اینترنت اشیای شناختی صنعتی
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اینترنت اشیای شناختی صنعتی

عنوان فارسی مقاله: سرمقاله: اینترنت اشیای شناختی صنعتی
عنوان انگلیسی مقاله: Editorial: Cognitive Industrial Internet of Things
مجله/کنفرانس: شبکه های موبایل و برنامه های کاربردی - Mobile Networks and Applications
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، سامانه های شبکه ای، شبکه های کامپیوتری
نوع نگارش مقاله: سرمقاله (Editorial)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1007/s11036-018-1115-y
دانشگاه: School of Computer Science and Technology, Huazhong University of Science and Technology (HUST), Wuhan, China
ناشر: اسپرینگر - Springer
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 2/850 در سال 2018
شاخص H_index: 79 در سال 2019
شاخص SJR: 0/426 در سال 2018
شناسه ISSN: 1572-8153
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 3
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E11258
نمونه متن انگلیسی مقاله

Editorial


Following the great success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks or 5th generation wireless systems (in short 5G) was proposed aiming to provide infinite networking capability to mobile users. Different from 4G, 5G is much more than increased maximum throughput. It aim to involve and benefit from many current technical advances including massive dense networks, interference and mobility management, Internet of Things (IoT), pervasive and social computing, mobile ad hoc networks (MANET), cognitive radio, World Wide Wireless Web (WWWW), cloud computing, IPv6, and so on. How 5G should and will be, what will be the keys for 5G? What is the perspective of 5G architecture and technologies? How to effectively apply and benefit from the above technologies and make them intelligently interoperate together? How can 5G stimulate our innovation for the next generation of mobile networks and services? Obviously, integrating all above existing advanced technologies and innovating new techniques for 5G bring extreme challenges on 5G networks and services in both research and development. This study has just initiated in both industry and academia, but with great fervor all over the world. Cognitive Industrial Internet of Things (Cognitive-IIoT) is the use of cognitive computing technologies, which is derived from cognitive science and artificial intelligence, to power next generation Industrial IoT. With the growth and adoption of IoT, factories are becoming more instrumented and interconnected. Cognitive-IIoT provides high performance of communicating, computing, controlling, and even high degree of machine intelligence for emerging smart industrial IoT applications, such as cognitive manufacturing and Industry 4.0. Cognitive-IIoT redefines the relationship between human, machine and their pervasive digital environments. They may play the role of assistant or coach for the manufacturers, and industrial technologists. Specifically, the Industrial IoT generated industrial big data, when used to power predictive analytics algorithms or to develop a corps for a cognitive computing solution, can provide insights that would never be discovered in time to be useful if the departmental silos do not collaboration in data sensing and analysis. It is the integration of this data that enables cognitive computing applications for Industrial IoT of the next decade. Therefore, the services of a Cognitive-IIoT could be constructive, prescriptive, or instructive in nature. This special issue aims to explore recent advances and disseminate state-of-the-art research related to Cognitive-IIoT on designing, building, and deploying novel cognitive computing, services and technologies, to enable smart industrial IoT services and applications. This special issue features six selected papers with high quality. The first article, BCrossRec: Cross-domain Recommendations based on Social Big Data and Cognitive Computing^, authored by Yin Zhang et al., considered the advantages of social-based and cross-domain approaches involving further additional data. They proposed a cross-domain recommender system, including three approaches, based on multisource social big data. The proposed approach is available to to effectively alleviate the issues of cold-start users by transferring user preferences from a related auxiliary domain to a target domain. Moreover, the sufficient experiments show that the proposed system is significantly improved compared with the conventional recommender approaches, such as collaborative filtering and matrix factorization.

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