Sustainable AI Techniques for Real-Time Risk Monitoring
Series: The Springer Series in Applied Machine Learning;
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 5 September 2026
- ISBN 9783032286710
- Binding Hardback
- No. of pages370 pages
- Size 235x155 mm
- Language English
- Illustrations II, 370 p. 70 illus., 34 illus. in color. 700
Categories
Long description:
Sustainable AI Techniques for Real-Time Risk Monitoring offers a comprehensive examination of energy-efficient artificial intelligence approaches for hazard detection in smart environments. The book begins by identifying the limitations of traditional AI models —particularly their high computational and energy demands — and introduces the concept of Green AI as a sustainable alternative. It systematically presents key methodologies, including lightweight deep learning architectures, model optimization techniques, and the integration of edge and fog computing. In addition, it explores advanced paradigms such as federated learning and bio-inspired computing to enable scalable and resource-efficient real-time monitoring systems.
The book further elaborates on practical applications across diverse domains, including fire hazard detection, industrial safety, environmental monitoring, and smart healthcare systems. It also examines how secure and decentralized technologies—such as blockchain—enhance the reliability of IoT-based hazard detection frameworks. The concluding section outlines future research directions, emphasizing renewable-powered IoT infrastructures and the ethical, legal, and societal implications of Green AI.
Overall, this book serves as a valuable resource for academics, researchers, and practitioners striving to develop sustainable, reliable, and energy-conscious intelligent safety systems.
MoreTable of Contents:
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.- Introduction to Green AI in Hazard Detection.
.- Energy Challenges of Traditional AI in Hazard Monitoring.
.- Sustainable Smart Environments: A Systems Perspective.
.- Lightweight Deep Learning Models for Hazard Detection.
.- Model Optimization Techniques for Green AI.
.- Edge AI and Fog Computing for Energy-Efficient Safety Systems.
.- Federated Learning for Sustainable Hazard Detection in IoT Ecosystems.
.- Neuromorphic and Bio-Inspired Computing for Hazard Monitoring.
.- Green AI for Fire Hazard Early Warning Systems.
.- Energy-Efficient AI in Industrial Hazard Monitoring.
.- Environmental Hazard Detection using Green AI.
.- Green AI for Smart Healthcare Safety Systems.
.- Blockchain and Green AI for Secure Hazard Detection in IoT.
.- Renewable-Powered IoT and Hazard Detection Frameworks.
