Name
Title | ||
|---|---|---|
Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks |
Abstract | ||
|---|---|---|
Collaborative mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this article, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: 1) a platform-based strategy where the platform exploits ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2021.3086410 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Task analysis,Recruitment,Crowdsourcing,Resource management,Social networking (online),Genetic algorithms,Simulation | Journal | 9 |
Issue | ISSN | Citations |
1 | 2327-4662 | 2 |
PageRank | References | Authors |
0.43 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aymen Hamrouni | 1 | 6 | 2.23 |
| Hakim Ghazzai | 2 | 181 | 34.97 |
| Turki Alelyani | 3 | 6 | 1.94 |
| Yehia Massoud | 4 | 772 | 113.05 |