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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 vari...

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Detalhes bibliográficos
Autor Corporativo: SpringerLink (Online service)
Outros Autores: Donta, Praveen Kumar (Editor), Hazra, Abhishek (Editor), Lovén, Lauri (Editor)
Formato: Recurso Electrónico livro electrónico
Idioma:inglês
Publicado em: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Edição:1st ed. 2024.
Assuntos:
Cloud Computing.
Internet of things.
Artificial intelligence.
Internet of Things.
Artificial Intelligence.
Acesso em linha:https://doi.org/10.1007/978-3-031-50514-0
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Sumário:
  • 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.

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