Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing
Purposes To address the imbalance between supply and demand in traditional single platform task assignment, Cross Online Matching (COM) has emerged as a novel solution that allows multiple similar platforms to establish cooperative relationships and send uncompleted tasks to other platforms, increas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2024-01-01
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| Series: | Taiyuan Ligong Daxue xuebao |
| Subjects: | |
| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2255.html |
| _version_ | 1827283456042729472 |
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| author | Qianqian JIN Boyang LI Yurong CHENG Yongjiao SUN |
| author_facet | Qianqian JIN Boyang LI Yurong CHENG Yongjiao SUN |
| author_sort | Qianqian JIN |
| collection | DOAJ |
| description | Purposes To address the imbalance between supply and demand in traditional single platform task assignment, Cross Online Matching (COM) has emerged as a novel solution that allows multiple similar platforms to establish cooperative relationships and send uncompleted tasks to other platforms, increasing the probability of task acceptance. However, current COM solutions only consider single-round matching processes, making it difficult to find optimal decision results in multi-platform competition. To settle these limitations, the Multi-Round Cross Online Matching problem (MRCOM) is studied and Greedy-based Multi-Round Cross Online Matching (G-MRCOM) and Game-Theoretic Multi-Round Cross Online Matching (GT-MRCOM) algorithms are proposed. Methods G-MRCOM improves task completion efficiency by forwarding and matching tasks in multiple rounds, with platforms greedily selecting high-reward tasks to accomplish. GT-MRCOM, on the other hand, establishes incentive mechanisms among algorithms cooperating platforms, calculates task assignment strategies that satisfy Nash Equilibrium, and enables the platform to find better strategies in competition, thereby enhancing overall performance. Findings Experimental results demonstrate that the proposed algorithms can increase the total revenue of platforms, showcasing the effectiveness and efficiency of this study. |
| first_indexed | 2024-04-24T09:36:46Z |
| format | Article |
| id | doaj.art-b02fbd4723d34cb8b9b7587f81dd02a3 |
| institution | Directory Open Access Journal |
| issn | 1007-9432 |
| language | English |
| last_indexed | 2024-04-24T09:36:46Z |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj.art-b02fbd4723d34cb8b9b7587f81dd02a32024-04-15T09:17:22ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322024-01-0155115516210.16355/j.tyut.1007-9432.202206381007-9432(2024)01-0155-08Multi-round Cross Online Matching in Spatial-temporal CrowdsourcingQianqian JIN0Boyang LI1Yurong CHENG2Yongjiao SUN3School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110167, ChinaPurposes To address the imbalance between supply and demand in traditional single platform task assignment, Cross Online Matching (COM) has emerged as a novel solution that allows multiple similar platforms to establish cooperative relationships and send uncompleted tasks to other platforms, increasing the probability of task acceptance. However, current COM solutions only consider single-round matching processes, making it difficult to find optimal decision results in multi-platform competition. To settle these limitations, the Multi-Round Cross Online Matching problem (MRCOM) is studied and Greedy-based Multi-Round Cross Online Matching (G-MRCOM) and Game-Theoretic Multi-Round Cross Online Matching (GT-MRCOM) algorithms are proposed. Methods G-MRCOM improves task completion efficiency by forwarding and matching tasks in multiple rounds, with platforms greedily selecting high-reward tasks to accomplish. GT-MRCOM, on the other hand, establishes incentive mechanisms among algorithms cooperating platforms, calculates task assignment strategies that satisfy Nash Equilibrium, and enables the platform to find better strategies in competition, thereby enhancing overall performance. Findings Experimental results demonstrate that the proposed algorithms can increase the total revenue of platforms, showcasing the effectiveness and efficiency of this study.https://tyutjournal.tyut.edu.cn/englishpaper/show-2255.htmlspatial-temporal crowdsourcingtask assignmentonline matchinggame theorygreedy |
| spellingShingle | Qianqian JIN Boyang LI Yurong CHENG Yongjiao SUN Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing Taiyuan Ligong Daxue xuebao spatial-temporal crowdsourcing task assignment online matching game theory greedy |
| title | Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing |
| title_full | Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing |
| title_fullStr | Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing |
| title_full_unstemmed | Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing |
| title_short | Multi-round Cross Online Matching in Spatial-temporal Crowdsourcing |
| title_sort | multi round cross online matching in spatial temporal crowdsourcing |
| topic | spatial-temporal crowdsourcing task assignment online matching game theory greedy |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2255.html |
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