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Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events

Smart cities use Information and Communication Technologies (ICT) to enrich existing public services and to improve citizens’ quality of life. In this scenario, Mobile CrowdSensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. MCS allow people roamin...

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Autores principales: Foschini, Luca, Martuscelli, Giuseppe, Montanari, Rebecca, Solimando, Michele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264483/
https://www.ncbi.nlm.nih.gov/pubmed/34254006
http://dx.doi.org/10.1007/s10723-021-09569-9
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author Foschini, Luca
Martuscelli, Giuseppe
Montanari, Rebecca
Solimando, Michele
author_facet Foschini, Luca
Martuscelli, Giuseppe
Montanari, Rebecca
Solimando, Michele
author_sort Foschini, Luca
collection PubMed
description Smart cities use Information and Communication Technologies (ICT) to enrich existing public services and to improve citizens’ quality of life. In this scenario, Mobile CrowdSensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. MCS allow people roaming around with their smart devices to collectively sense, gather, and share data, thus leveraging the possibility to capture the pulse of the city. That can be very helpful in emergency scenarios, such as the COVID-19 pandemic, that require to track the movement of a high number of people to avoid risky situations, such as the formation of crowds. In fact, using mobility traces gathered via MCS, it is possible to detect crowded places and suggest people safer routes/places. In this work, we propose an edge-anabled mobile crowdsensing platform, called ParticipAct, that exploits edge nodes to compute possible dangerous crowd situations and a federated blockchain network to store reward states. Edge nodes are aware of all critical situation in their range and can warn the smartphone client with a smart push notification service that avoids firing too many messages by adapting the warning frequency according to the transport and the specific subarea in which clients are located.
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spelling pubmed-82644832021-07-08 Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events Foschini, Luca Martuscelli, Giuseppe Montanari, Rebecca Solimando, Michele J Grid Comput Article Smart cities use Information and Communication Technologies (ICT) to enrich existing public services and to improve citizens’ quality of life. In this scenario, Mobile CrowdSensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. MCS allow people roaming around with their smart devices to collectively sense, gather, and share data, thus leveraging the possibility to capture the pulse of the city. That can be very helpful in emergency scenarios, such as the COVID-19 pandemic, that require to track the movement of a high number of people to avoid risky situations, such as the formation of crowds. In fact, using mobility traces gathered via MCS, it is possible to detect crowded places and suggest people safer routes/places. In this work, we propose an edge-anabled mobile crowdsensing platform, called ParticipAct, that exploits edge nodes to compute possible dangerous crowd situations and a federated blockchain network to store reward states. Edge nodes are aware of all critical situation in their range and can warn the smartphone client with a smart push notification service that avoids firing too many messages by adapting the warning frequency according to the transport and the specific subarea in which clients are located. Springer Netherlands 2021-07-08 2021 /pmc/articles/PMC8264483/ /pubmed/34254006 http://dx.doi.org/10.1007/s10723-021-09569-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Foschini, Luca
Martuscelli, Giuseppe
Montanari, Rebecca
Solimando, Michele
Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events
title Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events
title_full Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events
title_fullStr Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events
title_full_unstemmed Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events
title_short Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events
title_sort edge-enabled mobile crowdsensing to support effective rewarding for data collection in pandemic events
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8264483/
https://www.ncbi.nlm.nih.gov/pubmed/34254006
http://dx.doi.org/10.1007/s10723-021-09569-9
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