Abstract
Task assignment is a crucial aspect of mobile crowdsourcing research. The focus is on allocating appropriate perceptual tasks based on task and worker characteristics to optimize perceptual quality. Tasks arrive dynamically in a crowdsourcing system, and the platform cannot assign all tasks at the beginning. This can lead to the task allocation process falling into a local optimal solution. To address the problem, this paper proposes a two-stage prediction algorithm that utilises historical spatio-temporal data of crowdsourcing services. Firstly, the temporal data is converted into image data through Markov Transition Field. Then, the task availability is transformed into a categorisation problem. In the first stage, a ConvNeXt-based network is used to predict the task availability. In the second stage, a GRU-based network is used to predict the task duration. This paper presents the results of several experiments that confirm the effectiveness of the proposed algorithm.
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Fan, Z., Pan, Q., Gao, Z., Luan, P., Wei, K., Li, J. (2025). Mobile Crowdsourcing Task Assignment Algorithm Based on ConvNeXt and GRU. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14997. Springer, Cham. https://doi.org/10.1007/978-3-031-71464-1_22
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