Advancements in IoT Sensors and Security - Harnessing AI/ML for Secured IoT Sensor Data

This book uncovers and presents insights into the intersecting realms of IoT sensors and AI/ML technologies, providing a unique opportunity for researchers and developers to contribute to progress in these fields. By exploring methods, applications, and similar aspects, it sheds light on how these technologies will continue to shape and impact our lives.

Inhaltsverzeichnis

Foundations of IoT, Security, and AI/ML Integration.- A Study of Security in IoT Enabled Wireless Sensor Systems: Its Features, Architectures, Communications and Application Perspectives.- Strategic Approaches to Efficient Data Aggregation and Malicious Node Detection in IoT-Enabled Healthcare Systems.- Implications of Generative AI on IoT Security: A Dual Perspective.- INDOOR LOCALIZATION AND HEADING ESTIMATION LEVERAGING MACHINE LEARNING ALGORITHMS WITH WI-FI FINGERPRINTS AND SENSOR DATA.- Using Machine Learning for Mutual Coupling Compensation in Strongly Coupled Arrays.

Zusammenfassung

This book uncovers and presents insights into the intersecting realms of IoT sensors and AI/ML technologies, providing a unique opportunity for researchers and developers to contribute to progress in these fields. By exploring methods, applications, and similar aspects, it sheds light on how these technologies will continue to shape and impact our lives.

Produktdetails

• Mitarbeit: Shilpa Bade-Gite (Herausgeber), Subhas Mukhopadhyay (Herausgeber), Biswajeet Pradhan (Herausgeber)
• Verlag: Springer, Berlin
• Sprache: Englisch
• Produktform: Fester Einband
• Erscheint: 23.11.2025
• EAN: 9783032055064
• ISBN: 978-3-0-3205506-4
• Seiten: 594
• Illustration: VIII, 594 p. 219 illus., 186 illus. in color.
• Serie: Smart Sensors, Measurement and Instrumentation
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Technik > Allgemeines, Lexika; machine learning, Maschinelles Lernen, Deep Learning, Internet of things, Computernetzwerke und maschinelle Kommunikation, Materialwissenschaft, Computer Vision, Natural Language Processing, Data Engineering, Autonomous Systems, Sensors and biosensors, Generative AI models