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

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1517
Author(s):  
Di Xiao ◽  
Min Li ◽  
Hongying Zheng
Keyword(s):  
Data Storage ◽  
Privacy Protection ◽  
Rapid Development ◽  
Fog Computing ◽  
Low Complexity ◽  
Video Data ◽  
Edge Computing ◽  
Scalar Data ◽  
Key Frames ◽  
Multi Access

Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.

Research on Key Technologies of Massive Videos Management Under the Background of Cloud Platform

2019 ◽  
Vol 23 (1) ◽  
pp. 72-77
Author(s):  
Lin Jin ◽  
◽  
Changhong Yan
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Management System ◽  
Rapid Development ◽  
Mobile Internet ◽  
Video Data ◽  
Cloud Platform ◽  
Module Design ◽  
Management Platform ◽  
Key Technologies

With the rapid development of mobile internet and smart city, video surveillance is popular in areas such as transportation, schools, homes, and shopping malls. It is important subject to manage the massive videos quickly and accurately. This paper tries to use Hadoop cloud platform for massive video data storage, transcoding and retrieval. The key technologies of cloud computing and Hadoop are introduced firstly in the paper. Then, we analyze the functions of video management platform, such as user management, videos storage, videos transcoding, and videos retrieval. According to the basic functions and cloud computing, each module design process and figure are provided in the paper. The massive videos management system based on cloud platform will be better than the traditional videos management system in the aspects of storage capacity, transcoding performance and retrieval speed.

scholarly journals A Novel Blockchain Framework for Industrial IoT Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2061 ◽  
Author(s):  
Xuesong Xu ◽  
Zhi Zeng ◽  
Shengjie Yang ◽  
Hongyan Shao
Keyword(s):  
Data Storage ◽  
Resource Constraints ◽  
Rapid Development ◽  
Edge Computing ◽  
Consensus Algorithm ◽  
Resource Constrained ◽  
Blockchain Technology ◽  
Industrial Internet ◽  
Industrial Iot ◽  
And Performance

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application.

scholarly journals Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain

2019 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ammar Muthanna ◽  
Abdelhamied A. Ateya ◽  
Abdukodir Khakimov ◽  
Irina Gudkova ◽  
Abdelrahman Abuarqoub ◽  
...  
Keyword(s):  
High Reliability ◽  
Fog Computing ◽  
Network Virtualization ◽  
Software Defined Networking ◽  
High Availability ◽  
Edge Computing ◽  
Iot Applications ◽  
Distributed Controllers ◽  
Multi Access ◽  
Reliability And Availability

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

scholarly journals A Homomorphic Encryption and Privacy Protection Method Based on Blockchain and Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoyan Yan ◽  
Qilin Wu ◽  
Youming Sun
Keyword(s):  
Data Storage ◽  
Privacy Protection ◽  
Simulation Experiment ◽  
Homomorphic Encryption ◽  
Database Security ◽  
Edge Computing ◽  
Operating Efficiency ◽  
Cloud Data ◽  
Protection Method ◽  
Additive Homomorphism

With its decentralization, reliable database, security, and quasi anonymity, blockchain provides a new solution for data storage and sharing as well as privacy protection. This paper combines the advantages of blockchain and edge computing and constructs the key technology solutions of edge computing based on blockchain. On one hand, it achieves the security protection and integrity check of cloud data; and on the other hand, it also realizes more extensive secure multiparty computation. In order to assure the operating efficiency of blockchain and alleviate the computational burden of client, it also introduces the Paillier cryptosystem which supports additive homomorphism. The task execution side encrypts all data, while the edge node can process the ciphertext of the data received, acquire and return the ciphertext of the final result to the client. The simulation experiment proves that the proposed algorithm is effective and feasible.

Multi-access edge computing aided mobility for privacy protection in Internet of Things

Computing ◽  
2018 ◽  
Vol 101 (7) ◽  
pp. 729-742 ◽  
Author(s):  
Ping Zhang ◽  
Mimoza Durresi ◽  
Arjan Durresi
Keyword(s):  
Internet Of Things ◽  
Privacy Protection ◽  
Edge Computing ◽  
Multi Access

scholarly journals Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things

2017 ◽  
Vol 11 (4) ◽  
pp. 647-652 ◽  
Author(s):  
David C. Klonoff
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Storage ◽  
Cloud Model ◽  
Fog Computing ◽  
Edge Computing ◽  
Sensor Data ◽  
The Internet ◽  
Privacy And Security ◽  
Continuous Glucose Monitors

The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

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