Keeping Cell Selection Model Up-to-Date to Adapt to Time-Dependent Environment in Sparse Mobile Crowdsensing

Sparse mobile crowdsensing (MCS) requires participants to collect data from partial cells and then intelligently infer the data of the rest cells. Since collecting data from different cells will probably result in different data inference quality, cell selection (i.e., which cells need to be selected to collect data) is a critical issue in Sparse MCS. Currently, state-of-the-art cell selection algorithms are implemented based on reinforcement learning. These algorithms ignore the problem that the urban environment is usually time dependent, and the cell selection model needs to be kept up-to-date to adapt to the time-dependent environment. However, Sparse MCS applications require participants to collect data only in a few cells, which makes it hard to obtain suitable training data for continuous cell selection model learning. To solve this problem, we model the spatiotemporal correlations in the collected sparse data, and then design various methods to update training data based on it. Particularly, these methods make full use of the gradual changes of data in time and space, and reasonably transform and splice sparse data at different moments. Finally, updated training data is fed to the cell selection model to keep it up-to-date. We conduct experimental evaluations by performing several sensing tasks in air quality monitoring. The results show that our proposed methods can effectively update training data as well as the cell selection model. Compared with several baselines, our best method can reduce inference error by more than 10% on average.

Automated summary

Sparse mobile crowdsensing requires intelligent data collection, with cell selection being a critical issue. Current algorithms overlook the time-dependency of urban environments. It is challenging to obtain suitable training data for continuous cell selection model learning in Sparse MCS applications. To address this, methods based on spatiotemporal correlations for updating training data have been proposed.