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…mehr
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.
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
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
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