Tinyml for Edge Intelligence in Iot and Lpwan Networks

Texte du rabat

TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to IoT and LPWANs. It starts by providing the foundations of IoT/LPWANs, low power embedded systems and hardware, the role of artificial intelligence 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 the use of TinyML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies.

Contenu

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