Shape-based clustering in wireless sensor networks

Ijeoma Okeke, Fabio Verdicchio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A low-complexity algorithm is presented that clusters sensor nodes based on similarity in the sensed signals. This feature makes it an enabler for distributed detection of events that are impossible to identify using information available to a single node. The algorithm does not require system training prior to deployment nor does it assume statistical knowledge of the signal. Experimental results confirm that clusters produced by our algorithm match signal patterns more closely than those formed by a comparatively simple algorithm that minimizes Euclidean distance between signals.

Original languageEnglish
Title of host publicationIEEE SENSORS 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
Volume2017-December
ISBN (Electronic)9781509010127
DOIs
Publication statusPublished - 21 Dec 2017
Event16th IEEE SENSORS Conference, ICSENS 2017 - Glasgow, United Kingdom
Duration: 30 Oct 20171 Nov 2017

Conference

Conference16th IEEE SENSORS Conference, ICSENS 2017
CountryUnited Kingdom
CityGlasgow
Period30/10/171/11/17

Keywords

  • event detection
  • sensor clustering
  • similarity metric

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Shape-based clustering in wireless sensor networks'. Together they form a unique fingerprint.

  • Cite this

    Okeke, I., & Verdicchio, F. (2017). Shape-based clustering in wireless sensor networks. In IEEE SENSORS 2017 - Conference Proceedings (Vol. 2017-December, pp. 1-3). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSENS.2017.8233989