Can We Achieve Fresh Information with Selfish Users in Mobile Crowd-Learning?

Bin Li, Jia Liu

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

5 Scopus citations

Abstract

The proliferation of smart mobile devices has spurred an explosive growth of mobile crowd-learning services, where service providers rely on the user community to voluntarily collect, report, and share real-time information for a collection of scattered points of interest (PoI). A critical factor affecting the future large-scale adoption of such mobile crowd-learning applications is the freshness of the crowd-learned information, which can be measured by a metric termed 'age-of-information' (AoI). However, we show that the AoI of mobile crowd-learning could be arbitrarily bad under selfish users' behaviors if the system is poorly designed. This motivates us to design efficient reward mechanisms to incentivize mobile users to report information in time, with the goal of keeping the AoI and congestion level of each PoI low. Toward this end, we consider a simple linear AoI-based reward mechanism and analyze its AoI and congestion performances in terms of price of anarchy (PoA), which characterizes the degradation of the system efficiency due to selfish behavior of users. Remarkably, we show that the proposed mechanism achieves the optimal AoI performance asymptotically in a deterministic scenario. Further, we prove that the proposed mechanism achieves a bounded PoA in general stochastic cases, and the bound only depends on system parameters. Particularly, when the service rates of PoIs are symmetric in stochastic cases, the achieved PoA is upperbounded by 1/2 asymptotically. Collectively, this work advances our understanding of information freshness in mobile crowd-learning systems.

Original languageEnglish (US)
Title of host publicationProceedings - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
EditorsFrancesco de Pelligrini, Francesco de Pelligrini, Walid Saad, Chee Wei Tan
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783903176201
DOIs
StatePublished - Jun 2019
Event17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019 - Avignon, France
Duration: Jun 3 2019Jun 7 2019

Publication series

NameProceedings - 17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019

Conference

Conference17th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2019
Country/TerritoryFrance
CityAvignon
Period6/3/196/7/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Signal Processing
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Modeling and Simulation
  • Computer Networks and Communications

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