Heterogenous Computational Intelligence in Internet of Things

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.

在過去幾年中，我們在網絡行業中看到了數據傳輸技術的大幅增長。我們可以看到，即使在當前的COVID-19大流行情況下，照片也能幫助臨床醫生檢測患者的感染情況。在機器學習/人工智能的幫助下，醫學影像學，如COVID-19感染的肺部X光檢查，對於許多疾病的早期檢測至關重要。我們還了解到，在COVID-19的情況下，有線和無線網絡都得到了改善，但存在網絡擁塞的問題。一個有趣的概念是提供無線網絡虛擬化，它具有減少頻譜擁塞並不斷提供新的網絡服務的能力。虛擬化程度和資源共享在不同範式之間有所不同。每個範式都有技術和非技術問題，在無線虛擬化成為常見技術之前，這些問題需要仔細設計和評估。未來的無線網絡架構必須遵守一些服務質量（QoS）要求。虛擬化已擴展到無線網絡以及傳統網絡。通過實現多租戶和定制服務以及更廣泛的載波頻率範圍，它提高了效率和利用率。在物聯網環境中，無線用戶是異構的，網絡狀態是動態的，這使得網絡控制問題極其難以解決，因為維度和計算複雜性迅速上升。深度強化學習（DRL）通過使用深度神經網絡（DNN）作為解決高維和連續控制問題的潛在方法而得到發展。

深度強化學習技術在物聯網、邊緣計算和軟件定義網絡（SDN）場景中具有巨大潛力，並且在物聯網基於每個軟件定義網絡（SDN）服務所需的QoS管理中用於異構網絡。儘管DRL已經顯示出在複雜的無線網絡虛擬化中解決新興問題的巨大潛力，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架構與5G網絡優化問題，資源發現和分配，開發智能機制，允許在SDN中自動和動態管理建立的虛擬通信，這被認為是研究的視角。

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.

Dr. Pawan Singh是印度拉克瑙阿米提大學阿米提工程與技術學院計算機科學與工程系的副教授。他在Magadh大學獲得計算機科學博士學位。他在研究和教學方面擁有超過十五年的經驗。他在SCI/SCIE/Scopus期刊和高聲譽的會議上發表了多篇研究論文。他還撰寫了多本書籍。他擁有多項國家和國際專利，其中一些已經獲得授權。他在IEEE、Elsevier等知名期刊上有貢獻。他還是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是印度北方邦馬圖拉GLA大學計算機工程與應用系的助理教授。他正在馬哈里希信息技術大學攻讀醫學影像學的博士學位。他在研究和教學方面擁有超過四年的經驗。他在SCI/SCIE/Scopus期刊和高聲譽的會議上發表了多篇研究論文。他還撰寫了一本關於雲計算的書籍。他擁有多項國家和國際專利，其中一些已經獲得授權。他在IEEE、Elsevier等知名期刊上有貢獻。他是目前學院研究諮詢成員團隊的一員。他目前的研究興趣包括圖像處理、醫學影像、人類計算界面、神經計算和物聯網。

Dr. Pramod Kumar Mishra在印度瓦拉納西的班納拉斯印度大學計算機科學與工程系擔任系主任和教授。他在APS大學完成了關於串行和並行計算機高效最短路徑算法的博士學位研究。他在研究和教學方面擁有超過三十年的經驗。他曾獲得多個知名組織的獎項和獎學金。他還獲得了國家和國際政府機構的多項資助。他在SCI/SCIE/Scopus期刊和高聲譽的會議上發表了多篇研究論文。他還撰寫了一本關於雲計算的書籍。他擁有多項國家和國際專利，其中一些已經獲得授權。他在IEEE、Elsevier等知名期刊上有貢獻。他是目前學院研究諮詢成員團隊的一員。他目前的研究興趣包括人工智能和機器學習算法、數據分析、並行計算、高性能集群、算法工程、高性能算法工程、並行計算和計算複雜性。

Dr. Avimanyou Vatsa在美國費爾利迪金森大學-提尼克分校計算機科學系擔任助理教授。他曾在西德克薩斯州立大學擔任助理教授，在密蘇里大學哥倫比亞分校擔任教學和研究助理，並在印度的多所工程學院和大學擔任助理教授超過十年。此外，他還在工業界擔任軟件工程師。他總是以一句話激勵和鼓舞學生：“沒有什麼是不可能的，只要你努力工作，堅持不懈地追求目標。”