Product details:
| ISBN13: | 9780443222023 |
| ISBN10: | 0443222029 |
| Binding: | Paperback |
| No. of pages: | 385 pages |
| Language: | English |
| 700 |
Category:
-
Publisher: Academic Press
Date of Publication: 1 June 2024
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Long description:
TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to the Internet of Things (IoT) and low-power wide area networks (LPWANs). It starts by providing the foundations of IoT/LPWANs, low-power embedded systems and hardware, the role of AI and machine learning in communication networks in general, and cloud/edge intelligence. It then presents the concepts, methods, algorithms, and tools of TinyML. Practical applications of TinyML are given from the healthcare and industrial sectors, providing practical guidance on the design of applications and the selection of appropriate technologies.
- This book provides one-stop solutions for emerging TinyML for IoT and LPWAN applications.
- The principles and methods of TinyML are explained, with a focus on how it can be used for IoT, LPWANs, and 5G applications.
- Applications from the healthcare and industrial sectors are presented.
- Guidance on the design of applications and the selection of appropriate technologies is provided.
Table of Contents:
1. TinyML for Ultra Low Power Internet of Things
2. Embedded Systems for Ultra Low Power Applications
3. Cloud and Edge Intelligence
4. TinyML: Principles and Algorithms
5. TinyML using Neural Networks for Resource Constraint Devices
6. Reinforcement Learning for LoRaWANs
7. Software Frameworks for TinyML
8. Extensive Energy Modeling for LoRaWANs
9. TinyML for 5G Networks
10. Non-Static TinyML for Ad hoc Networked Devices
11. Bayesian-Driven Optimizations of TinyML for Efficient Edge Intelligence in LPWAN Networks
12. 6TiSCH Adaptive Scheduling for Industrial Internet of Things
13. Securing TinyML in a Connected World
14. TinyML Applications and Use Cases for Healthcare
15. Machine Learning Techniques for Indoor Localization on Edge Devices
16. Embedded Intelligence in Internet of Things Scenarios: TinyML Meets eBPF
17. A Real-Time Price Recognition System using Lightweight Deep Neural Networks on Mobile Devices
18. TinyML Network Applications for Smart Cities
19. Emerging Application Use Cases and Future Directions
2. Embedded Systems for Ultra Low Power Applications
3. Cloud and Edge Intelligence
4. TinyML: Principles and Algorithms
5. TinyML using Neural Networks for Resource Constraint Devices
6. Reinforcement Learning for LoRaWANs
7. Software Frameworks for TinyML
8. Extensive Energy Modeling for LoRaWANs
9. TinyML for 5G Networks
10. Non-Static TinyML for Ad hoc Networked Devices
11. Bayesian-Driven Optimizations of TinyML for Efficient Edge Intelligence in LPWAN Networks
12. 6TiSCH Adaptive Scheduling for Industrial Internet of Things
13. Securing TinyML in a Connected World
14. TinyML Applications and Use Cases for Healthcare
15. Machine Learning Techniques for Indoor Localization on Edge Devices
16. Embedded Intelligence in Internet of Things Scenarios: TinyML Meets eBPF
17. A Real-Time Price Recognition System using Lightweight Deep Neural Networks on Mobile Devices
18. TinyML Network Applications for Smart Cities
19. Emerging Application Use Cases and Future Directions