Paper Info
Title
Real-Time Cross Online Matching in Spatial Crowdsourcing
Abstract
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.
Year
DOI
Venue
2020
10.1109/ICDE48307.2020.00008
2020 IEEE 36th International Conference on Data Engineering (ICDE)
Keywords
DocType
ISSN
borrowed workers,spatial crowdsourcing,mobile communication techniques,task assignment,total revenue,crowd workers,user requests,money cost,task allocation,unoccupied crowd workers,deterministic cross online matching,real time cross online matching,quality of services,randomized cross online matching,DemCOM,RamCOM,probability
Conference
1063-6382
ISBN
Citations 
PageRank 
978-1-7281-2904-4
1
0.36
References 
Authors
13
6
Name
Order
Citations
PageRank
Yurong Cheng11098.90
Boyang Li28212.61
Xiangmin Zhou331925.53
Ye Yuan411724.40
Guoren Wang51366159.46
Lei Chen66239395.84