Mean Field Game and Broadcast Encryption-based Joint Data Freshness Optimization and Privacy Preservation for Mobile Crowdsensing

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Yaoqi Yang
  • Bangning Zhang
  • Daoxing Guo
  • Renhui Xu
  • Neeraj Kumar

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)14860-14874
Journal / PublicationIEEE Transactions on Vehicular Technology
Volume72
Issue number11
Online published5 Jun 2023
Publication statusPublished - Nov 2023

Abstract

Recently, due to the benefits of system extensibility, deployment cost, and implementation flexibility, mobile crowdsensing (MCS) is a popular method for sensing data collection. This paper aims at developing a timely and secure model for MCS by realizing the joint Age of Information (AoI) performance optimization and privacy preservation. First, based on the queuing theory, the AoI expression is derived in the closed form. Next, through the mean field game (MFG)-based sensing rate control policy, the AoI of the sensing data is optimized. Meanwhile, by adopting the broadcast and EIGamal encryption schemes, sensing task, data content, location, and identities of mobile devices are protected. Finally, simulation results under different parameter settings show that the proposed scheme is effective in improving the timeliness and security performance of the MCS system. 

© 2023 IEEE.

Research Area(s)

  • Mobile crowdsensing (MCS), age of information (AoI), privacy preservation, mean filed game (MFG), broadcast encryption

Citation Format(s)

Mean Field Game and Broadcast Encryption-based Joint Data Freshness Optimization and Privacy Preservation for Mobile Crowdsensing. / Yang, Yaoqi; Zhang, Bangning; Guo, Daoxing et al.
In: IEEE Transactions on Vehicular Technology, Vol. 72, No. 11, 11.2023, p. 14860-14874.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review