TY - GEN
T1 - Real-time cross online matching in spatial crowdsourcing
AU - Cheng, Yurong
AU - Li, Boyang
AU - Zhou, Xiangmin
AU - Yuan, Ye
AU - Wang, Guoren
AU - Chen, Lei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - With the development of mobile communication techniques, spatial crowdsourcing has become popular recently. A typical topic of spatial crowdsourcing is task assignment, which assigns crowd workers to users' requests in real time and maximizes the total revenue. However, it is common that the available crowd workers over a platform are too far away to serve the requests, so some user requests may be rejected or responded at high money cost after long waiting. Fortunately, the neighbors of a platform usually have available resources for the same services. Collaboratively conducting the task allocation among different platforms can greatly improve the quality of services, but have not been investigated yet. In this paper, we propose a Cross Online Matching (COM), which enables a platform to "borrow" unoccupied crowd workers from other platforms for completing the user requests. We propose two algorithms, deterministic cross online matching (DemCOM) and randomized cross online matching (RamCom) for COM. DemCOM focuses on the largest obtained revenue in a greedy manner, while RamCom considers the trade-off between the obtained revenue and the probability of request being accepted by the borrowed workers. Extensive experimental results verify the effectiveness and efficiency of our algorithms.
AB - With the development of mobile communication techniques, spatial crowdsourcing has become popular recently. A typical topic of spatial crowdsourcing is task assignment, which assigns crowd workers to users' requests in real time and maximizes the total revenue. However, it is common that the available crowd workers over a platform are too far away to serve the requests, so some user requests may be rejected or responded at high money cost after long waiting. Fortunately, the neighbors of a platform usually have available resources for the same services. Collaboratively conducting the task allocation among different platforms can greatly improve the quality of services, but have not been investigated yet. In this paper, we propose a Cross Online Matching (COM), which enables a platform to "borrow" unoccupied crowd workers from other platforms for completing the user requests. We propose two algorithms, deterministic cross online matching (DemCOM) and randomized cross online matching (RamCom) for COM. DemCOM focuses on the largest obtained revenue in a greedy manner, while RamCom considers the trade-off between the obtained revenue and the probability of request being accepted by the borrowed workers. Extensive experimental results verify the effectiveness and efficiency of our algorithms.
UR - https://www.scopus.com/pages/publications/85085857936
U2 - 10.1109/ICDE48307.2020.00008
DO - 10.1109/ICDE48307.2020.00008
M3 - Conference contribution
AN - SCOPUS:85085857936
T3 - Proceedings - International Conference on Data Engineering
SP - 1
EP - 12
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PB - IEEE Computer Society
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
Y2 - 20 April 2020 through 24 April 2020
ER -