scholarly journals Cómputo en la niebla aplicado a la manufactura inteligente bajo el contexto de la industria 4.0: Desafíos y oportunidades

2019 ◽  
pp. 16-27
Author(s):  
Mariela Juana Alonso-Calpeño ◽  
Julieta Santander-Castillo ◽  
Yuridia Ramírez-Chocolatl ◽  
Raúl Alanis-Teutle
Keyword(s):  
Data Processing ◽  
Vertical Integration ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Time Data ◽  
Industrial Systems ◽  
Computing Paradigm ◽  
Industrial Internet ◽  
Level Data ◽  
Real Time Data Processing

Cloud computing offers high server-level data processing capacity, while fog computing works using nodes at the edge of the network, enabling real-time data processing with low latency and improved ubiquity, so it can contribute on Industrial Internet of Things (IIoT) applications. This article discusses the technical challenges that have arisen in implementing the IIoT, and how the fog computing paradigm is helping to solve some of them. For this, a review of scientific articles in the Google Scholar and Web of Science databases has been carried out using keywords. The results show that there are various challenges related to interoperability, mixed criticality, latency, fault tolerance, scalability, horizontal and vertical integration, functional safety, legacy industrial systems, and energy efficiency. The main trends to face these challenges are reported. This article proposes a series of opportunity areas for research and development of possible solutions.

scholarly journals Fog Computing Mitigate Limitations of Cloud Computing

2020 ◽  
Vol 8 (5) ◽  
pp. 1732-1736
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Public Cloud ◽  
Time Data ◽  
Security Issues ◽  
Computing Model ◽  
Real Time Data ◽  
Real Time Data Processing

In today’s world, cloud computing is the most exciting and advanced technology. It came into existence with lots of advantages, but cloud-only computing has some disadvantages also like latency in real-time data processing, network congestion, less bandwidth utilization, fault tolerance, and security issues in public cloud. To address the issue of real-time data-processing and security in public cloud new computing model are used which is known as Fog Computing. It is nearer to the client or edge so that it can reduce the latency in real time data-processing and security in public cloud using techniques like user profiling and decoying technique. Fog Computing help us to overcome the latency and security issues of cloud computing. It reduces cloud latency in real time data-processing because fog computing model is nearer to the edge devices. It also improves cloud security in the public cloud.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

scholarly journals Distributed Watchdogs Based on Blockchain for Securing Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4393
Author(s):  
JongHyup Lee ◽  
Taekyoung Kwon
Keyword(s):  
Internet Of Things ◽  
Software Protection ◽  
Industrial Internet Of Things ◽  
Industrial Systems ◽  
Industrial Internet ◽  
Unique Identity ◽  
Protection Mechanisms ◽  
Software Updates ◽  
Detailed Specification ◽  
Industrial Environments

The Industrial Internet of Things (IIoT) could enhance automation and analytics in industrial environments. Despite the promising benefits of IIoT, securely managing software updates is a challenging problem for those critical applications. This is due to at least the intrinsic lack of software protection mechanisms in legacy industrial systems. In this paper, to address the challenges in building a secure software supply chain for industrial environments, we propose a new approach that leverages distributed watchdogs with blockchain systems in protecting software supply chains. For this purpose, we bind every entity with a unique identity in the blockchain and employ the blockchain as a delegated authenticator by mapping every reporting action to a non-fungible token transfer. Moreover, we present a detailed specification to clearly define the behavior of systems and to apply model checking.

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

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