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Smart Self-Adaptive Clustering Technique for Collaborative Sensing in IoT Risk Contexts
Citation
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: |
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| 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 |
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
Type
Code
Acronym
Leader
Title
Government of Spain
TEC2014-58036-C4-2-R
REBECCA
Unspecified
SISTEMAS ELECTRONICOS EMPOTRADOS CONFIABLES PARA CONTROL DE CIUDADES BAJO SITUACIONES ATIPICAS
More information
| Item ID: | 55295 |
|---|---|
| DC Identifier: | https://oa.upm.es/55295/ |
| OAI Identifier: | oai:oa.upm.es:55295 |
| Official URL: | https://dl.acm.org/citation.cfm?id=3234847&picked=... |
| Deposited by: | Memoria Investigacion |
| Deposited on: | 10 Jun 2019 15:25 |
| Last Modified: | 10 Jun 2019 15:25 |
