Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing
Spatial crowdsourcing refers to the allocation of crowdsourcing workers to each task based on location information. K-nearest neighbor technology has been widely applied in crowdsourcing applications for crowdsourcing allocation. However, there are still several issues need to be stressed. Most of t...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-02-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1244.pdf |
| _version_ | 1797900334749712384 |
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| author | Jing Zhang Qian Ding Biao Li Xiucai Ye |
| author_facet | Jing Zhang Qian Ding Biao Li Xiucai Ye |
| author_sort | Jing Zhang |
| collection | DOAJ |
| description | Spatial crowdsourcing refers to the allocation of crowdsourcing workers to each task based on location information. K-nearest neighbor technology has been widely applied in crowdsourcing applications for crowdsourcing allocation. However, there are still several issues need to be stressed. Most of the existing spatial crowdsourcing allocation schemes operate on a centralized framework, resulting in low efficiency of crowdsourcing allocation. In addition, these spatial crowdsourcing allocation schemes are one-way allocation, that is, the suitable matching objects for each task can be queried from the set of crowdsourcing workers, but cannot query in reverse. In this article, a bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing (BKNN-CAP) is proposed. Firstly, a spatial crowdsourcing task allocation framework based on edge computing (SCTAFEC) is established, which can offload all tasks to edge nodes in edge computing layer to realize parallel processing of spatio-temporal queries. Secondly, the positive k-nearest neighbor spatio-temporal query algorithm (PKNN) and reverse k-nearest neighbor spatio-temporal query algorithm (RKNN) are proposed to make the task publishers and crowdsourcing workers conduct two-way query. In addition, a road network distance calculation method is proposed to improve the accuracy of Euclidean distance in spatial query scenarios. Experimental results show that the proposed protocol has less time cost and higher matching success rate compared with other ones. |
| first_indexed | 2024-04-10T08:43:16Z |
| format | Article |
| id | doaj.art-0dcd3cc5e769485d84123a371610f702 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-10T08:43:16Z |
| publishDate | 2023-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-0dcd3cc5e769485d84123a371610f7022023-02-22T15:05:11ZengPeerJ Inc.PeerJ Computer Science2376-59922023-02-019e124410.7717/peerj-cs.1244Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computingJing Zhang0Qian Ding1Biao Li2Xiucai Ye3School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, ChinaSchool of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, ChinaSchool of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, ChinaDepartment of Computer Science, University of Tsukuba, Tsukuba, JapanSpatial crowdsourcing refers to the allocation of crowdsourcing workers to each task based on location information. K-nearest neighbor technology has been widely applied in crowdsourcing applications for crowdsourcing allocation. However, there are still several issues need to be stressed. Most of the existing spatial crowdsourcing allocation schemes operate on a centralized framework, resulting in low efficiency of crowdsourcing allocation. In addition, these spatial crowdsourcing allocation schemes are one-way allocation, that is, the suitable matching objects for each task can be queried from the set of crowdsourcing workers, but cannot query in reverse. In this article, a bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing (BKNN-CAP) is proposed. Firstly, a spatial crowdsourcing task allocation framework based on edge computing (SCTAFEC) is established, which can offload all tasks to edge nodes in edge computing layer to realize parallel processing of spatio-temporal queries. Secondly, the positive k-nearest neighbor spatio-temporal query algorithm (PKNN) and reverse k-nearest neighbor spatio-temporal query algorithm (RKNN) are proposed to make the task publishers and crowdsourcing workers conduct two-way query. In addition, a road network distance calculation method is proposed to improve the accuracy of Euclidean distance in spatial query scenarios. Experimental results show that the proposed protocol has less time cost and higher matching success rate compared with other ones.https://peerj.com/articles/cs-1244.pdfSpatial crowdsourcingTask allocationEdge computingBidirectional k-nearest neighborRoad network distance |
| spellingShingle | Jing Zhang Qian Ding Biao Li Xiucai Ye Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing PeerJ Computer Science Spatial crowdsourcing Task allocation Edge computing Bidirectional k-nearest neighbor Road network distance |
| title | Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing |
| title_full | Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing |
| title_fullStr | Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing |
| title_full_unstemmed | Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing |
| title_short | Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing |
| title_sort | bidirectional k nearest neighbor spatial crowdsourcing allocation protocol based on edge computing |
| topic | Spatial crowdsourcing Task allocation Edge computing Bidirectional k-nearest neighbor Road network distance |
| url | https://peerj.com/articles/cs-1244.pdf |
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