IoT-enabled Convolutional Neural Networks: Techniques and Applications 1st Edition

IoT-enabled Convolutional Neural Networks: Techniques and Applications (1st Edition) by is an essential, practical guide for engineers, researchers, and data scientists seeking to bridge deep learning with real-world Internet of Things deployments. This book captures the latest advances in designing, optimizing, and deploying Convolutional Neural Networks (CNNs) on resource-constrained edge devices—making it indispensable for anyone working in smart cities, healthcare, automotive, agriculture, or industrial automation.

Start reading and you’ll quickly grasp core concepts like model compression, pruning, quantization, transfer learning, and federated learning, explained alongside IoT-specific strategies such as sensor fusion, energy-aware inference, and edge-cloud partitioning. Clear diagrams and real-world case studies show how to adapt CNN architectures for low-power microcontrollers, gateways, and embedded GPUs without sacrificing accuracy.

What makes this edition particularly valuable is its focus on practical implementation: step-by-step workflows for dataset preparation, on-device training and fine-tuning, latency and throughput optimization, and security considerations for distributed systems. Whether you’re prototyping a smart-camera for traffic analytics or building an IoT-based diagnostic tool in healthcare, the book offers actionable techniques to accelerate development and improve reliability across geographies and industries.

If you’re aiming to deliver scalable, efficient AI at the edge, this volume equips you with tools and best practices used worldwide. Compact yet comprehensive, it’s suited for graduate students, software and hardware engineers, and product managers who need to translate CNN research into deployable IoT solutions.

Order your copy today to start transforming sensor data into intelligent, deployable services—bringing dependable AI to the edge of your network.

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