Optimal Transport for Mobile Crowd Sensing Participants

Smart cities are becoming more complex and greater volumes of data are required for its efficient operation. Mobile Crowdsensing (MCS) is a paradigm that employs smartphones as instruments to collect data, where the recruitment of participants is based on rewards and incentives. However due to the mobile nature of people, sensing may not be available in a specific area of interest, reducing the quality of the MCS inference of that region. In this paper, we propose a method that utilizes optimal transport so that the MCS administrator could direct participants towards areas with poor quality to improve overall quality. An analysis of optimal transport is presented where the method is evaluated using computer simulations, where it is shown to be efficient for moving participants among spatiotemporal cells.