Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities
Document Type
Article
Publication Date
1-2021
Department
Department of Electrical and Computer Engineering
Abstract
IEEE 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.
Publication Title
IEEE Transactions on Mobile Computing
Recommended Citation
Chen, X.,
Zhang, L.,
Pang, Y.,
Lin, B.,
&
Fang, Y.
(2021).
Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities.
IEEE Transactions on Mobile Computing.
http://doi.org/10.1109/TMC.2021.3052963
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14637
