Can we improve information freshness with predictions in mobile crowd-learning?

Zhengxiong Yuan, Bin Li, Jia Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rapid growth of mobile devices has spurred the development of crowd-learning applications, which rely on users to collect, report and share real-time information. A critical factor of crowd-learning is information freshness, which can be measured by a metric called age-of-information (AoI). Moreover, recent advances in machine learning and abundance of historical data have enabled crowd-learning service providers to make precise predictions on user arrivals, data trends and other predictable information. These developments lead to a fundamental question: Can we improve information freshness with predictions in mobile crowd-learning? In this paper, we show that the answer is affirmative. Specifically, motivated by the age-optimal Round-Robin policy, we propose the so-called 'periodic equal spreading' (PES) policy. Under the PES policy, we first reveal a counter-intuitive insight that the frequency of prediction should not be too often in terms of AoI improvement. Further, we analyze the AoI performances of the proposed PES policy and derive upper bounds for the average age under i.i.d. and Markovian arrivals, respectively. In order to evaluate the AoI performance gain of the PES policy, we also derive two closed form expressions for the average age under uncontrolled i.i.d. and Markovian arrivals, which could be of independent interest. Our results in this paper serve as a first building block towards understanding the role of predictions in mobile crowd-learning.

Original languageEnglish (US)
Title of host publicationIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages702-709
Number of pages8
ISBN (Electronic)9781728186955
DOIs
StatePublished - Jul 2020
Event2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 - Toronto, Canada
Duration: Jul 6 2020Jul 9 2020

Publication series

NameIEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020

Conference

Conference2020 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
Country/TerritoryCanada
CityToronto
Period7/6/207/9/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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