Description
Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated embedded IoT and low power wide area networks (LPWAN) nodes with very low on-board memory and computational capabilities are getting connected to the Internet each year, there is a critical need to have a special computational framework like TinyML.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.TinyML for Edge Intelligence in IoT and LPWAN Networks is highly suitable for academic researchers and professional system engineers, architects, designers, testers, deployment engineers seeking to design ultra-lower power and time-critical applications. It would also help in designing the networks for emerging and future applications for resource-constrained nodes.- 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 Things2. Embedded Systems for Ultra Low Power Applications3. Cloud and Edge Intelligence4. TinyML: Principles and Algorithms5. TinyML using Neural Networks for Resource Constraint Devices6. Reinforcement Learning for LoRaWANs7. Software Frameworks for TinyML8. Extensive Energy Modeling for LoRaWANs9. TinyML for 5G Networks10. Non-Static TinyML for Ad hoc Networked Devices11. Bayesian-Driven Optimizations of TinyML for Efficient Edge Intelligence in LPWAN Networks12. 6TiSCH Adaptive Scheduling for Industrial Internet of Things13. Securing TinyML in a Connected World14. TinyML Applications and Use Cases for Healthcare15. Machine Learning Techniques for Indoor Localization on Edge Devices16. Embedded Intelligence in Internet of Things Scenarios: TinyML Meets eBPF17. A Real-Time Price Recognition System using Lightweight Deep Neural Networks on Mobile Devices18. TinyML Network Applications for Smart Cities19. Emerging Application Use Cases and Future Directions
