دانلود مقاله چگونگی ارتقا شبکه اینترنت اشیای صنعتی با فناوری های شبکه نرم افزار محور
خلاصه
1. مقدمه
2. پیشینه و موقعیت تحقیق فعلی
3. معماری AI-SDIN
4. فن آوری های کلیدی درگیر
5. برنامه های کاربردی بهبود یافته بر اساس AI-SDIN
6. فرصت و جهت های تحقیقاتی بیشتر
7. نتیجه گیری
سپاسگزاریها
منابع
Abstract
1. Introduction
2. Background and current research position
3. AI-SDIN architecture
4. Involved key technologies
5. Improved applications based on AI-SDIN
6. Opportunity and further research directions
7. Conclusion
Acknowledgements
References
چکیده
گسترش مداوم اینترنت اشیای صنعتی (IIoT) امکان کارآمد انقلاب صنعتی چهارم را فراهم می کند، جایی که دستگاه های حسگر عظیم در محیط های ناهمگن از طریق پروتکل های ارتباطی اختصاصی به هم متصل می شوند. این روش‌ها و مدل‌های جدیدی را برای ترکیب اطلاعات به‌دست‌آمده از عناصر مختلف کارخانه‌های صنعتی به ارمغان می‌آورد و چالش‌های امنیتی نوظهوری را ایجاد می‌کند که باید با آن‌ها مواجه شویم و عملکردهای موقتی را برای زمان‌بندی و تضمین عملیات شبکه ارائه می‌دهد. اخیراً، توسعه گسترده شبکه های تعریف شده با نرم افزار (SDN) و فناوری های هوش مصنوعی (AI) طراحی و کنترل شبکه های مقیاس پذیر و ایمن IIoT را امکان پذیر کرده است. این مقاله به بررسی این موضوع می‌پردازد که چگونه فناوری‌های هوش مصنوعی و SDN می‌توانند برای بهبود امنیت و عملکرد این شبکه‌های IIoT به کار گرفته شوند. پس از بررسی تلاش‌های تحقیقاتی پیشرفته در این موضوع، این مقاله یک معماری کاندید برای شبکه IIoT تعریف‌شده با نرم‌افزار فعال با هوش مصنوعی (AI-SDIN) معرفی می‌کند که شبکه‌های صنعتی سنتی را به سه لایه عملکردی تقسیم می‌کند. و با در نظر گرفتن این هدف، فناوری‌های کلیدی (اشتراک‌گذاری داده مبتنی بر بلاک چین، سنجش داده‌های بی‌سیم هوشمند، هوش لبه، شبکه های حساس به زمان، ادغام SDN&TSN، هوش مصنوعی توزیع شده) و بهبود برنامه های کاربردی مبتنی بر AI-SDIN نیز مورد بحث قرار می گیرند. علاوه بر این، این مقاله همچنین فرصت‌های جدید و چالش‌های پژوهشی بالقوه در کنترل و اتوماسیون شبکه‌های اینترنت اشیای صنعتی را برجسته می‌کند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
The ongoing expansion of the Industrial Internet of Things (IIoT) is enabling the possibility of effective Industry 4.0, where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols. This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face, providing ad-hoc functions for scheduling and guaranteeing the network operations. Recently, the large development of Software-Defined Networking (SDN) and Artificial Intelligence (AI) technologies have made feasible the design and control of scalable and secure IIoT networks. This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks. After surveying the state-of-the-art research efforts in the subject, the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network (AI-SDIN) that divides the traditional industrial networks into three functional layers. And with this aim in mind, key technologies (Blockchain-based Data Sharing, Intelligent Wireless Data Sensing, Edge Intelligence, Time-Sensitive Networks, Integrating SDN&TSN, Distributed AI) and improve applications based on AI-SDIN are also discussed. Further, the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.
Introduction
Currently, we are witnessing an industrial revolution ranging term as Industry 4.0. Although this term may not be fully correct and involves a certain level of hype, the generalized implementation of autonomous decision making systems has become a new industry per se. The industry 4.0 is built by integrating networking capabilities that interconnect Artificial Intelligence (AI) agents in industrial plants, leading to the paradigms of Industrial Internet of Things (IIoT) and smart manufacturing [1,2]. These subjects are attracting continuous attention from both academia and industry, and have been categorized in various prototype models [3,4].
Fig. 1 displays a typical architecture for IIoT networks, integrating four functional layers: data sensing, data transfer, data processing, and application layers [5,6]. The data sensing layer utilizes information acquisition and sensing technologies to collect heterogeneous data with different industrial attributes, and uploads them to the data transfer layer through advanced communication technologies, e.g., WiFi, Bluetooth, Internet of Things (IoT), 5G, etc. [7,8]. As the intermediated layer, the data transfer layer (based on the Internet backbone) aims at uploading the industrial data to the data processing layer by deploying Traffic Engineering (TE) or data routing policies, according to the given requirements and constraints (e.g., Quality of Service (QoS), costs, lags, etc.) of the industrial data delivery services [9]. Based on the dedicated data computing schemes (e.g., cloud/edge computing, distributed computing), the data processing layer adopts data fusion algorithms to process the data in real-time, and it provides an open interface for the application layer, such that the diverse categories of industrial applications can be edited and deployed at the application layer according to the current needs [10].
Conclusion
Although the paradigm of IIoT networks promotes the progress of smart manufacture towards achieving reduced costs and increased efficiency, IIoT networks present the constraints of network heterogeneity and scalability. Current network architectures integrate computing units of low-intelligence, which cannot support the scheduling for diverse and complex industrial flow – especially for case scenarios with delay-sensitivity requirements. In this paper, we argued that these issues can be addressed by utilizing an AI-enabled SDN technique, and we have presented a formal architecture named by AI-SDIN to achieve this vision. Within the presented architecture, the IIoT networks are divided into three functional layers: data layer, network layer, and control layer. In the latter, advanced machine intelligence can be deployed to improve the security and functionality of the network. Then, we have surveyed possible key technologies towards actual implementation – e.g., Blockchain-based data sharing, intelligent wireless data sensing, edge intelligence, TSN&SDN, distributed AI, etc. We have also presented potential enhanced industrial applications based on AI-SDIN ranging from logistics to agriculture. Finally, the paper has indicated challenges and areas where further research is required. Our paper aims at revealing that the AI-SDIN leads the future of smart manufacture to be clear and bright. With AI-SDIN, industrial manufacture will be supermatic with less manufacture time and intelligent logistics, supporting fast production and marketing.