Zhang Jing, Wang Qianqian, Lang Ding, Xu Yuguang, Li Hong-An, Li Xuewen
School of Computer Science and Technology, Xi'an University of Science and Technology, Xian 710600, China.
School of Energy Enginnering, Xi'an University of Science and Technology, Xian 710600, China.
Math Biosci Eng. 2023 May 12;20(7):11998-12023. doi: 10.3934/mbe.2023533.
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.
稀疏移动人群感知通过招募少量用户从少量子区域感知数据，然后推断其余子区域的数据来节省感知成本。不同的人在各自轨迹上收集的数据具有不同的价值，我们可以根据轨迹预测选择能够收集高价值数据的参与者。在本文中，我们研究用户轨迹预测和用户招募两个方面。首先，我们提出一种STGCN-GRU用户轨迹预测算法，该算法使用STGCN算法从轨迹地图中提取与时间和空间信息相关的特征，然后将特征序列输入GRU进行轨迹预测，该算法提高了用户轨迹预测的准确性。其次，提出了一种ADQN（动作DQN）用户招募算法。ADQN算法基于强化学习的思想改进了DQN中的目标函数。从Q网络中找到输入值最大的动作，然后找到相应动作Q网络的目标函数的输出值。这减少了Q网络中出现的高估问题，提高了用户招募的准确性。实验结果表明，本文提出的STGCN-GRU算法的评估指标FDE和ADE优于其他代表性算法。并且在两个真实数据集上的实验验证了ADQN用户选择算法的有效性，该算法能够在预算约束下有效提高数据推断的准确性。
