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Title	Timeliness-Aware Incentive Mechanism For Vehicular Crowdsourcing In Smart Cities
ID_Doc	57451
Authors	Chen X.; Zhang L.; Pang Y.; Lin B.; Fang Y.
Year	2022
Published	IEEE Transactions on Mobile Computing, 21, 9
DOI	http://dx.doi.org/10.1109/TMC.2021.3052963
Abstract	Vehicular crowdsourcing is a promising paradigm that takes advantage of powerful onboard capabilities of vehicles to perform various tasks in smart cities. To fulfill this vision, a well-designed incentive mechanism is essential to stimulate the participation of vehicles. In this paper, we propose a timeliness-aware incentive mechanism for vehicular crowdsourcing by taking vehicle's uncertain travel time into account. In view of the stochastic nature of traffic conditions, we derive a tractable expression for the probability distribution of task delay based on a discrete-time traffic model. By leveraging reverse auction framework, we model the utility of a service requester as a function in terms of uncertain task delay and incurred payment. To maximize the requester's utility under a budget constraint, we cast the mechanism design as a non-monotone submodular maximization problem over a knapsack constraint. Based on this formulation, we develop a truthful budgeted utility maximization auction (TBUMA), which is truthful, budget feasible, profitable, individually rational and computationally efficient. Through extensive trace-based simulations, we demonstrate the effectiveness of our proposed incentive mechanism.
Author Keywords	crowdsensing; edge computing; incentive mechanism; reverse auction; Vehicular crowdsourcing

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