Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020
Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing
Smart frost measurement for anti-disaster intelligent control in greenhouses via embedding IoT and hybrid AI methods
اندازه گیری یخ زدگی هوشمند برای کنترل هوشمند ضد فاجعه در گلخانه ها از طریق تعبیه روش های اینترنت اشیا و هوش مصنوعی ترکیبی-2020
A novel Agro-industrial IoT (AIIoT) technology and architecture for intelligent frost forecasting in greenhouses via hybrid Artificial Intelligence (AI), is reported. The Internet of Things (IoT) allows the objects interconnection on the physical world using sensors and actuators via the Internet. The smart system was designed and implemented through a climatological station equipped with Artificial Neural Networks (ANN) and a fuzzy associative memory (FAM) for ecological control of the anti-frost disaster irrigation. The ANN forecasts the inside temperature of the greenhouses and the fuzzy control predicts the cropland temperatures for the activation of five output levels of the water pump. The results were compared to a Fourier-statistical analysis of hourly data, showing that the ANN models provide a temperature prediction with effectiveness higher than 90%, as compared to monthly data model. Moreover, results of this process were validated through the determination of the coefficient of variance analysis method (R2).
Keywords: Smart frost measurement in greenhouses | Anti-frost irrigation | Artificial Neural Network | Fuzzy expert system | Internet-of-things | Hybrid AI methods
A new model to compare intelligent asset management platforms (IAMP)
مدل جدیدی برای مقایسه سیستم عامل های مدیریت دارایی هوشمند (IAMP)-2020
Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0)
Keywords: Asset Management | Industrial IoT | Digitalization | Predictive Analytics | Intelligent assets management systems
Edge computational task offloading scheme using reinforcement learning for IIoT scenario
طرح بارگیری وظیفه محاسباتی لبه با استفاده از یادگیری تقویتی برای سناریوی IIoT-2020
In this paper, end devices are considered here as agent, which makes its decisions on whether the network will offload the computation tasks to the edge devices or not. To tackle the resource allocation and task offloading, paper formulated the computation resource allocation problems as a sum cost delay of this framework. An optimal binary computational offloading decision is proposed and then reinforcement learning is introduced to solve the problem. Simulation results demonstrate the effectiveness of this reinforcement learning based scheme to minimize the offloading cost derived as computation cost and delay cost in industrial internet of things scenarios.
Keywords: Edge computing | Industrial IoT | Offloading | Reinforcement learning
Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT
تخصیص منابع پیشرفته در محاسبات لبه تلفن همراه با استفاده از الگوریتم MOACO مبتنی بر یادگیری تقویت کننده برای IIOT-2020
The Mobile networks deploy and offers a multiaspective approach for various resource allocation paradigms and the service based options in the computing segments with its implication in the Industrial Internet of Things (IIOT) and the virtual reality. The Mobile edge computing (MEC) paradigm runs the virtual source with the edge communication between data terminals and the execution in the core network with a high pressure load. The demand to meet all the customer requirements is a better way for planning the execution with the support of cognitive agent. The user data with its behavioral approach is clubbed together to fulfill the service type for IIOT. The swarm intelligence based and reinforcement learning techniques provide a neural caching for the memory within the task execution, the prediction provides the caching strategy and cache business that delay the execution. The factors affecting this delay are predicted with mobile edge computing resources and to assess the performance in the neighboring user equipment. The effectiveness builds a cognitive agent model to assess the resource allocation and the communication network is established to enhance the quality of service. The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has been applied to deal with the accurate resource allocation between the end users in the way of creating the cost mapping tables creations and optimal allocation in MEC
Keywords: Mobile edge computing | Industrial IOT | Reinforcement learning | Multi objective ant colony optimization | Resource allocation | Cognitive agent
کنترل بازخورد بی سیم با طول بسته متغیر برای اینترنت اشیا صنعتی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 18
این مقاله یک سیستم کنترل شبکه ای بی سیم (WNCS) را در نظر می گیرد، که یک کنترل کننده بسته های حامل اطلاعات کنترل را از طریق یک کانال بی سیم به یک فعال کننده می فرستد تا فرآیند فیزیکی را برای کاربردها و برنامه های کنترل صنعتی کنترل کند. در اکثر کارهای موجود در زمینه سیستم کنترل شبکه ای بی سیم (WNCS)، طول بسته برای انتقال ثابت است. با این حال، باتوجه به نظریه کدگذاری کانال، اگر یک پیام به صورت یک رمز طولانی تر کدگذاری شود، قابلیت اطمینان آن به ازای تاخیر طولانی تر بهبود خواهد یافت. تاخیر و قابلیت اطمینان بر عملکرد کنترل خیلی تاثیر دارند. این طور مبادله اساسی تاخیر- قابلیت اطمینان به ندرت در سیستم کنترل شبکه ای بی سیم (WNCS) درنظر گرفته می شود. در این مقاله، ما یک سیستم کنترل شبکه ای بی سیم (WNCS)را پیشنهاد می دهیم، که در آن کنترل کننده به طور تطبیقی طول بسته را برای کنترل براساس وضعیت فعلی فرایند فیزیکی تغییر می دهد. ما یک مسئله تصمیم گیری را فرمول بندی کرده و سیاست بهینه انتقال بسته با طول متغیر را برای حداقل کردن میانگین هزینه طولانی مدت سیستم های کنترل شبکه ای بی سیم (WNCS) پیدا می کنیم. ما برحسب قابلیت های اطمینان انتقال با طول های بسته متفاوت و پارامتر سیستم کنترل، یک شرط لازم و کافی برای وجود سیاست بهینه به دست می آوریم.
