De Castro Mota, JF;
Zimos, E;
Rodrigues, M;
Deligiannis, N;
(2016)
Internet-of-things data aggregation using compressed sensing with side information.
In:
2016 23rd International Conference on Telecommunications (ICT).
IEEE: Thessaloniki.
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Abstract
The Internet-of-Things (IoT) is the key enabling technology for transforming current urban environments into so-called Smart Cities. One of the goals behind making cities smarter is to provide a healthy environment that improves the citizens' quality of life and wellbeing. In this work, we introduce a novel data aggregation mechanism tailored to the application of large-scale air pollution monitoring with IoT devices. Our design exploits the intra- and inter-source correlations among air-pollution data using the framework of compressed sensing with side information. The proposed method delivers significant improvements in the data reconstruction quality with respect to the state of the art, even in the presence of noise when measuring and transmitting the data.
| Type: | Proceedings paper |
|---|---|
| Title: | Internet-of-things data aggregation using compressed sensing with side information |
| Event: | International Conference on Telecommunications (ICT) |
| ISBN-13: | 978-1-5090-1990-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICT.2016.7500418 |
| Publisher version: | http://dx.doi.org/10.1109/ICT.2016.7500418 |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Internet of Things; air pollution measurement; compressed sensing; correlation methods; data aggregation; environmental monitoring (geophysics); environmental science computing; smart cities; Internet-of-Things data aggregation; IoT devices; air-pollution data; citizen quality of life improvement; citizen wellbeing improvement; compressed sensing; data reconstruction quality improvements; data transmission; inter-source correlations; intra-source correlations; large-scale air pollution monitoring; side information; smart cities; Compressed sensing; Correlation; Noise measurement; Pollution measurement; Sensors; Wireless sensor networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1529226 |
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