Abstract
In this article, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs’ planned trajectories. We tackle this problem by dynamically modifying the AVs’ trajectories and collecting sensing samples from regions otherwise unreachable by originally planned routes. We model this problem as a non-cooperative game in which a set of AVs equipped with sensors are the players and their trajectories are the strategies. Thus, our solution corresponds to a model in which expected individual utility drives the mobility decision of participants. Using open-street maps, SUMO vehicular traffic simulator, and extensive simulations, we show our algorithm significantly outperforms traditional approaches for trajectory generation. In particular, our performance evaluation shows a significant lift in crowdsourcer coverage, road utilization, and average participant utility.
Highlights
T HE RAPID spreading of mobile computing has opened the door to a new data collection paradigm called mobile crowdsensing (MCS) [1], [2]
SYSTEM MODEL we present the components of the Autonomous Vehicular Crowdsensing Game (AVCG), and show their interconnection
GREEDY ALGORITHM FOR Approximated Trajectory Nash Equilibtium (ATNE) we present our Approximated Trajectory Nash Equilibrium (ATNE) algorithm splitted into three components: location selection, participant trajectory development, and sensing plan adjustment
Summary
T HE RAPID spreading of mobile computing has opened the door to a new data collection paradigm called mobile crowdsensing (MCS) [1], [2]. VCS leverages vehicles’ mobility patterns which usually cover wider regions than pedestrians’, making possible to acquire sensing samples from places otherwise unreachable On the another hand, unlike MCS, vehicles’ mobility patterns are usually predictable [10], which means that any sensing task has to be located in the already known participants’ trajectories. The use of these samples may result in a poor reconstruction of the variable of interest We formulate this problem in the form of the following question: Is it possible to use VCS for acquisition of sensing samples from regions located out of the participants’ pre-planned trajectories? Some grid cells represent crowdsourcers which are in charge of posting a sensing task in their region of influence, and the associated task reward These announcements attract a crowd of vehicles who are willing to work on the posted tasks, namely visiting the requested regions (grid cells) and collecting sensing samples from those cells.
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