학술논문
An Incentive Approach in Mobile Crowdsensing for Perceptual User
Document Type
Conference
Author
Source
2021 IEEE 46th Conference on Local Computer Networks (LCN) Local Computer Networks (LCN), 2021 IEEE 46th Conference on. :359-362 Oct, 2021
Subject
Language
Abstract
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.