Spatial-Temporal Data Inference With Graph Attention Neural Networks in Sparse Mobile Crowdsensing

Sparse mobile crowdsensing, driven by the increasing ubiquity of smartphones, has emerged as a popular method for data collection. This approach can reconstruct the whole sensing map by inferring missing temporal data from their spatial-temporal correlations to sparse samples. Traditional methods face challenges in capturing non-linear correlations in sensors and error accumulation. To address these issues, we propose a dynamic signal map reconstruction model, i.e., Spatial-Temporal Graph Attention (STGA). Specifically, STGA first applies graph embedding to convert sensing points into graph structures, followed by using a message-passing mechanism based on Graph Attention Networks (GAT) to capture complex spatial-temporal dependent features in sensing data. Then, we propose hierarchical attention mechanism, where different nodes are selected and weighted, and the temporal and spatial sequences of the nodes are stratified separately. By collecting a small amount of observation data, the spatial-temporal characteristics of nodes in the current sparse sensing data graph are effectively learned and the signal graph is updated. Attention mechanism quantitatively evaluates and weights the measurement points to further reduce the measurement error. Evaluations conducted on three typical urban sensing datasets demonstrate the effectiveness of the proposed methods in enhancing the inference accuracy of sparse sensing data.