Incentive Mechanism Design for Mobile Crowdsourcing Without Verification

Chao Huang, Haoran Yu, Jianwei Huang, Randall Berry*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter studies the design of incentive mechanisms for mobile crowdsourcing systems in which verifying the underlying ground truth is not possible. Namely, we consider a crowdsourcing platform that seeks to incentivize a group of workers to put in effort and truthfully report solutions to a given task. Challenges in this setting include that the workers may have heterogeneous capabilities and may have an incentive to collude in order to deceive the platform. The platform itself may have incomplete information regarding the workers’ capabilities, which it could attempt to learn over time. Furthermore, there may be asymmetries in the information available to the platform and to the workers. We will survey approaches to dealing with such problems using game-theoretical and online learning-based approaches.

Original languageEnglish (US)
Title of host publicationWireless Networks (United Kingdom)
PublisherSpringer Nature
Pages117-140
Number of pages24
DOIs
StatePublished - 2023

Publication series

NameWireless Networks (United Kingdom)
VolumePart F1100
ISSN (Print)2366-1186
ISSN (Electronic)2366-1445

Keywords

  • Game-theoretical
  • Incentive mechanism
  • Mobile crowdsensing
  • Online learning

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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