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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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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. |
format | Online Article Text |
id | pubmed-8264483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
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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