Smart Self-Adaptive Clustering Technique for Collaborative Sensing in IoT Risk Contexts

Zornoza Sánchez-Lozano, Jaime Gonzalo and Mujica Rojas, Gabriel Noe and Portilla Berrueco, Jorge and Riesgo Alcaide, Teresa (2018). Smart Self-Adaptive Clustering Technique for Collaborative Sensing in IoT Risk Contexts. In: "EWSN ’18. 2018 International Conference on Embedded Wireless Systems and Networks", February 14 - 16, 2018, Madrid, España. ISBN 978-0-9949886-2-1. pp. 187-188.

Description

Title: Smart Self-Adaptive Clustering Technique for Collaborative Sensing in IoT Risk Contexts
Author/s:
  • Zornoza Sánchez-Lozano, Jaime Gonzalo
  • Mujica Rojas, Gabriel Noe
  • Portilla Berrueco, Jorge
  • Riesgo Alcaide, Teresa
Item Type: Presentation at Congress or Conference (Poster)
Event Title: EWSN ’18. 2018 International Conference on Embedded Wireless Systems and Networks
Event Dates: February 14 - 16, 2018
Event Location: Madrid, España
Title of Book: EWSN ’18 Proceedings of the 2018 International Conference on Embedded Wireless Systems and Networks
Date: 2018
ISBN: 978-0-9949886-2-1
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this work the integration of a modular and flexible WSN hardware platform with a smart wearable device for IoT application prototyping is leveraged to introduce a new adaptive hybrid clustering technique for mesh collaborative networks. The proposed dynamic dissemination strategy attempts to efficiently support on-site data provision considering the location variability of smart mobile devices relative to deployed stationary sensor nodes, with a particular application use case: risk scenarios.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2014-58036-C4-2-RREBECCAUnspecifiedSISTEMAS ELECTRONICOS EMPOTRADOS CONFIABLES PARA CONTROL DE CIUDADES BAJO SITUACIONES ATIPICAS

More information

Item ID: 55295
DC Identifier: http://oa.upm.es/55295/
OAI Identifier: oai:oa.upm.es:55295
Official URL: https://dl.acm.org/citation.cfm?id=3234847&picked=prox
Deposited by: Memoria Investigacion
Deposited on: 10 Jun 2019 15:25
Last Modified: 10 Jun 2019 15:25
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