Sparse Region Prediction and Interpretability Analysis for Mobile Crowdsensing

Existing task allocation methods do not consider the uneven distribution of sensing users and ignore the sparse sensing region. This leads to the problem of low sensing coverage of the whole sensing task. In this article, a method of sparse region prediction and interpretability analysis for mobile crowdsensing is proposed. First, the spatio-temporal graph neural network is used to establish a prediction model for the sensing user’s flow distribution. The model considers the spatial dependency and the influence of sensing user flow on the long-term, medium-term, and short-term prediction results. Next, the prediction results are interpreted in terms of both edges and nodes using GNNExplainer. Then, the sensing range is divided into sparse and nonsparse sensing regions based on the prediction results. Finally, the sensing coverage is maximized with reduced movement. Experimental results under different data sets show that our method effectively reduces movement costs and improves sensing coverage compared to other baseline methods.