Pournaras, E orcid.org/0000-0003-3900-2057, Ghulam, AN, Kunz, R et al. (1 more author) (2022) Crowd Sensing and Living Lab Outdoor Experimentation Made Easy. IEEE Pervasive Computing, 21 (1). pp. 18-27. ISSN 1536-1268
Abstract
Living lab outdoor experimentation using pervasive computing provides new opportunities: higher realism, external validity, and socio-spatio-temporal observations in large scale. However, experimentation “in the wild” is complex and costly. Noise, biases, privacy concerns, compliance with standards of ethical review boards, remote moderation, control of experimental conditions, and equipment perplex the collection of high-quality data for causal inference. This article introduces Smart Agora, a novel open-source software platform for rigorous systematic outdoor experimentation. Without writing a single line of code, highly complex experimental scenarios are visually designed and automatically deployed to smart phones. Novel geolocated survey and sensor data are collected subject of participants verifying desired experimental conditions, for instance, their localization at certain urban spots. This new approach drastically improves the quality and purposefulness of crowd sensing, tailored to conditions that confirm/reject hypotheses. The features that support this innovative functionality and the broad spectrum of its applicability are demonstrated.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. This is an author produced version of a paper published in IEEE Pervasive Computing. Uploaded in accordance with the publisher's self-archiving policy. 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. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Swiss National Science Foundation Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Nov 2021 12:16 |
Last Modified: | 26 Jul 2022 11:16 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/mprv.2021.3116466 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180938 |