Stochastic Geometry-Based Age of Information Performance Analysis for Privacy Preservation-Oriented 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
  • Jiangtian Nie
  • Zehui Xiong
  • Renhui Xu
  • Xiaokang Zhou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)9527-9541
Number of pages15
Journal / PublicationIEEE Transactions on Vehicular Technology
Volume72
Issue number7
Online published3 Mar 2023
Publication statusPublished - Jul 2023

Abstract

As a promising enabler for sensing data collection manner, Mobile Crowdsensing (MCS) can play an important role due to its low implementation cost, flexible collection manner and controllable data quality traits. To investigate the sensing data freshness metric as well as ensure the data security, with the help of stochastic geometry tool, this paper designs a Non-Orthgonal Multiple Access (NOMA)-based Age of Information (AoI) performance analysis for privacy preservation-oriented MCS. To be specific, the privacy-preserving MCS system model is established at first. Then, based on stochastic geometry and queue theory, the AoI performance under cryptography guarantee is investigated. Next, given the traits of wireless channel and mobile terminal, mathematical AoI expressions for sensing data are determined under different serving rules, queue models and transmission schemes. Finally, under various parameter settings, numerical results evaluate the correctness and effectiveness of the established AoI model. © 2023 IEEE.

Research Area(s)

  • Age of Information, Data privacy, Geometry, Mobile Crowdsensing, NOMA, Privacy, Privacy Preservation, Sensors, Stochastic Geometry, Stochastic processes, Task analysis

Citation Format(s)

Stochastic Geometry-Based Age of Information Performance Analysis for Privacy Preservation-Oriented Mobile Crowdsensing. / Yang, Yaoqi; Zhang, Bangning; Guo, Daoxing et al.
In: IEEE Transactions on Vehicular Technology, Vol. 72, No. 7, 07.2023, p. 9527-9541.

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