[1]张骥先,洪金梁.移动群智感知任务的预算可行时钟拍卖机制[J].郑州大学学报(工学版),2025,46(04):85-92.[doi:10.13705/j.issn.1671-6833.2025.04.013]
　ZHANG Jixian,HONG Jinliang.A Budget-feasible Clock Auction Mechanism for Mobile Crowdsensing Tasks[J].Journal of Zhengzhou University (Engineering Science),2025,46(04):85-92.[doi:10.13705/j.issn.1671-6833.2025.04.013]
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移动群智感知任务的预算可行时钟拍卖机制()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年04期
页码:
85-92
栏目:
出版日期:
2025-07-10

文章信息/Info

Title:
A Budget-feasible Clock Auction Mechanism for Mobile Crowdsensing Tasks
文章编号:
1671-6833(2025)04-0085-08
作者:
张骥先1; 2;  洪金梁1
1. 云南大学 信息学院,云南 昆明 650500;2. 云南大学 云南省智能系统与计算重点实验室,云南 昆明 650500
Author(s):
ZHANG Jixian1; 2;  HONG Jinliang1
1. School of Information Science and Engineering, Yunnan University, Kunming 650500, China; 2. Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming 650500, China
关键词:
时钟拍卖;  机制设计;  移动群智感知;  任务分配;  预算可行性
Keywords:
clock auction;  mechanism design;  mobile crowdsensing;  task allocation;  budget feasibility
分类号:
TP3-0
DOI:
10.13705/j.issn.1671-6833.2025.04.013
文献标志码:
A
摘要:
针对传统激励机制中要求用户提前披露个人价值判断,进而可能导致隐私泄露的问题,通过建立移动群智感知的数学模型,明确了感知任务、价值函数、预算以及用户效益等关键因素,并提出了一种基于时钟拍卖的MCCA 机制,以有效解决隐私泄露问题。 所提机制包括初分配定价阶段和最终赢家确定阶段,能够有效保护用户隐私。 理论分析表明:MCCA 算法满足真实性、个体理性、预算可行性和高效性。 在实验部分,将 MCCA 与现有算法从用户规模、预算规模和 POI 规模等维度进行对比分析,结果显示:MCCA 在价值收益与现有算法相当的同时,执行效率显著提升,并成功避免了用户隐私的泄露。
Abstract:
In response to the issue of traditional incentive mechanisms that required users to disclose personal value judgments in advance, potentially leading to privacy leakage, a mathematical model for mobile crowdsensing was established, to clarify key factors such as sensing tasks, value functions, budgets, and user benefits. Then, an MCCA mechanism based on clock auction was proposed to effectively address privacy leakage. The mechanism consisted of an initial allocation pricing phase and a final winner determination phase. Both could effectively protect uner privacy. Theoretical analysis demonstrated that the MCCA algorithm satisfied all requirements of truthfulness, individual rationality, budget feasibility, and efficiency. In the experimental section, MCCA was compared with existing algorithms from the perspectives of use scale, budget scale, and POI scale. The results showed that MCCA achieved comparable value gain to existing algorithms while significantly improving execution efficiency and successfully preventing user privacy leakage.

参考文献/References:

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更新日期/Last Update: 2025-07-13