Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing

Jing Zhang; Qianqian Wang; Ding Lang; Yuguang Xu; Hong-an Li; Xuewen Li; Jing Zhang; Qianqian Wang; Ding Lang; Yuguang Xu; Hong-an Li; Xuewen Li

doi:10.3934/mbe.2023533

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 11998-12023. doi: 10.3934/mbe.2023533

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Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing

1.
School of Computer Science and Technology, Xi'an University of Science and Technology, Xian 710600, China
2.
School of Energy Enginnering, Xi'an University of Science and Technology, Xian 710600, China
3.
School of Safety Science and Engineering, Xi'an University of Science and Technology, Xian 710600, China

Academic Editor: Yiu-ming Cheung

Received: 10 February 2023 Revised: 13 April 2023 Accepted: 24 April 2023 Published: 12 May 2023

Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints.
- sparse mobile crowd sensing,
- STGCN-GRU,
- user selection,
- reinforcement learning
Citation: Jing Zhang, Qianqian Wang, Ding Lang, Yuguang Xu, Hong-an Li, Xuewen Li. Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 11998-12023. doi: 10.3934/mbe.2023533

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Abstract

Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints.

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