LAUSR

With the widespread use of smartphones and the continuous increase of their capabilities, a new sensing paradigm has emerged: mobile crowdsensing. The concept of crowdsensing implies the reliance on the crowd to perform sensing tasks and collect data about a phenomena of interest. Due to the benefits it offers in terms of time and cost savings in terms of sensors’ deployment and maintenance, the concept of mobile crowdsensing is now being adopted in the area of intelligent transportation. In this context, drivers or pedestrians equipped with sensor enabled smartphones collaborate to collect information about roads and traffic. However, the current solutions proposed for the use of crowdsensing for the collection of traffic related data adopt an opportunistic continuous sensing approach, which entails high resource consumption on the server and mobile device side, a high communication overhead, while offering little control of the users over the sensing activity. In this paper, we address these limitations by proposing an infrastructure-assisted on-demand crowdsensing approach for the real time detection and prediction of traffic conditions in an area of interest. Our approach combines the strengths of mobile crowdsensing, with the support of the mobile infrastructure, a multi-criteria algorithm for the participants’ selection, and a deductive rule-based model for traffic condition estimation. The proposed solution was validated through a combination of prototyping and simulated traffic traces, and the results show a significant reduction in terms of resources’ consumption and network overhead, while reaching high accuracy for the traffic condition estimation.