TinyML for Edge Intelligence in IoT and LPWAN Networks 1st Edition

Grab the next wave of embedded intelligence with TinyML for Edge Intelligence in IoT and LPWAN Networks (1st Edition) by Bharat S Chaudhari, Sheetal N Ghorpade, Marco Zennaro, and Rytis Paškauskas. This concise, practical guide brings together machine learning, low-power wide-area networks, and real-world IoT constraints so you can build smarter, leaner edge systems.

Explore clear explanations of TinyML fundamentals, model quantization, and on-device inferencing alongside LPWAN technologies such as LoRaWAN and NB‑IoT. Rich with step-by-step deployment strategies, energy-aware design patterns, and case studies spanning smart cities, precision agriculture, and industrial monitoring, the book translates theory into actionable solutions for constrained devices and sensor networks.

Engineers, data scientists, product managers, and researchers will value the balanced mix of algorithmic insight and hardware-focused guidance. Whether designing latency-sensitive anomaly detection for manufacturing floors or long-range environmental sensing in rural areas, you’ll find pragmatic advice on model selection, compression, connectivity trade-offs, and security for edge inference.

Optimized for practitioners around the world — from IoT ecosystems in India and Europe to North American smart-grid projects — this volume is a go-to reference for anyone deploying ML at the edge over LPWANs. Precise, readable, and future-focused, it helps you shorten development cycles and scale low-power intelligence across diverse geographies.

Add TinyML for Edge Intelligence in IoT and LPWAN Networks to your professional library and start turning constrained sensors into intelligent, field-ready systems today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.