Industrial Internet Of Things Concept Research Articles

A Dynamic IIoT Framework Based on the Publish-Subscribe Paradigm.

The use of the Internet of Things (IoT) technologies and principles in industrial environments is known as the Industrial Internet of Things (IIoT). The IIoT concept aims to integrate various industrial devices, sensors, and actuators for collection, storage, monitoring, and process automation. Due to the complexity of IIoT environments, there is no one-size-fits-all solution. The main challenges in developing an IIoT solution are represented by the diversity of sensors and devices, connectivity, edge/fog computing, and security. This paper proposes a distributed and customized IioT (Industrial Internet of Things) framework for the interaction of things from the industrial environment. This framework is distributed on the fog nodes of the IIoT architecture proposed, and it will have the possibility to interconnect local things (with low latency) or global things (with a latency generated by the Internet network). To demonstrate the functionality of the proposed framework, it is included in the fog nodes presented in other paper. These fog nodes allow the integration of CANOpen networks into an IioT architecture. The most important advantages of the proposed architecture are its customizability and the fact that it allows decision operations to be carried out at the edge of the network to eliminate latency due to the Internet.

MATRIX FACTORIZATION OF BIG DATA IN THE INDUSTRIAL SYSTEMS

The creation of new technologies and their implementation in various fields necessitated Big Data processing and storage. In industrial systems, modernization means the use of a large number of smart devices that perform specialized functions. Data from such devices are used to control the system and automate production processes. A change in the parameters of individual components of the manufacturing system may indicate the need to adjust the global management strategy. The intelligent industrial systems main characteristics were defined in the paper. The Industrial Internet of Things concept and the relevance of the modernization problem for manufacturing were analyzed. The problems of processing Big Data in Industrial Internet of Things systems were examined in the paper. The use of recommendation systems for quickly finding relationships between users and production services was considered. The advantages of Big Data analysis by recommendation systems, which have a favourable effect on industrial enterprise efficiency were given. The use of SVD and FunkSVD matrix factorization algorithms for processing sparse data matrices was analyzed. The possibility of optimizing arrays of information, choosing the most important, and rejecting redundancy with the help of the above algorithms was determined. The proposed algorithms were simulated. The advantages of FunkSVD for working with sparse data were assigned. It was found that the FunkSVD algorithm processes the data in a shorter time than SVD, but this does not affect the accuracy of the result. The SVD is also more difficult to implement and it requires more computing resources was established. It has been shown that FunkSVD uses a lot of data to determine the relationships between it and make recommendations about the products most likely to be of interest to users. To increase the efficiency of processing large sets of information the FunkSVD algorithm was improved in such a way that it uses fewer data to generate recommendations. Based on the results of the research, the modified method works faster than the non-modified one but retains high calculation accuracy, which is important for work in recommender systems. The possibility of providing recommendations to users of industrial systems in a shorter period, thus improving their relevance, was revealed. It was proposed to continue research for finding the optimal parameters of the FunkSVD algorithm for Big Data processing.

Applications of IoT in Manufacturing: Issues and Challenges

The Internet of Things (IoT) is the global network of inter-related physical devices such as sensors, objects, computing devices, mechanical instruments, smart applications and human resources that are becoming an essential part of the internet. These devices are the sources of data which provide abundant information in manufacturing processes. In an industrial environment, the Industrial Internet of Things (IIoT) plays a pivotal role in manufacturing. IIoT is the transformation of manufacturing process by making the industries more efficient, productive, and smarter. In order to survive, withstand in the market place and gain competitive advantage, the manufacturers are initiating to leverage IIoT technologies and data analytics. With globalization, global competitive pressures are challenging industries and manufacturing companies to manage the gaps in the workforce skills, drive out inefficiencies from the existing systems and enhance their business opportunities. The world is plunging into an era of data inter-connectivity and companies are able to enhance their production after adapting new technologies. In this context, this paper provides an understanding of the concept of IIoT and smart manufacturing. Further, the paper discusses in detail the issues concerning different applications of IoT in the industrial sector; and presents the challenges encountered in an IoT-based Industrial Data Management System (IDMS), which can manage the huge industrial data, support online monitoring and control smart manufacturing.

