Shape-based clustering in wireless sensor networks

Ijeoma Okeke; Fabio Verdicchio

doi:10.1109/ICSENS.2017.8233989

Shape-based clustering in wireless sensor networks

Ijeoma Okeke, Fabio Verdicchio

Engineering

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

2 Citations (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 language	English
Title of host publication	IEEE SENSORS 2017 - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-3
Number of pages	3
Volume	2017-December
ISBN (Electronic)	9781509010127
DOIs	https://doi.org/10.1109/ICSENS.2017.8233989
Publication status	Published - 21 Dec 2017
Event	16th IEEE SENSORS Conference, ICSENS 2017 - Glasgow, United Kingdom Duration: 30 Oct 2017 → 1 Nov 2017

Conference

Conference	16th IEEE SENSORS Conference, ICSENS 2017
Country/Territory	United Kingdom
City	Glasgow
Period	30/10/17 → 1/11/17

Keywords

event detection
sensor clustering
similarity metric

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10.1109/ICSENS.2017.8233989Licence: Unspecified

Cite this

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title = "Shape-based clustering in wireless sensor networks",

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.",

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author = "Ijeoma Okeke and Fabio Verdicchio",

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AU - Verdicchio, Fabio

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AB - 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.

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KW - sensor clustering

KW - similarity metric

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DO - 10.1109/ICSENS.2017.8233989

M3 - Published conference contribution

AN - SCOPUS:85044301810

VL - 2017-December

SP - 1

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BT - IEEE SENSORS 2017 - Conference Proceedings

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T2 - 16th IEEE SENSORS Conference, ICSENS 2017

Y2 - 30 October 2017 through 1 November 2017

ER -

Shape-based clustering in wireless sensor networks

Abstract

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Keywords

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