Zhang Jing, Ding Qian, Li Biao, Ye Xiucai
School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China.
Department of Computer Science, University of Tsukuba, Tsukuba, Japan.
PeerJ Comput Sci. 2023 Feb 20;9:e1244. doi: 10.7717/peerj-cs.1244. eCollection 2023.
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.
空间众包是指根据位置信息将众包工作者分配到每个任务。K近邻技术已广泛应用于众包分配的众包应用中。然而，仍有几个问题需要强调。现有的大多数空间众包分配方案都在集中式框架上运行，导致众包分配效率低下。此外，这些空间众包分配方案是单向分配，即可以从众包工作者集合中查询每个任务的合适匹配对象，但不能反向查询。本文提出了一种基于边缘计算的双向K近邻空间众包分配协议（BKNN-CAP）。首先，建立了一种基于边缘计算的空间众包任务分配框架（SCTAFEC），该框架可以将所有任务卸载到边缘计算层的边缘节点，以实现时空查询的并行处理。其次，提出了正向K近邻时空查询算法（PKNN）和反向K近邻时空查询算法（RKNN），使任务发布者和众包工作者能够进行双向查询。此外，还提出了一种道路网络距离计算方法，以提高空间查询场景中欧几里得距离的准确性。实验结果表明，与其他协议相比，该协议具有更少的时间成本和更高的匹配成功率。
