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Push vs Pull Participant Recruitment System for Personalized Vehicular Crowdsensing

From road condition information to locating parking spaces, there exists a growing need for real time sensing information. This can be especially important to support the smart applications of the future. Vehicles are an ideal platform for such data gathering given its mobility, sensors and computing resources. In this paper, we focus on small scale sensing tasks, tasks which are highly personalized and are unlikely to be shared. Thus, sensing information is collected on demand rather than continuously and at all time. We refer to this as personalized vehicular crowdsensing. To achieve personalized sensing coverage, we must resolve the problem of vehicular participant selection. We propose two solutions based on push and pull for vehicular crowdsensing. We aim to maximize sensing coverage, improve load balance, error tolerance, and minimize costs. We compare our results with existing approaches and show superior performances for all metrics.