Theoretical design of decentralized auction framework under mobile crowdsourcing environment

被引:7
作者
Guo, Jianxiong [1 ,2 ]
Ding, Xingjian [3 ]
Wang, Tian [1 ,2 ]
Jia, Weijia [1 ,2 ]
机构
[1] Beijing Normal Univ, Adv Inst Nat Sci, Zhuhai 519087, Peoples R China
[2] BNU HKBU United Int Coll, Guangdong Key Lab AI & Multimodal Data Proc, Zhuhai 519087, Peoples R China
[3] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Decentralization; Incentive mechanism; Auction theory; Utility maximization; Truthfulness; TRUTHFUL INCENTIVE MECHANISM; BLOCKCHAIN;
D O I
10.1016/j.tcs.2022.10.030
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
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.
引用
收藏
页码:250 / 260
页数:11
相关论文
共 35 条
[11]  
Hu Z., 2018, ADV NEURAL INF PROCE, V31
[12]   Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks [J].
Jiao, Yutao ;
Wang, Ping ;
Niyato, Dusit ;
Suankaewmanee, Kongrath .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2019, 30 (09) :1975-1989
[13]  
Li Ji, 2016, IEEE INFOCOM
[14]   WiFi-RITA Positioning: Enhanced Crowdsourcing Positioning Based on Massive Noisy User Traces [J].
Li, Zan ;
Zhao, Xiaohui ;
Zhao, Zhongliang ;
Braun, Torsten .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (06) :3785-3799
[15]  
Liu B., 2021, MOB INF SYST, V2021
[16]   Truthful Online Double Auctions for Mobile Crowdsourcing: An On-Demand Service Strategy [J].
Liu, Shumei ;
Yu, Yao ;
Guo, Lei ;
Yeoh, Phee Lep ;
Ni, Qiang ;
Vucetic, Branka ;
Li, Yonghui .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (17) :16096-16112
[17]   Trust-Aware sensing Quality estimation for team Crowdsourcing in social IoT [J].
Liu, Xiuwen ;
Fu, Jianming ;
Chen, Yanjiao ;
Luo, Weichen ;
Tang, Zihan .
COMPUTER NETWORKS, 2021, 184
[18]   Mechanism design via differential privacy [J].
McSherry, Frank ;
Talwar, Kunal .
48TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, PROCEEDINGS, 2007, :94-103
[19]  
Nisan N, 2007, ALGORITHMIC GAME THEORY, P1, DOI 10.1017/CBO9780511800481
[20]   A privacy-protected intelligent crowdsourcing application of IoT based on the reinforcement learning [J].
Ren, Yingying ;
Liu, Wei ;
Liu, Anfeng ;
Wang, Tian ;
Li, Ang .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2022, 127 :56-69