End-to-End-Architekturen zur Datenmonetarisierung im Industrial Internet of Things (IIoT)

2021 ◽  
pp. 169-206
Author(s):  
Christoph F. Strnadl
Keyword(s):  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End

scholarly journals A Novel Edge Security Gateway for End-to-End Protection in Industrial Internet of Things

2021 ◽  
Author(s):  
Rafiullah Khan ◽  
Kieran McLaughlin ◽  
BooJoong Kang ◽  
David Laverty ◽  
Sakir Sezer
Keyword(s):  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Security Gateway ◽  
End To End

scholarly journals A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

2021 ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

Abstract Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

scholarly journals A risk-sensitive task offloading strategy for edge computing in industrial Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

AbstractEdge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst-case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer nonlinear programming problem, a decomposition into subproblems is performed and a two-stage heuristic algorithm is proposed. The simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

scholarly journals A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

2020 ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

Abstract Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

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