scholarly journals Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments

Actuators ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 197
Author(s):  
Rongxu Xu ◽  
Lei Hang ◽  
Wenquan Jin ◽  
Dohyeun Kim
Keyword(s):  
Real Time ◽  
Strong Dependence ◽  
Data Integrity ◽  
Data Encryption ◽  
Edge Computing ◽  
Time Data ◽  
Computing Architecture ◽  
Security Issues ◽  
Multiple Data ◽  
Cloud Servers

The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety to solve these problems of the IoT. In this paper, we present a distributed secure edge computing architecture using multiple data storages and blockchain agents for the real-time context data integrity in the IoT environment. The proposed distributed secure edge computing architecture provides reliable access and an unlimited repository for scalable and secure transactions. The architecture eliminates traditional centralized servers using an edge computing framework that represents cloud computing for computer and security issues. Also, blockchain-based edge computing-compatible IoT design is supported to achieve the level of security and scalability required for data integrity. Furthermore, we present the blockchain agent to provide internetworking between blockchain networks and edge computing. For experimenting with the proposed architecture in the IoT environment, we implement and perform a concrete IoT environment based on the EdgeX framework and Hyperledger Fabric. The evaluation results are collected by measuring the performance of the edge computing and blockchain platform based on service execution time to verify the proposed architecture in the IoT environment.

scholarly journals Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Huo ◽  
Chengtao Yong ◽  
Yanfei Lu
Keyword(s):  
Real Time ◽  
Data Aggregation ◽  
Differential Privacy ◽  
Fog Computing ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Data Availability ◽  
Time Data ◽  
Real Time Data ◽  
Cloud Servers

In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive ω-event differential privacy (Re-ADP). Based on adaptive ω-event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.

5G Edge Cloud Power Real-time Inspection Technology Based on YOLOV4-Tiny

2021 ◽  
Author(s):  
Jiaqi Song ◽  
Jing Li ◽  
Di Wu ◽  
Guangye Li ◽  
Jiaxin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
High Efficiency ◽  
Real Data ◽  
Detection Algorithm ◽  
Power Line ◽  
Edge Computing ◽  
Inspection Method ◽  
Computing Architecture ◽  
Low Efficiency

Power line corridor inspection plays a vital role in power system safe operation, traditional human inspection’s low efficiency makes the novel inspection method requiring high precision and high efficiency. Combined with the current deep learning target detection algorithm based on high accuracy and strong real-time performance, this paper proposes a YOLOV4-Tiny based drone real-time power line inspection method. The 5G and edge computing technology are combined properly forming a complete edge computing architecture. The UAV is treated as an edge device with a YOLOV4-Tiny deep- learning-based object detection model and AI chip on board. Extensive experiments on real data demonstrate the 5G and Edge computing architecture could satisfy the demands of real-time power inspection, and the intelligence of the whole inspection improved significantly.

scholarly journals Method for detection of unsafe actions in power field based on edge computing architecture

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yanfang Yin ◽  
Jinjiao Lin ◽  
Nongliang Sun ◽  
Qigang Zhu ◽  
Shuaishuai Zhang ◽  
...  
Keyword(s):  
Electric Power ◽  
Real Time ◽  
Action Recognition ◽  
Electric Power Industry ◽  
Edge Computing ◽  
Classification Rule ◽  
Computing Architecture ◽  
The Real ◽  
Time Performance ◽  
Temporal Features

AbstractDue to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators’ nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method.

AI-enabled IoT-Edge Data Analytics for Connected Living

2021 ◽  
Vol 21 (4) ◽  
pp. 1-20
Author(s):  
Zhihan Lv ◽  
Liang Qiao ◽  
Sahil Verma ◽  
Kavita
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Data Centers ◽  
Edge Computing ◽  
Time Data ◽  
Processing Task ◽  
Light Load ◽  
Hash Algorithm ◽  
Real Time Data ◽  
Real Time Data Processing

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

Security Issues on Internet of Things in Smart Cities

2019 ◽  
pp. 149-164
Author(s):  
C. Thilagavathi ◽  
M. Rajeswari ◽  
Sheethal M. S. ◽  
Deepa Devassy ◽  
Priya K. V. ◽  
...  
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Heavy Traffic ◽  
Time Data ◽  
Unauthorized Access ◽  
Central Processing ◽  
Security Issues ◽  
Traffic Congestion Control ◽  
Authentication Security ◽  
Security Analytics

Many researchers are focusing on IoT in smart cities. It invites researchers to concentrate on simplifying engineering challenges. IoT includes recognition, locating, tracking, monitoring, and management of devices in a reliable manner. There are numerous security challenges that include network security, authentication, security-side-challenge attacks, security analytics, interface protection, delivery mechanism, and system development. IoT needs security analytics to overcome a number of problems in smart cities to prevent unauthorized access. One among the security analytics is streaming analytics, which include all the real-time data streams to detect emergency situations. Threat detection and behavioral monitoring will be done after analyzing the traffic data. The aim is to analyze and predict real-time streaming data to achieve security. Different analytical tools on security will be used to obtain the optimal result in smart cities. Traffic analysis, which is treated as a real-time stream, will be applied in street and traffic lights, transportation, and parking space occupancy and so on. Large volume of data that are received from different sensors and cameras will be given as the input in order to analyze traffic in a smart cities. Intelligent traffic congestion control system will be developed in order to analyze the heavy traffic on roadside. Security in IoT is proposed, which includes encrypting and decrypting the user request, which is further to be processed by the central processing hub, in order to prevent unauthorized access.

Sign in / Sign up

Export Citation Format

Share Document