language	English
Title of host publication	Mobile and Ubiquitous Systems: Computing, Networking, and Services
Subtitle of host publication	7th International ICST Conference, MobiQuitous 2010 Sydney, Australia, December 6-9, 2010, Revised Selected Papers
Editors	Patrick Sénac, Max Ott, Aruna Seneviratne
Place of Publication	Heidelberg, Berlin
Publisher	Springer
Pages	303-304
Number of pages	2
ISBN (Electronic)	9783642291548
ISBN (Print)	9783642291531
DOIs	https://doi.org/10.1007/978-3-642-29154-8_26
Publication status	Published - 2012
Event	International Conference on Mobile and Ubiquitous Systems: Networks and Services 2010 - Sydney, Australia Duration: 6 Dec 2010 → 9 Dec 2010 Conference number: 7th https://link.springer.com/book/10.1007/978-3-642-29154-8

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Publisher	Springer
Volume	73 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	International Conference on Mobile and Ubiquitous Systems: Networks and Services 2010
Abbreviated title	MobiQuitous 2010
Country/Territory	Australia
City	Sydney
Period	6/12/10 → 9/12/10
Internet address	https://link.springer.com/book/10.1007/978-3-642-29154-8

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10.1007/978-3-642-29154-8_26

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Imran, N., & Khan, A. I. (2012). Scalable and efficient pattern recognition classifier for WSN. In P. Sénac, M. Ott, & A. Seneviratne (Eds.), Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010 Sydney, Australia, December 6-9, 2010, Revised Selected Papers (pp. 303-304). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 73 LNICST). Springer. https://doi.org/10.1007/978-3-642-29154-8_26

Imran, Nomica ; Khan, Asad I. / Scalable and efficient pattern recognition classifier for WSN. Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010 Sydney, Australia, December 6-9, 2010, Revised Selected Papers. editor / Patrick Sénac ; Max Ott ; Aruna Seneviratne. Heidelberg, Berlin : Springer, 2012. pp. 303-304 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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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 locally 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.",

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booktitle = "Mobile and Ubiquitous Systems: Computing, Networking, and Services",

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Imran, N & Khan, AI 2012, Scalable and efficient pattern recognition classifier for WSN. in P Sénac, M Ott & A Seneviratne (eds), Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010 Sydney, Australia, December 6-9, 2010, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 73 LNICST, Springer, Heidelberg, Berlin, pp. 303-304, International Conference on Mobile and Ubiquitous Systems: Networks and Services 2010, Sydney, New South Wales, Australia, 6/12/10. https://doi.org/10.1007/978-3-642-29154-8_26

Scalable and efficient pattern recognition classifier for WSN. / Imran, Nomica; Khan, Asad I.

Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010 Sydney, Australia, December 6-9, 2010, Revised Selected Papers. ed. / Patrick Sénac; Max Ott; Aruna Seneviratne. Heidelberg, Berlin : Springer, 2012. p. 303-304 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 73 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Other › peer-review

TY - CHAP

T1 - Scalable and efficient pattern recognition classifier for WSN

AU - Imran, Nomica

AU - Khan, Asad I.

N1 - Conference code: 7th

PY - 2012

Y1 - 2012

N2 - 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 locally 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.

AB - 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 locally 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.

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U2 - 10.1007/978-3-642-29154-8_26

DO - 10.1007/978-3-642-29154-8_26

M3 - Chapter (Book)

AN - SCOPUS:84890087980

SN - 9783642291531

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 303

EP - 304

BT - Mobile and Ubiquitous Systems: Computing, Networking, and Services

A2 - Sénac, Patrick

A2 - Ott, Max

A2 - Seneviratne, Aruna

PB - Springer

CY - Heidelberg, Berlin

T2 - International Conference on Mobile and Ubiquitous Systems: Networks and Services 2010

Y2 - 6 December 2010 through 9 December 2010

ER -

Imran N, Khan AI. Scalable and efficient pattern recognition classifier for WSN. In Sénac P, Ott M, Seneviratne A, editors, Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010 Sydney, Australia, December 6-9, 2010, Revised Selected Papers. Heidelberg, Berlin: Springer. 2012. p. 303-304. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). https://doi.org/10.1007/978-3-642-29154-8_26

Scalable and efficient pattern recognition classifier for WSN

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