Griego, D, Buff, V, Hayoz, E et al. (2 more authors) (2017) Sensing and Mining Urban Qualities in Smart Cities. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA). 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), 27-29 Mar 2017, Taipei, Taiwan. IEEE , pp. 1004-1011. ISBN 978-1-5090-6029-0
Abstract
The emergence of the Internet of Things in Smart Cities questions how the future citizens will perceive their predominant living and working environments and what quality of living they can experience within it, for instance the level of everyday stress. However, perception and experienced stress levels are challenging metrics to measure and are even more challenging to correlate with an underlying causal-effectual relationship in such stimulus abundant environments. The Internet of Things, enabled by several pervasive and ubiquitous devices such as smart phones and smart sensors, can provide real-time contextual information that can be used by advanced data science methodologies to generate new insights about urban qualities in Smart Cities and how they can be improved. The goal of this study is to show the predominant factors, which influence perceptual qualities of inhabitants in a Smart City equipped with sensing capabilities by the Internet of Things. To serve this goal, a novel data collection process for Smart Cities is introduced that involves (i) environmental data, such noise, dust, illuminance, temperature, relative humidity, (ii) location/mobility data, such as GNSS and citizens density detected via WiFi, and (iii) perceptual social data collected by citizens' responses in smart phones. These fine-grained real-time data can provide invaluable insights about the spatial correlations of the sensor measurements as well as the spatial and citizens' similarity illustrated. The data analysis illustrated reveals significant links between stress level and environmental changes observed.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 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. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Internet of Things , Smart City , urban quality , environment , stress , sensing , sensor , data analytics |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Apr 2021 13:35 |
Last Modified: | 13 Apr 2021 14:08 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/aina.2017.14 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172734 |