Learning Techniques for the Internet of Things (Record no. 87503)
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| 000 -LEADER | |
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| fixed length control field | 05815nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-50514-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730171313.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240219s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031505140 |
| -- | 978-3-031-50514-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 004.6782 |
| 245 10 - TITLE STATEMENT | |
| Title | Learning Techniques for the Internet of Things |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XXII, 322 p. 72 illus., 67 illus. in color. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Chapter. 1. Edge Computing for IoT -- Chapter. 2. Federated Learning Systems: Mathematical modelling and Internet of Things -- Chapter. 3. Federated Learning for Internet of Things -- Chapter. 4. Machine Learning Techniques for Industrial Internet of Things -- Chapter. 5. Exploring IoT Communication Technologies and Data-Driven Solutions -- Chapter. 6. Towards Large-Scale IoT Deployments in Smart Cities: Requirements and Challenges -- Chapter. 7. Digital Twin and IoT for Smart City Monitoring -- Chapter. 8. Multiobjective and Constrained Reinforcement Learning for IoT -- Chapter. 9. Intelligence Inference on IoT Devices -- Chapter. 10. Applications of Deep Learning models in diverse streams of IoT -- Chapter. 11. Quantum Key Distribution in Internet of Things -- Chapter. 12. Quantum Internet of Things for Smart Healthcare -- Chapter. 13. Enhancing Security in Intelligent Transport Systems: A Blockchain-Based Approach for IoT Data Management -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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 10summarizes 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Donta, Praveen Kumar. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Hazra, Abhishek. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Lovén, Lauri. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-50514-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer Nature Switzerland : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
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| -- | computer |
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| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Cloud Computing. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Internet of things. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Cloud Computing. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Internet of Things. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
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