An Incentive Approach in Mobile Crowdsensing for Perceptual User

被引：1

作者：

Chen, Lu ^{[1
]}

Zhang, Degan ^{[1
]}

Zhang, Jie ^{[2
]}

Zhang, Ting ^{[3
]}

Du, Jinyu ^{[1
]}

Fan, Hongrui ^{[1
]}

机构：

[1] Tianjin Univ Technol, Minist Educ, Tianjin Key Lab Intelligent Comp & Novel Software, Key Lab Comp Vis & Syst, Tianjin, Peoples R China

[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China

[3] Tianjin Univ Sport, Sch Sports Econ & Management, Tianjin 301617, Peoples R China

来源：

PROCEEDINGS OF THE IEEE 46TH CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN 2021) | 2021年

基金：

中国国家自然科学基金;

关键词：

mobile crowdsensing; mobile edge computing; privacy protection; incentive strategy; machine learning; ALGORITHM; STRATEGY;

D O I：

10.1109/LCN52139.2021.9524951

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The privacy protection of perceptual user and their enthusiasm improvement for participating in perceptual tasks are two important problems in MCS (Mobile Crowdsensing) network. A mechanism of local differential privacy protection of attribute correlation can generate perceptual results with higher precision of attribute correlation and protect perceptual users' privacy data. A flow compensation incentive model for perceptual users' privacy data protection based on opportunity cooperation transmission can reduce the flow compensation expenditure of MCS and improve perceptual users' enthusiasm. Experiments show that our approach improves the perceptual result precision, reduces MCS overhead, and reduces flow compensation cost compared with the related approaches.

引用

页码：359 / 362

页数：4

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