Spatial-temporal Coverage Maximization in Vehicle-based Mobile Crowdsensing for Air Quality Monitoring

In this paper, we address vehicle-based mobile crowdsensing for air quality monitoring applications. We tackle a novel issue that asks to determine monitoring frequencies for maximizing spatial-temporal coverage while reducing the monitoring costs and balancing load across the vehicles. We begin by theoretically formulating the problem and proposing an objective function that considers the three goals. We then leverage the evolutionary approach to develop an algorithm for determining the optimal monitoring frequency. We conduct comprehensive experiments to evaluate the performance of the proposed approach and compare it to the other methods. The results indicate that our approach can enhance the objective function by a factor of 1.33 to 4 compared to the others.