TinyML for Edge Intelligence in IoT and LPWAN Networks

Auteur

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, Artificial Intelligence, Tiny ML, etc. She is instrumental in bridging the institute-industry gap and is passionate about democratizing data science. She is on the advisory board of various engineering and management institutes.

Dr. Marco Zennaro is a Research Scientist at the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy, where he coordinates the Science, Technology, and Innovation Unit. He received his PhD from KTH-Royal Institute of Technology, Stockholm, and his MSc in Electronic Engineering from the University of Trieste. He is a visiting Professor at KIC-Kobe Institute of Computing, Japan, and a Senior Member of IEEE and ACM. His research interest is in ICT4D, the use of ICT for Development, and in particular, he investigates the use of IoT in Developing Countries. He is TinyML4D Chair and Academic Network Co-Chair in the framework of the TinyML4D initiative. He has given lectures on wireless technologies in more than 30 different countries. More info here: https://www.ictp.it/member/marco-zennaro#biography

Dr. Rytis Pakauskas is a Postdoctoral Fellow at the Abdus Salam International Centre for Theoretical Physics in Trieste, Italy, where he works on embedded machine-learning applications. He received his PhD in Physics from Georgia Institute of Technology and has worked at ELETTRA Sincrotrone Trieste, CNR Pisa, CNRS Lyon, and the National Institute for Theoretical Physics (NiTheP) in Stellenbosch, South Africa.

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