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نتیجه جستجو - اینترنت اشیا صنعتی

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 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
مقاله انگلیسی
2 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
مقاله انگلیسی
3 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
مقاله انگلیسی
4 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
5 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)
مقاله انگلیسی
6 High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things
مدلسازی و تلفیق سطح بالای شبکه های حسگر هوشمند در اینترنت اشیا صنعتی -2017
In this work, we use a high-level design methodology for the rapid hardware synthesis of a complex smart sensor network (SSN) system. The GRAFCET is then used to model the individual functional modules and the hierarchical behavior of the system. The behavior of each module is represented as a sequential–concurrent hybrid discrete event system. We apply high-level synthesis rules to generate a VHSIC hardware description language (VHDL)-target efficient hardware for a smart sensor controller and smart gateway con troller. Finally, these embedded hardware controllers are generated automatically to inte grate all intelligent functional modules into a complex embedded system, and a hardware circuit is then synthesized. The experimental results show that the hardware circuit can meet the definition of an SSN system for Industrial Internet of Things applications. More over, this methodology enables a coherent design quality, short design period, low devel opment cost, and short time-to-market for complex industrial applications.
Keywords: Discrete-event modeling | High-level synthesis | Industrial Internet of Things (IIoT) | Smart sensor network
مقاله انگلیسی
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