Planning-based mobile crowdsourcing bidirectional multi-stage online task assignment

With the development of mobile Internet networks, Mobile CrowdSourcing (MCS) is becoming increasingly popular in online scenarios. This poses a new challenge to the task assignment mechanism in the mobile crowdsourcing system. Existing task assignment schemes are mainly platform-centric and worker-centric. The previous focuses are on the total utility, while the latter focuses on the interests of workers. In actual online scenarios, current researches are difficult to take both platform and worker wishes into consideration. In this paper, a Bidirectional Multi-Stage Online task assignment algorithm (BMSO) is proposed. It divides the task assignment process into three stages. The first stage selects tasks not only satisfy the basic Spatio-temporal constraints but also have a high acceptance value for workers by using two rounds of screening. The second stage combines price forecasting with reverse auctions, considering both of the platform’s benefits and workers’ wish. In the third stage, the workers and tasks which have not completed their assignments are assigned twice to improve the total utility. Furthermore, we solve the problem that traditional task-planning methods are unsuitable for mobile crowdsourcing scenarios. This paper proposes a Dynamic Voronoi diagram-based Task Planning algorithm (DVTP) that generates a Voronoi diagram using the real-time positions of the workers to find the most efficient task execution sequence for the workers. Finally, we conduct comprehensive experiments on real datasets. The results demonstrate our proposed BMSO algorithm achieves superior performances on total utility, running time, number of assigned tasks, and task assignment rate compared to other baseline algorithms.