Edge Computing for Industrial IoT: Challenges and Solutions

2020 ◽  
pp. 225-240
Author(s):  
Erkki Harjula ◽  
Alexander Artemenko ◽  
Stefan Forsström
Keyword(s):  
Edge Computing ◽  
Industrial Iot

Implementation of MQTT protocol based network architecture for smart factory

2021 ◽  
pp. 095440542110144
Author(s):  
Chia-Shin Yeh ◽  
Shang-Liang Chen ◽  
I-Ching Li
Keyword(s):  
Machine Tool ◽  
Gesture Recognition ◽  
Manufacturing Process ◽  
Network Architecture ◽  
Information Management System ◽  
Edge Computing ◽  
Smart Factory ◽  
Smart Manufacturing ◽  
Industrial Iot ◽  
Computing Module

The core concept of smart manufacturing is based on digitization to construct intelligent production and management in the manufacturing process. By digitizing the production process and connecting all levels from product design to service, the purpose of improving manufacturing efficiency, reducing production cost, enhancing product quality, and optimizing user experience can be achieved. To digitize the manufacturing process, IoT technology will have to be introduced into the manufacturing process to collect and analyze process information. However, one of the most important problems in building the industrial IoT (IIoT) environment is that different industrial network protocols are used for different equipment in factories. Therefore, the information in the manufacturing process may not be easily exchanged and obtained. To solve the above problem, a smart factory network architecture based on MQTT (MQ Telemetry Transport), IoT communication protocol, is proposed in this study, to construct a heterogeneous interface communication bridge between the machine tool, embedded device Raspberry Pi, and website. Finally, the system architecture is implemented and imported into the factory, and a smart manufacturing information management system is developed. The edge computing module is set up beside a three-axis machine tool, and a human-machine interface is built for the user controlling and monitoring. Users can also monitor the system through the dynamically updating website at any time and any place. The function of real-time gesture recognition based on image technology is developed and built on the edge computing module. The gesture recognition results can be transmitted to the machine controller through MQTT, and the machine will execute the corresponding action according to different gestures to achieve human-robot collaboration. The MQTT transmission architecture developed here is validated by the given edge computing application. It can serve as the basis for the construction of the IIoT environment, assist the traditional manufacturing industry to prepare for digitization, and accelerate the practice of smart manufacturing.

scholarly journals A Self-Configurable Edge Computing for Industrial IoT

2019 ◽  
Vol 9 (2) ◽  
pp. 2890-2894
Keyword(s):  
Material Handling ◽  
Industrial Revolution ◽  
Rapid Development ◽  
Edge Computing ◽  
Industrial Robots ◽  
Production Environment ◽  
Automatic Guided Vehicles ◽  
Industrial Wireless Networks ◽  
Industrial Iot ◽  
Smart Industry

The proliferation of Industrial Internet of Things (IIoT) introduces the concept of a smarter production environment. The emerging technologies like software defined network (SDN), IIoT and cloud computing will bring great advancements in the modern industrial revolution called Industry 4.0. Therefore, with the rapid development of IIoT technology, the proposed work incorporates with Edge Computing (EC). The current manufacturing process and automation, computing and wireless network reaches out to headways in innovation from easy to the point where all things (devices) and machines can interface through an Internet of Everything (IoE). This paper extends the work carried out in traditional methods, by integrating the cloud layer, Automatic Guided Vehicles (AGV), Industrial Wireless networks (IWN) and Industrial robots through EC is conferred to make autonomous decision-making capabilities. EC is emerging as a significant element in the smart industry to bring legacy in the context of Industrial IoT (IIoT). Finally, our proposed framework demonstrates that the active RFID-enabled AGV and industrial robots are brought in to exploit for effective resource management under the EC-based IIoT architecture, subsequently, it improves the conveyor efficiency and overall energy consumption in the warehouse for material handling.

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 Context-Aware Kubernetes Scheduler for Edge-native Applications on 5G

2020 ◽  
Vol 16 (1) ◽  
pp. 85-94
Author(s):  
Michael Chima Ogbuachi ◽  
Anna Reale ◽  
Péter Suskovics ◽  
Benedek Kovács
Keyword(s):  
Cloud Computing ◽  
Environmental Changes ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Proper Solution ◽  
Context Aware ◽  
Dynamic Information ◽  
Iot Applications ◽  
Industrial Iot ◽  
Cpu Usage

This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.

scholarly journals Aggregated multi-attribute query processing in edge computing for industrial IoT applications

2019 ◽  
Vol 151 ◽  
pp. 114-123 ◽  
Author(s):  
Xiaocui Li ◽  
Zhangbing Zhou ◽  
Junqi Guo ◽  
Shangguang Wang ◽  
Junsheng Zhang
Keyword(s):  
Query Processing ◽  
Edge Computing ◽  
Iot Applications ◽  
Industrial Iot
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