Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (Tiny ML) enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (Io T) nodes. This book provides a comprehensive guide to designing optimizing securing and applying Tiny ML models in real-world constrained environments. This book offers thorough coverage of key topics including: Foundations and Optimization of Tiny ML: Covers microcontroller-centric power optimization core principles and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints. Applications of Tiny ML in Healthcare and Io T: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges real-time detection systems and integration with low-power Io T and low-power wide-area network (LPWAN) technologies. Security and Privacy in Tiny ML: Addresses the unique challenges of securing Tiny ML deployments including privacy-preserving techniques blockchain integration for secure Io T applications and methods for protecting resource-constrained devices. Emerging Trends and Future Directions: Explores the evolving landscape of Tiny ML research highlighting new applications adaptive frameworks and promising avenues for future investigation. Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating Tiny ML in action across diverse scenarios providing guidance for engineers researchers and students. This book is an essential resource for embedded system designers AI practitioners cybersecurity professionals and academics who want to harness the power of Tiny ML for smarter more efficient and secure edge intelligence solutions.
