Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

10 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)3373-3387
Journal / PublicationIEEE Transactions on Mobile Computing
Volume21
Issue number9
Online published19 Jan 2021
Publication statusPublished - Sept 2022
Externally publishedYes

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.

Research Area(s)

  • crowdsensing, edge computing, incentive mechanism, reverse auction, Vehicular crowdsourcing

Citation Format(s)

Timeliness-Aware Incentive Mechanism for Vehicular Crowdsourcing in Smart Cities. / Chen, Xianhao; Zhang, Lan; Pang, Yawei et al.
In: IEEE Transactions on Mobile Computing, Vol. 21, No. 9, 09.2022, p. 3373-3387.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review