A Dynamic Task Assignment Framework based on Prediction and Adaptive Batching
Lijun Sun, Xiaojie Yu, Shicong Chen, Yan Tao Yang · 2020
With the continuous popularization of smart devices, a new computing paradigm-Spatial Crowdsourcing(SC) came into being. As an important part of SC, task assignment has received more and more attention. However, in the real scenario, the emergence of tasks is random and dynamic, which pose a huge challenge to task assignment. In order to solve this challenge, we propose a Dynamic Task Assignment Framework based on Prediction and Adaptive Batching (DTAF-PAB), which utilizes the Gated Recurrent Unit (GRU) in deep learning to predict the number of tasks entering a specific area, and propose an adaptive batching algorithm based on Deep Q Network (DQN) to dynamically adjust the size of batches, thereby improving the overall benefit of assignment. We use datasets from the real world to evaluate the competitiveness of DTAF-PAB and the experimental results show that the proposed framework is superior to other existing technologies in terms of both predictive performance and crowdsourcing platform benefit.