Probabilistic Control of Dynamic Crowds Toward Uniform Spatial-Temporal Coverage

Vehicular mobility and connectivity vary significantly over space and time when vehicular crowd sensing covers a city-wide area for a long time period, but it is important to achieve sufficiently uniform data coverage to satisfy the requirements of an environmental monitoring scenario. Our goal is thus to ensure uniform spatial-temporal coverage of sensed data over a city-wide area despite such vehicle dynamics. For a large area, trajectory-based approaches must deal with a great number and variety of participant mobility patterns. Hence, we propose a probabilistic control mechanism that adaptively adjusts the incentive to each participant, without using any prior information about participants. We provide a mathematical analysis that ensures stability of the number of participants with assigned tasks (called workers), and we evaluate the mechanism's robustness by using 24-hr vehicle trace data from a city-wide area. Our results demonstrate that, when the number of participants is up to 1500 times higher than the required number of workers, sensing actions result in a distribution with a mean of about 1 and an interquartile range of around 4 for a required sensing interval; moreover, the mean increases by 2% when 30% of communication messages are randomly lost.