Industrial Internet of Things: covering standardization gaps for the next generation of reconfigurable production systems

Author(s):  
Olga Meyer ◽  
Greg Rauhoeft ◽  
Daniel Schel ◽  
Daniel Stock
Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.


Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiabao Sun ◽  
Ting Yang ◽  
Zhiying Xu

PurposeThe increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.Design/methodology/approachIntelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R2, T-Value and GOF.FindingsThe study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.Research limitations/implicationsStudy limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.Originality/valueThe research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.


2020 ◽  
Author(s):  
Karthik Muthineni

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.


Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский

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


2021 ◽  
Vol 173 ◽  
pp. 150-159
Author(s):  
Keming Mao ◽  
Gautam Srivastava ◽  
Reza M. Parizi ◽  
Mohammad S. Khan

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