Industrial Internet-of-Things Security Enhanced With Deep Learning Approaches for Smart Cities

The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning (DL) techniques for IIoT in smart cities, mainly deep reinforcement learning, recurrent neural networks, and convolutional neural networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying DL techniques to enhance IIoT security.

The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system

With the fourth industrial revolution, which has become increasingly widespread in the manufacturing industry, traditional maintenance has been replaced by the industrial internet of things (IIoT) based on condition monitoring system (CMS). The IIoT concept provides easier and reliable maintenance. Unlike traditional maintenance, IIoT systems that perform real-time monitoring can provide great advantages to the company by notifying the related maintenance team members of the factory before a serious failure occurs. It is very important to detect faulty bearings before they reach the critical level during the rotation. In this study, an industry 4.0 compatible, IIoT based and low-cost CMS was created and it consists of three main parts. Firstly experimental setup, secondly IIoT based condition monitoring application (CMA) and finally machine learning (ML) models and their evaluation. The experimental setup contains mechanical and electronic materials. Although the most common method used in the classification of bearing damage is vibration data, it observed that characteristics such as sound level, current, rotational speed, and temperature should be included in the data set in order to increase the success of the classification. All these data were collected from the setup, which is 6203 type bearing connected to the universal motor shaft. The designed CMA provides real-time monitoring and recording of the data, which comes wirelessly from the setup, on a mobile device that has an Android operating system. The CMA can also send SMS and e-mail notifications to maintenance team supervisors over mobile devices in case critical thresholds are exceeded. Lastly, the data collected from the experimental setup was modeled for classification with popular ML algorithms such as support vector machine (SVM), linear discrimination analysis (LDA), random forest (RF), decision tree (DT), and k-nearest neighbor (kNN). The models were evaluated with accuracy, precision, TPR, TNR, FPR, FNR, F1 score and, Kappa metrics. During the evaluation of all models, it was observed that with the increase in the number of features in the data set, the accuracy, sensitivity, TPR, TNR, F1 score and Kappa metrics increased above 99% at 95% confidence interval, and FPR and FNR metrics fell below 1%. Although ML models gave successful results, LDA and DT models gave results much faster than others did. On the other hand, the classification success of the LDA model is relatively low. However, DT model is the optimum choice for CMS due to its convenience in determining threshold values, and its ability to give fast and acceptable classification rates.

A wayside hotbox system with fuzzy and fault detection algorithms in IIoT environment

Moving towards industrial internet of things (IIoT) concept is one of the hot issues of the management, control, and maintenance of railway transportation. The wayside hotbox systems are useful for monitoring of overheated axle bearings in the harsh dust environment. In the paper, the fuzzy-based fault detection algorithm (FDA) of the spatially distributed hotbox monitoring system (SDHMS) and its IIoT concept are presented. SDHMS consists of three stand-alone wayside hotbox monitoring systems that are installed in three distant locations: two thermal power plants and a coal mine. The system plays an important role in keeping railway safety, preventive maintenance and accident avoidance. Therefore, the IIoT concept is needed for increasing the reliability of coal transportation and the energy efficiency of electricity production. The main advantage of the presented concept is the application of the FDA at the edge and fog levels as well as the application of a complex fuzzy model for decision making at the cloud level. Edge computing involves signal processing in hotbox systems with the aim of the fault detection and quality assessment of the measured signals. Fog computing involves data processing with the aim of detecting an overheated bearing (temperature, position, axle number and train side), and alarming. Cloud computing is in charge of assessing the bearing condition and the need for replacement and maintenance planning. The proposed algorithm and fuzzy model were validated in real conditions of exploitation, successfully.

