| Title |
Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events |
| ID_Doc |
44528 |
| Authors |
Foschini, L; Martuscelli, G; Montanari, R; Solimando, M |
| Title |
Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events |
| Year |
2021 |
| Published |
Journal Of Grid Computing, 19, 3 |
| DOI |
10.1007/s10723-021-09569-9 |
| Abstract |
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. |
| Author Keywords |
Edge computing; Mobile crowd sensing; Smart city; Blockchain; Pandemic prevention |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000671340500001 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://link.springer.com/content/pdf/10.1007/s10723-021-09569-9.pdf
|