ST-ICM: Spatial-Temporal Inference Calibration Model for Low Cost Fine-grained Mobile Sensing


In order to reduce the measurement error of low cost sensor in the real-time mobile sensing network, rendezvous calibration mechanism is widely used. To tackle the sparsity of reference data and the lack of calibration opportunities, we propose ST-ICM: a Spatial-Temporal Inference Calibration Model based on Gaussian Process Regression, assisting the calibration task by creating more calibration grids in both spatial and temporal dimensions. By using the GPR, the inferred grids generated by ST-ICM are associated with various confidence levels. Based on this property, we propose to make use of a hyperparameter, i.e., variance threshold, to balance the tradeoff between the quantity and quality of the inferred grids. Specifically, only the grids with variances below the threshold will be employed. We conducted experiments using a real-world dataset collected in Nanjing, China, to evaluate the performance of the proposed ST-ICM. The experimenal results show that our model achieves 24% improvement on error calibration compared to the baseline.

In Proceedings of the 28th Annual International Conference On Mobile Computing And Networking
Research Assistant Professor

My research interests include AIoT, edge computing, and mobile security.