An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks

The green industrial Internet of things (IIoT) is emerging as a new paradigm, which envisions the concept of connecting different devices and reducing energy consumption. In multi-hop low power and lossy network, a resource-constrained node should aware of its energy consumption while routing the data packets. As part of IoT, the routing protocol for low power and lossy network (RPL) is considered to be a default routing standard. Recently, RPL has gained a significant maturity, but still, energy optimization is one of the main issues, because the default objective function (OF), which makes routing decision mainly based on a single parameter, such as link quality, and ignores the energy cost. Therefore, this paper aims to consider the concept of green IIoT concerning how a routing approach can achieve energy efficiency in resource-constrained IoT networks. For this, we propose a resource aware and reliable OF (RAROF), which constructs an optimum routing path by exploiting the information regarding the duty cycle, link quality, energy condition, and resource availability of a node. In addition, we propose node vulnerability index (NVI), a new routing metric that identifies the vulnerable nodes in terms of energy. To deal with the diverse data traffic of the IIoT network, we implement a multi-queuing based traffic differentiation approach that ensures the application requirements. The extensive simulation results show that the proposed RAROF can effectively extend the lifetime of the network, enhance the energy efficiency, and achieve higher reliability than that of other OFs. In this way, RAROF makes a routing decision with the purpose of extending network lifetime and minimizing energy depletion, paving the way towards green IIoT.

Industrial internet of things: Recent advances, enabling technologies and open challenges

The adoption of emerging technological trends and applications of the Internet of Things (IoT) in the industrial systems is leading towards the development of Industrial IoT (IIoT). IIoT serves as a new vision of IoT in the industrial sector by automating smart objects for sensing, collecting, processing and communicating the real-time events in industrial systems. The major objective of IIoT is to achieve high operational efficiency, increased productivity, and better management of industrial assets and processes through product customization, intelligent monitoring applications for production floor shops and machine health, and predictive and preventive maintenance of industrial equipment. In this paper, we present a new and clear definition of IIoT, which can help the readers to understand the concept of IIoT. We have described the state-of-the-art research efforts in IIoT. Finally, we have highlighted the enabling technologies for IIoT and recent challenges faced by IIoT.

Industrial Internet of Things concept and energy efficient technologies implementation at a fish drying unit

Modern trends in the availability of information and the widespread use of information and communication technologies not only in everyday life, but also in industry, make it clear that society is on the threshold of a fourth industrial revolution. Such terms as Information Society, Internet of Things, and Digital Economy are understood today as the main focus of state development on which the competitiveness of the national economy depends. The digital economy is primarily defined as a system of economic, social and cultural relations based on the use of digital information and communication technologies. The aim of the presented work is to develop the concept of the Industrial Internet of Things (IIoT) based on an experiment conducted at Murmansk State Technical University. The concept of the Internet of Things was implemented using a small-sized drying unit designed for carrying out the processes of drying and smoking fish and fish products. The proposed method of dewatering reduces the cost of electricity in the production of dried products, extends the life of smoking and drying facilities at the expense of more efficient use of coolant, and improves product quality by reducing the effects of deformation in fish tissues as a result of relaxation.

Reanalyzing a simplified Markov model for the low-density P2P wireless sensor and actuator networks

Wireless sensor and actuator networks (WSAN) are an important part of the emerging Industrial Internet of Things concept. Providing a computationally simple but relatively accurate analytical quality of service (QoS) model for the wireless communications technology is an essential step in developing distributed adaptive network protocols which try to satisfy the stringent industrial QoS requirements. In this paper, a novel Markov chain analysis and delay model has been proposed for the IEEE802.11-based WSANs applications which improves the accuracy while maintaining simplicity and considering the implicit industrial characteristics. Simulation results prove its superior performance compared with similar works.

Information systems: Characteristics of an IIoT concept implementation at a fish processing enterprise

This research focuses on relevant questions of the Industrial Internet of Things (IIoT) concept development and implementation at the enterprise. Lately IIoT has been widely publicized as a part of Industry 4.0. In this article the features of the information system development are presented, including SOA telecommunications that allow the remote access and control of the ongoing technological process with mobile devices. The capacity of the developed system is shown. The realization of the data exchange between developed telecommunications is highlighted. The practice of using the telecommunications, developed within the IIoT, has shown positive results and become the basics of implementation of an integrated information system that would link the other enterprise's objects.