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Related Experiment Video

Updated: Dec 26, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

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Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing.

Di Xiao¹, Min Li¹, Hongying Zheng¹

¹College of Computer Science, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)

|March 14, 2020

Summary

This study introduces a hierarchical edge computing architecture for secure video storage in the Internet of Things (IoT). The novel scheme efficiently protects big video data privacy without adding computational or storage burdens.

Keywords:

cloud storage color video hierarchical edge computing low computation complexity smart privacy protection

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Last Updated: Dec 26, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

Area of Science:

Computer Science
Data Storage
Network Security

Background:

The Internet of Things (IoT) generates vast amounts of non-scalar data like videos, exceeding local storage and challenging cloud computing in distributed environments.
Existing cloud solutions struggle to support the unique demands of heterogeneous IoT settings, such as wireless sensor networks, necessitating new approaches for data management and privacy.

Purpose of the Study:

To design a hierarchical edge computing architecture for smart privacy protection of big video data storage in IoT environments.
To propose a low-complexity, high-security scheme leveraging multi-access edge computing, cloudlets, and fog computing for video data partitioning and storage.

Main Methods:

A hierarchical edge computing architecture integrating multi-access edge computing, cloudlets, and fog computing was designed.
Video data is segmented into three parts: significant bits of key frames on local devices, less significant bits of key frames on cloudlets, and compressed non-key frames on the cloud.
Encryption techniques, including 2D logistic-skew tent map, and compression methods like two-layer parallel compressive sensing were employed.

Main Results:

The proposed scheme effectively provides smart privacy protection for big video data storage within the hierarchical edge computing framework.
The method demonstrates low complexity, avoiding significant increases in computation burden and storage pressure on devices.
Simulation experiments and theoretical analyses validate the scheme's efficacy and security.

Conclusions:

The developed hierarchical edge computing scheme offers a viable solution for secure and efficient video data storage in IoT.
The approach balances privacy protection with resource constraints inherent in distributed IoT systems.
This research contributes to advancing secure data management strategies for the rapidly expanding Internet of Things ecosystem.