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MDU private data protection-oriented traffic compensation excitation method based on QL learning strategy

A flow compensation and privacy data technology, applied in the field of Internet of Things, can solve the problems of MDU consumption, multi-flow, etc.

Active Publication Date: 2020-09-29
TIANJIN UNIVERSITY OF TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, when the amount of perceived data that the MDU needs to transmit is large, it will cause the MDU to consume more traffic, power and concerns about data privacy issues.

Method used

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  • MDU private data protection-oriented traffic compensation excitation method based on QL learning strategy
  • MDU private data protection-oriented traffic compensation excitation method based on QL learning strategy
  • MDU private data protection-oriented traffic compensation excitation method based on QL learning strategy

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Embodiment 1

[0089] The development tool designed in this embodiment is PyCharm5.0.3, and the development language is Python3.5.2. The performance evaluation is performed using components DEAP (Distributed Evolutionary Algorithms in Python), SciPy scientific computing, and Matplotlib scientific drawing. The main objective of the performance evaluation is to determine the impact of the QLPPIA method on the quality of service and security of the MCS (Mobile Crowd Sensing) network in terms of privacy protection and traffic compensation incentives. The main implementation operations involved are the selection of data sets and the specific algorithm calculation process.

[0090] See attached Figure 15 , this embodiment is based on the QL learning strategy-oriented flow compensation and incentive method for MDU privacy data protection, mainly including the following key steps:

[0091] 1. Construction of the system model:

[0092] Section 1.1, MCS-MEC privacy protection system model;

[0093...

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Abstract

The invention discloses an MDU private data protection-oriented traffic compensation excitation method based on a QL learning strategy. A system architecture combining MCS and MEC is designed, a sensing result is uploaded to the MCS cloud through the EC, and the MCS cloud overhead is reduced. A local differential privacy attribute correlation protection model based on MCMC is constructed, a sensing result with higher attribute correlation accuracy is generated, and the security of MDU privacy data is protected. A traffic compensation excitation architecture for MDU private data protection based on QL opportunistic cooperative transmission is designed, the traffic compensation overhead of the MCS cloud is reduced, and the participation enthusiasm of the MDU is improved. Compared with existing methods such as high-dimensional attribute data privacy protection and opportunistic relay perception excitation, the QLPPiA method has the advantages that the perception result accuracy is averagely improved by 29.4%, the MCS cloud overhead is reduced by 89.92%, and the traffic compensation overhead is reduced by 19.03%.

Description

technical field [0001] The invention belongs to the field of the Internet of Things, and in particular relates to a traffic compensation and incentive method for MDU (mobile device user) privacy data protection based on a QL (Q learning) learning strategy. Background technique [0002] The use of smart mobile devices brings great convenience to people's lives. Mobile devices are equipped with various sensors, and there is inevitably the problem of MDU data privacy leakage. MCS (Mobile Crowd Sensing) plays an important role in environmental meteorological monitoring, intelligent traffic safety, medical and health care, and smart city management. MDU uses smart mobile devices to complete perception tasks. The task publisher creates a sensing task and issues a sensing task request to the MCS cloud. The MCS cloud recruits MDUs and assigns perception tasks to MDUs. MDU collects multi-dimensional attribute data records, performs sensing tasks and uploads sensing data to MCS clo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/62G06N7/00H04W28/10
CPCG06F21/6245H04W28/10G06N7/01
Inventor 张德干陈露杜金玉张捷张婷姜凯雯
Owner TIANJIN UNIVERSITY OF TECHNOLOGY
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