Learning Techniques for the Internet of Things

The book is structured into thirteen chapters; each comes with its own dedicated contributions and future research directions. Chapter 1 introduces IoT and the use of Edge computing, particularly cloud computing, and mobile edge computing. This chapter also mentions the use of edge computing in various real-time applications such as healthcare, manufacturing, agriculture, and transportation. Chapter 2 motivates mathematical modeling for federated learning systems with respect to IoT and its applications. Further Chapter 3 extends the discussion of federated learning for IoT, which has emerged as a privacy-preserving distributed machine learning approach. Chapter 4 provides various machine learning techniques in Industrial IoT to deliver rapid and accurate data analysis, essential for enhancing production quality, sustainability, and safety. Chapter discusses the potential role of data-driven technologies, such as Artificial Intelligence, Machine Learning, and Deep Learning, focuses on their integration with IoT communication technologies. Chapter 6 presents the requirements and challenges to realize IoT deployments in smart cities, including sensing infrastructure, Artificial Intelligence, computing platforms, and enabling communications technologies such as 5G networks. To highlight these challenges in practice, the chapter also presents a real-world case study of a city-scale deployment of IoT air quality monitoring within Helsinki city. Chapter 7 uses digital twins within smart cities to enhance economic progress and facilitate prompt decision-making regarding situational awareness. Chapter 8 provides insights into using Multi-Objective reinforcement learning in future IoT networks, especially for an efficient decision-making system. Chapter 9 offers a comprehensive review of intelligent inference approaches, with a specific emphasis on reducing inference time and minimizing transmitted bandwidth between IoT devices and the cloud. Chapter 10 summarizes the applications of deep learning models in various IoT fields. This chapter also presents an in-depth study of these techniques to examine new horizons of applications of deep learning models in different areas of IoT. Chapter 11 explores the integration of Quantum Key Distribution (QKD) into IoT systems. It delves into the potential benefits, challenges, and practical considerations of incorporating QKD into IoT networks. In chapter 12, a comprehensive overview regarding the current state of quantum IoT in the context of smart healthcare is presented, along with its applications, benefits, challenges, and prospects for the future. Chapter 13 proposes a blockchain-based architecture for securing and managing IoT data in intelligent transport systems, offering advantages like immutability, decentralization, and enhanced security.

這本書分為13個章節，每個章節都有專門的貢獻和未來研究方向。第1章介紹了物聯網和邊緣運算的使用，特別是雲運算和移動邊緣運算。該章還提到了邊緣運算在醫療、製造、農業和交通等各種實時應用中的使用。第2章以物聯網及其應用為背景，提出了對聯邦學習系統進行數學建模的動機。第3章進一步探討了物聯網中的聯邦學習，該方法是一種保護隱私的分散式機器學習方法。第4章介紹了工業物聯網中的各種機器學習技術，以實現快速準確的數據分析，這對提高生產質量、可持續性和安全性至關重要。第5章討論了數據驅動技術（如人工智能、機器學習和深度學習）在物聯網通信技術中的整合，並關注其潛在作用。第6章介紹了在智慧城市中實現物聯網部署所需的要求和挑戰，包括感測基礎設施、人工智能、計算平台和5G等通信技術。為了突出這些實踐中的挑戰，該章還介紹了赫爾辛基市內物聯網空氣質量監測的實際案例研究。第7章利用智慧城市中的數字孿生來提升經濟進步，並促進對情況的及時決策。第8章提供了在未來物聯網網絡中使用多目標強化學習的見解，特別是用於高效的決策系統。第9章全面回顧了智能推理方法，特別強調減少物聯網設備和雲之間的推理時間和傳輸帶寬。第10章總結了深度學習模型在各種物聯網領域的應用。該章還對這些技術進行了深入研究，以探討深度學習模型在物聯網不同領域的應用新視野。第11章探討了將量子密鑰分發（QKD）集成到物聯網系統中。該章深入探討了將QKD集成到物聯網網絡中的潛在好處、挑戰和實際考慮。第12章在智慧醫療的背景下，全面概述了量子物聯網的現狀，以及其應用、好處、挑戰和未來前景。第13章提出了一種基於區塊鏈的架構，用於保護和管理智能交通系統中的物聯網數據，提供了不可變性、去中心化和增強安全性等優勢。

