Posted Pricing for Chance Constrained Robust Crowdsensing

Yuben Qu, Shaojie Tang, Chao Dong, Peng Li, Song Guo, Haipeng Dai, Fan Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm.

Original languageEnglish
Article number8565999
Pages (from-to)188-199
Number of pages12
JournalIEEE Transactions on Mobile Computing
Volume19
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • chance-constrained optimization
  • convex optimization
  • Crowdsensing
  • robustness

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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