Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing
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Consensus‑Based Group Task Assignment with Social Impact in Spatial Crowdsourcing Xiang Li1 · Yan Zhao2 · Xiaofang Zhou4 · Kai Zheng3 Received: 18 May 2020 / Revised: 5 August 2020 / Accepted: 31 August 2020 / Published online: 15 September 2020 © The Author(s) 2020
Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely. Keywords Spatial crowdsourcing · Group task assignment · Social impact-based preference · Group consensus
1 Introduction
* Yan Zhao [email protected] Xiang Li [email protected] Xiaofang Zhou [email protected] Kai Zheng [email protected] 1
School of Computer Science and Technology, Soochow University, Suzhou, China
2
Department of Computer Science, Aalborg University, Aalborg, Denmark
3
University of Electronic Science and Technology of China, Chengdu, China
4
University of Queensland, Brisbane, Australia
With the ubiquitous deployment of wireless networks and mobile devices (e.g., smart phones), spatial crowdsourcing (SC), an emerging paradigm utilizing the distributed mobile devices to monitor diverse phenomena about human activities, has attracted much attention from both academic and industry communities. The main idea of spatial crowdsourcing is recruiting a set of available workers to perform the location-specific tasks by physically traveling to these locations, called task assignment. Most existing SC researches focus on single task assignment [20, 22], which assumes that tasks are simple and each task can only be assigned to a single worker. For example, Tong et al. [23] design several efficient gre
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