Edge Computing architecture to support Real Time Analytic applications : A State-of-the-art within the application area of Smart Factory and Industry 4.0

2018 ◽  
Author(s):  
Sebastian Trinks ◽  
Carsten Felden
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
State Of The Art ◽  
Edge Computing ◽  
Smart Factory ◽  
Application Area ◽  
Computing Architecture

scholarly journals A Modular and Scalable Software Reference Architecture for Decentralized Real-Time Execution on Edge Computing

2021 ◽  
Author(s):  
Volkan Gezer
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
State Of The Art ◽  
Problem Definition ◽  
Edge Computing ◽  
Reference Architecture ◽  
Conceptual Approach ◽  
Best Effort ◽  
Scalable Software

Edge Computing is expected to solve the latency and best-effort delivery problems of the renowned Cloud Computing. However, Edge Computing must be supported with a vendor-independent, scalable, and decentralized software reference architecture to fully exploit its benefits. This dissertation explains the enablers, requirements, and conceptual approach to create such architecture, and validates it with a framework to show its possibilities. Contents Foreword III Abstract VII Kurzfassung VIII List of Abbreviations IX 1 Introduction 1 1.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 State of the Art 11 2.1 Cloud Computing and Edge Computing . . . . . . . . . . . . . . . . . . . . . . 11 2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.1.3 Enablers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Real‐Tim...

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.

Smart Factory of Industry 4.0: Connection Infrastructure, Data Acquisition, Data Processing, and Applications

2019 ◽  
Author(s):  
Zhao Zhiqiang ◽  
Chua Wei Quan ◽  
Ding Xiaoming ◽  
Prabhu Vinayak Ashok
Keyword(s):  
Data Processing ◽  
Data Acquisition ◽  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Discrete Systems ◽  
Smart Factory ◽  
Hierarchical Architecture ◽  
Support Functions ◽  
Control Functions

Abstract Smart factory adopts cyber-physical technologies integrating independent discrete systems into a context-sensitive manufacturing environment to optimize manufacturing processes using decentralized information and real-time communication. This paper presents our work in the realization of a smart factory, which comprises of a four-layer hierarchical architecture, i.e. connection infrastructure, data acquisition, data processing and smart applications. In the connection infrastructure layer, all shopfloor machines are connected through diverse protocols, IoT sensors, PLC interfaces and DNC connectors. A centralized IoT gateway supports such a scalable and adaptable connection and ensures a reliable communication among all heterogeneous manufacturing systems. In the data acquisition layer, the real-time machine and job data are acquired from shopfloor systems. Machine data indicates machines’ working condition and job data reveals the production information. The data processing layer comprises of three modules, i.e. shopfloor monitoring, data visualization and data analytics, which monitor and visualize shopfloor activities and analyze the semantics of various data using AI-based TPM engines providing the scientific indicators for next-step decisions. The smart application layer provides with several decision-making and remote control functions for shopfloor productivity and efficiency, such as predictive maintenance, shopfloor management, machine & job optimization and digital twin. The smart factory system has been implemented in the manufacturing shopfloor at Nanyang Polytechnic. The results and validation show that the system can simultaneously collect and analyze the manufacturing data from shopfloor systems, and further communicate with and control the shopfloor systems with decision-support functions. The overall shopfloor efficiency and flexibility can be significantly improved towards a smart factory of Industry 4.0.

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.

scholarly journals Real-time Data Analytics Edge Computing Application for Industry 4.0: The Mahalanobis-Taguchi Approach

2020 ◽  
Vol 11 (3) ◽  
pp. 146-156
Author(s):  
Bojana Bajic ◽  
◽  
Nikola Suzic ◽  
Nenad Simeunovic ◽  
Slobodan Moraca ◽  
...  
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Data Analytics ◽  
Edge Computing ◽  
Taguchi Approach ◽  
Time Data ◽  
Real Time Data

scholarly journals Method for Detection of Unsafe Actions in Power Field Based on Edge Computing Architecture

2021 ◽  
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

Abstract Due 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 by 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 executing, 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 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 cgnition 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.

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