Heterogenous Computational Intelligence in Internet of Things
暫譯: 物聯網中的異質計算智能
Singh, Pawan, Singhal, Prateek, Mishra, Pramod Kumar
相關主題
商品描述
We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively.
Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective.
商品描述(中文翻譯)
我們在過去幾年中看到網路產業中數據傳輸技術的快速發展。我們可以看到，照片正在幫助臨床醫生在當前 COVID-19 大流行的情況下檢測患者的感染。借助機器學習（ML）和人工智慧（AI），醫療影像，例如 COVID-19 感染的肺部 X 光片，對於許多疾病的早期檢測至關重要。我們還了解到，在 COVID-19 的情境中，有線和無線網路在數據傳輸方面都有所改善，但也出現了網路擁塞。一個有趣的概念是提供無線網路虛擬化，這能夠減少頻譜擁塞並持續提供新的網路服務。不同範式之間的虛擬化程度和資源共享各不相同。每個範式都有技術和非技術問題需要解決，才能使無線虛擬化成為一項普遍技術。為了使無線網路虛擬化成功，這些問題需要仔細的設計和評估。未來的無線網路架構必須遵循多項服務質量（QoS）要求。虛擬化已擴展到無線網路以及傳統網路。通過啟用多租戶和量身定制的服務，並提供更廣泛的載波頻率，它提高了效率和利用率。在物聯網（IoT）環境中，無線用戶是異質的，網路狀態是動態的，這使得網路控制問題變得極其困難，因為維度和計算複雜性迅速上升。深度強化學習（DRL）是利用深度神經網路（DNN）發展出來的一種潛在方法，能有效解決高維和連續控制問題。
深度強化學習技術在物聯網、邊緣計算和軟體定義網路（SDN）場景中具有巨大的潛力，並用於異質網路中，以滿足每個軟體定義網路服務所需的 QoS。儘管 DRL 在解決複雜無線網路虛擬化中的新興問題方面顯示出巨大的潛力，但仍然存在特定領域的挑戰需要進一步研究，包括設計適當的 DNN 架構以解決 5G 網路優化問題、資源發現和分配、以及開發智能機制以實現自動化和動態管理在 SDN 中建立的虛擬通信，這被視為研究的前景。
作者簡介
Dr. Pawan Singh is an Associate Professor in the Department of Computer Science & Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow, India. He has completed Ph.D. degree in Computer Science from Magadh University, Gaya. He has more than fifteen years of experience in research and teaching. He has published several research articles in SCI/SCIE/Scopus journals and conferences of high repute. He has also authored various books. He has various National and international patents and some are granted. He holds contributions in IEEE, Elsevier, etc. repute journals. He is also a reviewer in various reputed journals. His current areas of interest include Computer Networks, Parallel Processing and Internet of Things.
Mr. Prateek Singhal is an Assistant Professor in the Department of Computer Engineering & Applications at GLA University, Mathura, Uttar Pradesh. He is pursuing a Ph.D. degree in Medical Imaging from the Maharishi University of Information Technology, Lucknow, India. He has more than four years of experience in research and teaching. He has published several research articles in SCI/SCIE/Scopus journals and conferences of high repute. He has also authored a book on Cloud Computing. He has various National and international patents and some are granted. He holds contributions to IEEE, Elsevier, etc. reputed journals. He is on the team of the research advisory member in his present institute. His current areas of interest include Image Processing, Medical Imaging, Human Computation Interface, Neuro-Computing, Internet of Things.
Dr. Pramod Kumar Mishra is working as a Head and Professor in the Department of Computer Science & Engineering at Banaras Hindu University, Varanasi. He has completed Ph.D. degree on A study of efficient shortest path algorithms for serial and parallel computers from APS University, Rewa, India. He has more than Thirty years of experience in research and teaching. He has received various Awards and fellowships from the good repute organizations. He has also received various grants from national and international government bodies/Agency. He has published several research articles in SCI/SCIE/Scopus journals and conferences of high repute. He has also authored a book on Cloud Computing. He has various National and international patents and some are granted. He holds contributions in IEEE, Elsevier, etc. reputed journal. He is in the team of the research advisory member in his present institute. His current areas of interest include AI and Machine Learning Algorithms, Data Analytics, Parallel Computing, High-Performance Clusters, Algorithm Engineering (AE), High-Performance AE, Parallel Computation, and Computational complexity.
Dr. Avimanyou Vatsa is working as an assistant professor in the department of computer science, Fairleigh Dickinson University - Teaneck. He also worked as an assistant professor at West Texas A&M University, a teaching & research assistant at the University of Missouri, Columbia, and an assistant professor for more than ten years in several engineering colleges and a university in India. Also, he worked as a software engineer in the industry. He always motivates and inspires students with a statement: "Nothing is impossible, just put your hard work and sincere effort persistently toward your goal.
作者簡介(中文翻譯)
Dr. Pawan Singh 是印度烏塔爾邦勒克瑙阿米提大學工程與技術學院計算機科學與工程系的副教授。他在馬加德大學（Magadh University, Gaya）獲得計算機科學的博士學位，擁有超過十五年的研究和教學經驗。他在SCI/SCIE/Scopus期刊及高聲望的會議上發表了多篇研究文章，並著有多本書籍。他擁有多項國內外專利，其中一些已獲得批准。他在IEEE、Elsevier等知名期刊上有貢獻，並擔任多個知名期刊的審稿人。他目前的研究興趣包括計算機網絡、並行處理和物聯網。
Mr. Prateek Singhal 是印度烏塔爾邦馬圖拉GLA大學計算機工程與應用系的助理教授。他正在印度勒克瑙的馬哈里希信息技術大學攻讀醫學影像的博士學位，擁有超過四年的研究和教學經驗。他在SCI/SCIE/Scopus期刊及高聲望的會議上發表了多篇研究文章，並著有一本關於雲計算的書籍。他擁有多項國內外專利，其中一些已獲得批准。他在IEEE、Elsevier等知名期刊上有貢獻，並在目前的學院擔任研究諮詢成員。他目前的研究興趣包括影像處理、醫學影像、人機計算介面、神經計算和物聯網。
Dr. Pramod Kumar Mishra 擔任印度瓦拉納西的班納拉斯印度大學計算機科學與工程系的系主任及教授。他在印度瑞瓦的APS大學獲得博士學位，研究主題為「串行和並行計算機的高效最短路徑演算法研究」，擁有超過三十年的研究和教學經驗。他曾獲得多個知名機構的獎項和獎學金，並獲得來自國內外政府機構的多項資助。他在SCI/SCIE/Scopus期刊及高聲望的會議上發表了多篇研究文章，並著有一本關於雲計算的書籍。他擁有多項國內外專利，其中一些已獲得批准。他在IEEE、Elsevier等知名期刊上有貢獻，並在目前的學院擔任研究諮詢成員。他目前的研究興趣包括人工智慧和機器學習演算法、數據分析、並行計算、高效能集群、演算法工程（AE）、高效能AE、並行計算和計算複雜性。
Dr. Avimanyou Vatsa 擔任美國費爾利迪金森大學（Fairleigh Dickinson University）計算機科學系的助理教授。他曾在西德克薩斯A&M大學擔任助理教授，並在密蘇里大學哥倫比亞校區擔任教學與研究助理，此外在印度的多所工程學院和一所大學擔任助理教授超過十年。他也曾在業界擔任軟體工程師。他總是以「沒有什麼是不可能的，只要持之以恆地努力工作並真誠地朝著目標前進」來激勵和啟發學生。