واژگان کلیدی: کنترل بی سیم | عمر اطلاعات | سیستم های فیزیکی سایبری | تحلیل کارایی | اینترنت اشیا صنعتی.
|مقاله ترجمه شده|
A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT
بررسی جامع در مورد حملات ، مسائل امنیتی و راه حلهای بلاکچین برای IoT و IIoT-2019
In recent years, the growing popularity of Internet of Things (IoT) is providing a promising opportunity not only for the development of various home automation systems but also for different industrial applications. By leveraging these benefits, automation is brought about in the industries giving rise to the Industrial Internet of Things (IIoT). IoT is prone to several cyberattacks and needs challenging approaches to achieve the desired security. Moreover, with the emergence of IIoT, the security vulnerabilities posed by it are even more devastating. Therefore, in order to provide a guideline to researchers, this survey primarily attempts to classify the attacks based on the objects of vulnerability. Subsequently, each of the individual attacks is mapped to one or more layers of the generalized IoT/IIoT architecture followed by a discussion on the countermeasures proposed in literature. Some relevant real-life attacks for each of these categories are also discussed. We further discuss the countermeasures proposed for the most relevant security threats in IIoT. A case study on two of the most important industrial IoT applications is also highlighted. Next, we explore the challenges brought by the centralized IoT/IIoT architecture and how blockchain can effectively be used towards addressing such challenges. In this context, we also discuss in detail one IoT specific Blockchain design known as Tangle, its merits and demerits. We further highlight the most relevant Blockchain-based solutions provided in recent times to counter the challenges posed by the traditional cloud-centered applications. The blockchain-related solutions provided in the context of two of the most relevant applications for each of IoT and IIoT is also discussed. Subsequently, we design a taxonomy of the security research areas in IoT/IIoT along with their corresponding solutions. Finally, several open research directions relevant to the focus of this survey are identified.
Keywords: IIoT | Security | Privacy | Blockchain | Smart Factory | Smart Grid | Supply Chain | E-Healthcare | VANET
Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry
چارچوب دوقلوی دیجیتال مبتنی بر یادگیری ماشین برای بهینه سازی تولید در صنعت پتروشیمی-2019
Digital twins, along with the internet of things (IoT), data mining, and machine learning technologies, offer great potential in the transformation of today’s manufacturing paradigm toward intelligent manufacturing. Production control in petrochemical industry involves complex circumstances and a high demand for timeliness; therefore, agile and smart controls are important components of intelligent manufacturing in the petrochemical industry. This paper proposes a framework and approaches for constructing a digital twin based on the petrochemical industrial IoT, machine learning and a practice loop for information exchange between the physical factory and a virtual digital twin model to realize production control optimization. Unlike traditional production control approaches, this novel approach integrates machine learning and real-time industrial big data to train and optimize digital twin models. It can support petrochemical and other process manufacturing industries to dynamically adapt to the changing environment, respond in a timely manner to changes in the market due to production optimization, and improve economic benefits. Accounting for environmental characteristics, this paper provides concrete solutions for machine learning difficulties in the petrochemical industry, e.g., high data dimensions, time lags and alignment between time series data, and high demand for immediacy. The approaches were evaluated by applying them in the production unit of a petrochemical factory, and a model was trained via industrial IoT data and used to realize intelligent production control based on real-time data. A case study shows the effectiveness of this approach in the petrochemical industry.
Keywords: digital twin | machine learning | internet of things | petrochemical industry | production control optimization
A Blockchain Tokenizer for Industrial IOT trustless applications
یک بلاکچین Tokenizer برای برنامه های کاربردی بی اعتماد صنعتی IOT-2019
The Blockchain is a novel technology with a wide range of potential industrial applications. Despite a vast range of tests, prototypes, and proof of concepts implemented in the last years, the industrial use of Blockchain technology is still in the early stages. Enabling the interaction of industrial Internet of Things (IOT) platforms with Blockchain might be challenging because standards are still missing in both these technologies. Moreover, integrating productive assets with distributed data exchange and storage technologies is a kind of activity that needs to take into account various aspects, in particular: interoperability, portability, scalability, and security that need to be guaranteed by design. This paper describes the implementation of a portable, platform-agnostic and secure Blockchain Tokenizer for Industrial IOT trustless applications. The Industrial Blockchain Tokenizer (IBT) is based on an industrial data acquisition unit able to gather data from both modern and legacy machines while also interfacing directly with sensors. Acquired data can be processed locally enabling an edge filtering paradigm and then sent to any Blockchain platform. The system has been designed, implemented and then tested on two supply chain scenarios. Tests demonstrated the system capability to act as a bridge between industrial assets and Blockchain platforms enabling the generation of immutable and trust-less ‘‘digital twins’’ for industrial IOT applications.
Keywords: Industrial IOT | Blockchain | Ethereum | Smart contract | Supply chain | Industry 4.0
Big Data Analytics in Industrial IoT Using a Concentric Computing Model
تجزیه و تحلیل داده های بزرگ در اینترنت اشیا صنعتی با استفاده از یک مدل محاسباتی مرکزی-2018
The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).
Keywords: Big Data, data analysis, Internet of Things,learning (artificial intelligence)