Bidirectional Choice for Many-to-many Online Task Assignment in Mobile Crowdsourcing
Xinhao Li, Yingjie Wang, Feng Jiang, Yang Gao, Chunxiao Mu, Zhipeng Cai, Yingxin Li, Shiju Jin · 2024
The evolution of 5G and 6G technologies has boosted mobile network speed, reduced delays, and widened coverage, empowering Mobile Crowd Sensing (MCS) to overcome surface and terrain obstacles. However, this advancement brings new hurdles for online task assignment. While most MCS methods suit surface applications, they struggle with allocation in complex environments. This paper focuses on MCS in varied settings like surface, air, and high altitude. Currently, planning-based task assignment works better for one-to-one or one-to-many scenarios, with limited options for many-to-many situations. Improving platform utility, attracting top-quality crowd workers, and enhancing task completion efficiency are vital. To tackle these challenges, the paper introduces a spatial division algorithm using 3D Voronoi diagrams for complex environments. This algorithm utilizes task coordinates to delineate assignment spaces. Additionally, it introduces a two-stage many-to-many online task assignment algorithm (MOTA) that forecasts crowd workers’ arrival probabilities and combines auction-based incentives with differential evolution algorithms. MOTA ensures efficient matching of workers and tasks within spatio-temporal constraints, balancing both parties’ interests. Finally, comparative experiments on real datasets assess the proposed MOTA algorithm’s usability and effectiveness based on overall gain, running time, task count, and assignment rate.