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. Weniger zeigen

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Technische Daten

Erscheinungsdatum

20.02.2024

Sprache

Englisch

EAN

9783031505133

Herausgeber

Springer International Publishing

Sonderedition

Nein

Seitenanzahl

322

Einbandart

Gebundene Ausgabe

Autorenporträt

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.

Schlagwörter

IoT Applications, Learning techniques, AI for Healthcare, AI for IoT, AI for Industry, AI for Transportation, Layers of IoT

Thema-Inhalt

UTC - Cloud Computing UT - Computernetzwerke und maschinelle Kommunikation UYQ - Künstliche Intelligenz

Höhe

235 mm

Breite

15.5 cm

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Hersteller: Springer, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com, Springer Nature Customer Service Center GmbH

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Learning Techniques for the Internet of Things

(Gebundene Ausgabe, Englisch)

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