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The rapid adoption of the vehicles and their on-board sensors as a primary means of transportation make them natural candidates for the outsourcing of data collection. However, vehicles mobility patterns tend to cluster into specific regions such as highways and popular roads, that makes their utilization difficult for data collection in isolated regions with low density traffic. We tackle this problem by proposing a probabilistic incentive mechanism for Vehicular Crowdsensing (VCS) that encourages vehicles to deviate from their pre-planned trajectories in order to visit and collect data from the isolated places. Our proposed framework is able to handle asynchronous vehicles. Also, vehicles consider the traffic holistically to find more profitable routes. By using a realistic vehicular movement data set (UBER movement), open-street maps (OSM) and SUMO vehicular traffic simulator, we show our algorithm significantly outperforms traditional approaches for trajectory generation in terms of spatial and temporal coverage, road utilization, and average participant utility.
Chakeri et al. (Sat,) studied this question.