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Author:

Guo, Jianxiong (Guo, Jianxiong.) | Ding, Xingjian (Ding, Xingjian.) | Wang, Tian (Wang, Tian.) | Jia, Weijia (Jia, Weijia.)

Indexed by:

EI Scopus SCIE

Abstract:

With the rapid popularization of mobile devices, the mobile crowdsourcing has become a hot topic in order to make full use of the resources of mobile devices. To achieve this goal, it is necessary to design an excellent incentive mechanism to encourage more mobile users to actively undertake crowdsourcing tasks, so as to achieve maximization of certain economic indicators. However, most of the reported incentive mechanisms in the existing literature adopt a centralized platform, which collects the bidding information from workers and task requesters. There is a risk of privacy exposure. In this paper, we design a decentralized auction framework where mobile workers are sellers and task requesters are buyers. This requires each participant to make its own local and independent decision, thereby avoiding centralized processing of task allocation and pricing. Both of them aim to maximize their utilities under the budget constraint. We theoretically prove that our proposed framework is individual rational, budget balanced, truthful, and computationally efficient, and then we conduct a group of numerical simulations to demonstrate its correctness and effectiveness.(c) 2022 Elsevier B.V. All rights reserved.

Keyword:

Truthfulness Decentralization Incentive mechanism Utility maximization Auction theory

Author Community:

  • [ 1 ] [Guo, Jianxiong]Beijing Normal Univ, Adv Inst Nat Sci, Zhuhai 519087, Peoples R China
  • [ 2 ] [Wang, Tian]Beijing Normal Univ, Adv Inst Nat Sci, Zhuhai 519087, Peoples R China
  • [ 3 ] [Jia, Weijia]Beijing Normal Univ, Adv Inst Nat Sci, Zhuhai 519087, Peoples R China
  • [ 4 ] [Guo, Jianxiong]BNU HKBU United Int Coll, Guangdong Key Lab AI & Multimodal Data Proc, Zhuhai 519087, Peoples R China
  • [ 5 ] [Wang, Tian]BNU HKBU United Int Coll, Guangdong Key Lab AI & Multimodal Data Proc, Zhuhai 519087, Peoples R China
  • [ 6 ] [Jia, Weijia]BNU HKBU United Int Coll, Guangdong Key Lab AI & Multimodal Data Proc, Zhuhai 519087, Peoples R China
  • [ 7 ] [Ding, Xingjian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Ding, Xingjian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;

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Source :

THEORETICAL COMPUTER SCIENCE

ISSN: 0304-3975

Year: 2023

Volume: 939

Page: 250-260

1 . 1 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:19

Cited Count:

WoS CC Cited Count: 7

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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