Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing
In the era of the Internet of Things (IoT), the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world. As a part of the IoT ecosystem, task assignment has become an important goal of the research community. Existing task assignment alg...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-08-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864821000857 |
| _version_ | 1817998047577112576 |
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| author | Qi Zhang Yingjie Wang Zhipeng Cai Xiangrong Tong |
| author_facet | Qi Zhang Yingjie Wang Zhipeng Cai Xiangrong Tong |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | In the era of the Internet of Things (IoT), the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world. As a part of the IoT ecosystem, task assignment has become an important goal of the research community. Existing task assignment algorithms can be categorized as offline (performs better with datasets but struggles to achieve good real-life results) or online (works well with real-life input but is difficult to optimize regarding in-depth assignments). This paper proposes a Cross-regional Online Task (CROT) assignment problem based on the online assignment model. Given the CROT problem, an Online Task Assignment across Regions based on Prediction (OTARP) algorithm is proposed. OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments. The first stage uses historical data to make offline predictions, with a graph-driven method for offline bipartite graph matching. The second stage uses a bipartite graph to complete the online task assignment process. This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies. To encourage crowd workers to complete crowd tasks across regions, an incentive strategy is designed to encourage crowd workers’ movement. To avoid the idle problem in the process of crowd worker movement, a drop-by-rider problem is used to help crowd workers accept more crowd tasks, optimize the number of assignments, and increase utility. Finally, through comparison experiments on real datasets, the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated. |
| first_indexed | 2024-04-14T02:47:22Z |
| format | Article |
| id | doaj.art-ce6b665036fa4fe4b27524e1b0a051fa |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-04-14T02:47:22Z |
| publishDate | 2022-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-ce6b665036fa4fe4b27524e1b0a051fa2022-12-22T02:16:30ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482022-08-0184516530Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcingQi Zhang0Yingjie Wang1Zhipeng Cai2Xiangrong Tong3The School of Computer and Control Engineering, Yantai University, Yantai, 264005, ChinaThe School of Computer and Control Engineering, Yantai University, Yantai, 264005, China; Yantai Key Laboratory of High-end Ocean Engineering Equipment and Intelligent Technology, Yantai, 264005, China; Corresponding author.The Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USAThe School of Computer and Control Engineering, Yantai University, Yantai, 264005, China; Yantai Key Laboratory of High-end Ocean Engineering Equipment and Intelligent Technology, Yantai, 264005, ChinaIn the era of the Internet of Things (IoT), the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world. As a part of the IoT ecosystem, task assignment has become an important goal of the research community. Existing task assignment algorithms can be categorized as offline (performs better with datasets but struggles to achieve good real-life results) or online (works well with real-life input but is difficult to optimize regarding in-depth assignments). This paper proposes a Cross-regional Online Task (CROT) assignment problem based on the online assignment model. Given the CROT problem, an Online Task Assignment across Regions based on Prediction (OTARP) algorithm is proposed. OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments. The first stage uses historical data to make offline predictions, with a graph-driven method for offline bipartite graph matching. The second stage uses a bipartite graph to complete the online task assignment process. This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies. To encourage crowd workers to complete crowd tasks across regions, an incentive strategy is designed to encourage crowd workers’ movement. To avoid the idle problem in the process of crowd worker movement, a drop-by-rider problem is used to help crowd workers accept more crowd tasks, optimize the number of assignments, and increase utility. Finally, through comparison experiments on real datasets, the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.http://www.sciencedirect.com/science/article/pii/S2352864821000857Spatiotemporal crowdsourcingCross-regionalEdge cloudOffline predictionOline task assignment |
| spellingShingle | Qi Zhang Yingjie Wang Zhipeng Cai Xiangrong Tong Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing Digital Communications and Networks Spatiotemporal crowdsourcing Cross-regional Edge cloud Offline prediction Oline task assignment |
| title | Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing |
| title_full | Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing |
| title_fullStr | Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing |
| title_full_unstemmed | Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing |
| title_short | Multi-stage online task assignment driven by offline data under spatio-temporal crowdsourcing |
| title_sort | multi stage online task assignment driven by offline data under spatio temporal crowdsourcing |
| topic | Spatiotemporal crowdsourcing Cross-regional Edge cloud Offline prediction Oline task assignment |
| url | http://www.sciencedirect.com/science/article/pii/S2352864821000857 |
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