Data Importance Aware Periodic Machine Learning Model Update for Sparse Mobile Crowdsensing

Sparse mobile crowdsensing is a crowdsensing paradigm that reduces the sensing cost while ensuring data quality by collecting data sparsely and reconstructing desired data using inference algorithms including machine learning algorithms. However, real-time inference of spatial information with sparse mobile crowdsensing has not sufficiently considered the change of temporal characteristics of data. As a result, the accuracy of the reconstructed data can deteriorate over time. Therefore, this paper proposes a framework that periodically updates a machine learning model used for reconstructing data by evaluating the importance of the data in terms of both inference and re-training and giving priority to collecting important data.