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作者:

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

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EI Scopus SCIE

摘要:

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.

关键词:

Truthfulness Decentralization Incentive mechanism Utility maximization Auction theory

作者机构:

  • [ 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

通讯作者信息:

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

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来源 :

THEORETICAL COMPUTER SCIENCE

ISSN: 0304-3975

年份: 2023

卷: 939

页码: 250-260

1 . 1 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

被引次数:

WoS核心集被引频次: 5

SCOPUS被引频次: 7

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 7

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