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- W4307177008 abstract "With the increasing popularity of smart devices and spatio-temporal crowdsourcing, a new data gathering and perception approach combined with edge computing have made exceptional development in solving crowdsourcing problems. As a basic component of the spatio-temporal crowdsourcing system, task assignment seeks to arrange suitable workers for tasks. Existing research is mostly concerned with dynamic and static circumstances. On the other hand, dynamic scenarios cannot meet the advanced understanding of workers who arrive dynamically, and static scenarios cannot match the dynamic requirements of real situations. This paper provides a framework for Latency Time Based Task Assignment with Online and Offline (LTB-TAOO). This framework dynamically receives workers and tasks from the perspective of full assignment process. The static assignment method is utilized throughout the job assignment algorithm. A Q-Learning Based Task Algorithm for LTC (QL-LTC) algorithm is proposed to address the problem of latency time compute (LTC) in the LTB-TAOO framework. In order to improve the efficiency and accuracy of the LTB-TAOO framework, a new state transition Q-Learning based task assignment for LTC (NSQL-LTC) algorithm is applied after optimizing the state transition matrix and reward mechanism. Finally, the proposed methods’ effectiveness and efficiency are evaluated by conducting extensive experiments based on real datasets. • Reinforcement-Learning task assignment based on edge cloud under spatiotemporal crowdsourcing. • A crowdsourcing platform combining dynamic perception and static perception. • Using Q-learning to complete the task of delay perception and improve platform utility." @default.
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- W4307177008 date "2022-12-01" @default.
- W4307177008 modified "2023-10-18" @default.
- W4307177008 title "Task assignment for hybrid scenarios in spatial crowdsourcing: A Q-Learning-based approach" @default.
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- W4307177008 doi "https://doi.org/10.1016/j.asoc.2022.109749" @default.
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