Electric Energy Data Storage and Privacy Protection in Edge Computing Mode

Author(s):  
Yixin Jiang ◽  
Yunan Zhang ◽  
Aidong Xu ◽  
Xiaoyun Kuang ◽  
Jiaxiao Meng ◽  
...  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jin Li ◽  
Songqi Wu ◽  
Yundan Yang ◽  
Fenghui Duan ◽  
Hui Lu ◽  
...  

In the process of sharing data, the costless replication of electric energy data leads to the problem of uncontrolled data and the difficulty of third-party access verification. This paper proposes a controlled sharing mechanism of data based on the consortium blockchain. The data flow range is controlled by the data isolation mechanism between channels provided by the consortium blockchain by constructing a data storage consortium chain to achieve trusted data storage, combining attribute-based encryption to complete data access control and meet the demands for granular data accessibility control and secure sharing; the data flow transfer ledger is built to record the original data life cycle management and effectively record the data transfer process of each data controller. Taking the application scenario of electric energy data sharing as an example, the scheme is designed and simulated on the Linux system and Hyperledger Fabric. Experimental results have verified that the mechanism can effectively control the scope of access to electrical energy data and realize the control of the data by the data owner.


2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaoyan Yan ◽  
Qilin Wu ◽  
Youming Sun

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.


Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1517
Author(s):  
Di Xiao ◽  
Min Li ◽  
Hongying Zheng

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.


Author(s):  
Hasnain Ali Almashhadani ◽  
Xiaoheng Deng ◽  
Suhaib Najeh Abdul Latif ◽  
Mohammed Mohsin Ibrahim ◽  
Ali Hussien Alshammari

2013 ◽  
Vol 4 ◽  
pp. 250-260
Author(s):  
Hongliang Sun ◽  
Jun Ye ◽  
Ke Zheng

2014 ◽  
Vol 1046 ◽  
pp. 305-309 ◽  
Author(s):  
Ji Li ◽  
Zhi Bin Zang ◽  
Da Peng Lin ◽  
Ye Shen He ◽  
Jing Jing Xie ◽  
...  

The remote communication is an important part of electric energy data acquisition system. Currently, the electric energy data acquisition remote communication has these disadvantages such as high costs of GPRS rental , poor reliability and it’s hard to meet the demand of real-time fee control and other senior business. To solve these problems, this paper proposes a solution for electric energy data acquisition remote communication channel by using built fiber access network to combine with broadband power line carrier communication technology .This solution have achieved a good result through field pilot application and validated the feasibility of it.


2021 ◽  
Author(s):  
Nicholas Parkyn

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.


Sign in / Sign up

Export Citation Format

Share Document