商品描述
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.
商品描述(中文翻譯)
本書共分為十三章，每章均有其專門的貢獻和未來研究方向。第一章介紹物聯網（IoT）及邊緣計算的應用，特別是雲計算和移動邊緣計算。本章還提到邊緣計算在各種實時應用中的使用，例如醫療保健、製造業、農業和交通運輸。第二章激勵針對物聯網及其應用的聯邦學習系統進行數學建模。第三章進一步擴展了針對物聯網的聯邦學習討論，這種方法已成為一種保護隱私的分散式機器學習方法。第四章提供了工業物聯網中的各種機器學習技術，以實現快速和準確的數據分析，這對於提高生產質量、可持續性和安全性至關重要。第五章討論數據驅動技術的潛在角色，例如人工智慧（Artificial Intelligence）、機器學習（Machine Learning）和深度學習（Deep Learning），並重點關注它們與物聯網通信技術的整合。第六章介紹了在智慧城市中實現物聯網部署的要求和挑戰，包括感測基礎設施、人工智慧、計算平台以及如5G網絡等通信技術的支持。為了突顯這些挑戰的實踐，該章還展示了赫爾辛基市物聯網空氣質量監測的城市規模部署的真實案例研究。第七章利用智慧城市中的數位雙胞胎來促進經濟進步並促進有關情境意識的即時決策。第八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，特別是針對高效的決策系統。第九章對智能推理方法進行了全面回顧，特別強調減少推理時間和最小化物聯網設備與雲之間傳輸帶寬。第十章總結了深度學習模型在各種物聯網領域的應用。本章還對這些技術進行了深入研究，以檢視深度學習模型在不同物聯網領域的應用新視野。第十一章探討量子密鑰分發（Quantum Key Distribution, QKD）在物聯網系統中的整合，深入探討將QKD納入物聯網網絡的潛在好處、挑戰和實際考量。第十二章提供了有關智慧醫療背景下量子物聯網當前狀態的全面概述，並介紹其應用、好處、挑戰和未來展望。第十三章提出了一種基於區塊鏈的架構，用於在智能交通系統中保護和管理物聯網數據，提供不可變性、去中心化和增強安全性等優勢。
作者簡介
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 resourcesas well as distributed learning and decision-making in the computing continuum. He has co-authored 2 patents and ca. 50 research articles.
作者簡介(中文翻譯)
普拉維恩·庫馬·東塔博士（IEEE 高級會員及 ACM 專業會員），目前在奧地利維也納的維也納科技大學（TU Wien）分散式系統組擔任博士後研究員。他於2021年在印度理工學院（印度礦業學院）丹巴德獲得博士學位，研究領域為基於機器學習的無線感測器網路演算法。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行動與雲端實驗室擔任訪問博士生，該計畫由愛沙尼亞阿基米德基金會提供的Dora plus獎助金支持。他於2014年和2012年分別在安納塔普爾的JNTUA計算機科學與工程系獲得技術碩士和技術學士學位，並以優異成績畢業。目前，他是Elsevier《計算機通訊》的技術編輯和客座編輯，並擔任Inderscience《數位轉型國際期刊》、Wiley《新興電信技術交易》（ETT）的編輯委員會成員。他還在Elsevier期刊《測量與測量：感測器》中擔任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。他目前的研究包括學習驅動的分散式計算連續系統、邊緣智能和邊緣的因果推斷。
阿比謝克·哈茲拉博士目前在印度安得拉邦奇圖爾的印度信息技術學院斯里城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計算機工程系的通訊與網路實驗室擔任博士後研究員。他在印度理工學院（印度礦業學院）丹巴德完成了博士學位。他在印度國立技術學院曼尼普爾獲得計算機科學與工程的碩士學位，並在印度國立技術學院阿加爾塔拉獲得學士學位。他目前擔任《物理通訊》、《計算機通訊》、《當代數學》、《IET 網路》、《SN 計算機科學》、《測量：感測器》的編輯/客座編輯，並擔任IEEE PICom 2023的會議總主席。他的研究興趣包括物聯網、霧/邊緣計算、機器學習和工業5.0。
勞里·洛文博士（IEEE 高級會員）擁有科技博士學位，是IEEE的高級會員，並擔任芬蘭奧盧大學普遍計算中心（UBICOMP）6G旗艦研究計畫中分散智能戰略研究領域的協調員。他於2021年在奧盧大學獲得科技博士學位，2022年在維也納科技大學的分散式系統組工作，並於2023年訪問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、資源的編排以及計算連續體中的分散學習和決策。他共同擁有2項專利和約50篇研究文章。
