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Url canonique : www.lavoisier.fr/livre/informatique/tinyml-for-edge-intelligence-in-iot-and-lpwan-networks/descriptif_5063827
Url courte ou permalien : www.lavoisier.fr/livre/notice.asp?ouvrage=5063827

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Langue : Anglais

Coordonnateurs : S Chaudhari Bharat, N Ghorpade Sheetal, Zennaro Marco, Paškauskas Rytis

Couverture de l’ouvrage -

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

Dr. Bharat S. Chaudhari is working as a Professor (HAG) in Electrical/Electronics and Communication Engineering at MIT World Peace University, Pune, India. He graduated in Industrial Electronics Engineering from Amravati University in 1989 and received M. E. Telecom. E. and PhD (Engineering) from Jadavpur University, Kolkata, India in 1993 and 2000, respectively. He has previously held positions like Principal, Dean, and Professor at various Institutes in Pune. He is a recipient of the 2020 IETE N V Gadadhar Memorial Award, the MAEER Pune’s Ideal Teacher Award 2015, and the Young Scientist Research Grant from the Department of Science and Technology, Government of India, in 2003. He was appointed as a Visiting Scientist (Simons Associate/Regular Associate) to Wireless Network Research Group, ICTP, Trieste, Italy from January 2007 to December 2020. He is a Senior Member of IEEE, Fellow of IETE, Fellow of IE (I), and a Founder Chairman of the IEEE Pune Section (2010 – 2011). He also chaired the IEEE Communications Society, Pune Chapter. He serves as a Program Evaluator of the Engineering Accreditation Commission (EAC) of ABET, United States, for accreditation of computer, communications, and similar engineering programs. His current research interest includes LPWANs, AIoT, TinyML, and Si-Photonics.


Dr. Sheetal Ghorpade is working as Director (Data Sciences) at Rubiscape Pvt. Limited, Pune, Maharashtra, India, where she is actively involved in the research and development of data science products. She received her PhD in Applied Mathematics and MSc in Mathematics from the University of Pune, India. She is a Regular Associate (Visiting Scientist) at the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy. Her career represents a great mix of data analytics and high-quality internationally collaborative research on algorithms and optimization for Industry problems. Her research interests are Optimization Techniques, Arti

  • 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.

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