Dr. Praveen Kumar Donta (Senior Member IEEE & Professional Member ACM), currently working as Postdoctoral researcher at Distributed Systems Group, TU Wien (Vienna University of Technology), Vienna, Austria. He is received his PhD. from Indian Institute of Technology (Indian School of Mines), Dhanbad in the field of Machine learning-based algorithms for wireless sensor networks in the year of 2021. From July 2019 to Jan 2020, he is a visiting Ph.D. fellow at Mobile \& Cloud Lab, Institute of Computer Science, University of Tartu, Estonia, under the Dora plus grant provided by the Archimedes Foundation, Estonia. He received his Master in Technology and Bachelor in Technology from the Department of Computer Science and Engineering at JNTUA, Ananthapur, with Distinction in 2014 and 2012. Currently, he is a Technical Editor and Guest Editor for Computer Communications, Elsevier, Editorial Board member for International Journal of Digital Transformation, Inderscience, Transactions on Emerging Telecommunications Technologies (ETT), Wiley. HE also serving as Early Career Editorial Board in Measurement and Measurement: Sensors, Elsevier journals. He served as IEEE Computer Society Young Professional Representative for Kolkata section. His current research includes Learning-driven Distributed Computing Continuum Systems, Edge Intelligence, and Causal Inference for Edge.
Dr. Abhishek Hazra currently works as an assistant professor in the Department of Computer Science and Engineering, Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh, India. He was a Post-doctoral Research Fellow at the Communications \& Networks Lab, Department of Electrical and Computer Engineering, National University of Singapore. He has completed his PhD at the Indian Institute of Technology (Indian School of Mines) Dhanbad, India. He received his M.Tech in Computer Science and Engineering from the National Institutes of Technology Manipur, India, and his B.Tech from the National Institutes of Technology Agartala, India. He currently serves as an Editor/Guest Editor for Physical Communication, Computer Communications, Contemporary Mathematics, IET Networks, SN Computer Science, Measurement: Sensors. He is also a conference general chair for IEEE PICom 2023. His research area of interest includes IoT, Fog/Edge Computing, Machine Learning, and Industry 5.0.
Dr. Lauri Loven (IEEE Senior Member) D.Sc. (Tech), is a senior member of IEEE and the coordinator of the Distributed Intelligence strategic research area in the 6G Flagship research program, at the Center for Ubiquitous Computing (UBICOMP), University of Oulu, in Finland. He received his D.Sc. at the university of Oulu in 2021, was with the Distributed Systems Group, TU Wien in 2022, and visited the Integrated Systems Laboratory at the ETH Zürich in 2023. His current research concentrates on edge intelligence, and on the orchestration of resources as well as distributed learning and decision-making in the computing continuum. He has co-authored 2 patents and ca. 50 research articles.

Dr. Praveen Kumar Donta（IEEE高級會員和ACM專業會員）目前在奧地利維也納工業大學（TU Wien）的分散系統研究小組擔任博士後研究員。他於2021年在印度工學院（印度礦業學院）獲得機器學習為基礎的無線感測器網絡算法博士學位。從2019年7月到2020年1月，他在愛沙尼亞塔爾圖大學計算機科學研究所的移動與雲實驗室擔任訪問博士研究員，該項目由愛沙尼亞阿基米德斯基金會提供Dora plus獎學金。他於2014年和2012年分別在JNTUA的計算機科學與工程系獲得碩士學位和學士學位，並獲得優異成績。目前，他是Elsevier的《計算機通信》技術編輯和客座編輯，Inderscience的《國際數字轉型期刊》編輯委員會成員，Wiley的《新興電信技術交易》編輯委員會成員。他還擔任Elsevier期刊《測量和測量：傳感器》的早期職業編輯委員會成員。他曾擔任IEEE計算機學會加爾各答分會的青年專業代表。他目前的研究領域包括基於學習的分散計算連續系統、邊緣智能和邊緣因果推斷。

Dr. Abhishek Hazra目前在印度安得拉邦錫里城的印度資訊技術學院計算機科學與工程系擔任助理教授。他曾在新加坡國立大學電機與電腦工程系通信與網絡實驗室擔任博士後研究員。他在印度工學院（印度礦業學院）丹巴德分校獲得博士學位。他在印度曼尼普爾國立技術學院獲得計算機科學與工程碩士學位，並在印度阿加爾塔拉國立技術學院獲得學士學位。他目前擔任《物理通信》、《計算機通信》、《當代數學》、《IET網絡》、《SN計算機科學》和《測量：傳感器》的編輯/客座編輯。他還是IEEE PICom 2023的會議主席。他的研究興趣包括物聯網、雲霧/邊緣計算、機器學習和工業5.0。

Dr. Lauri Loven（IEEE高級會員）是芬蘭奧盧大學普遍計算中心（UBICOMP）6G旗艦研究計劃中分散智能戰略研究領域的協調員。他於2021年在奧盧大學獲得D.Sc.學位，並於2022年在維也納工業大學的分散系統研究小組工作，並於2023年訪問了瑞士蘇黎世聯邦理工學院的集成系統實驗室。他目前的研究集中在邊緣智能、資源協調以及計算連續中的分散學習和決策。他共同撰寫了2項專利和約50篇研究論文。