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Full Description
Adaptive AI in wireless sensor networks is crucial for ensuring user understanding and confidence in AI outputs across fields such as healthcare, environmental monitoring, smart cities, and industrial automation. It enables compliance with regulations specific to these domains and encourages the design of user-centric AI systems that align with human values and operational requirements. Adaptive AI in Sensor Informatics: Methods, Applications, and Implications delves into the need for efficiency, interpretability, and reliability in Adaptive AI systems that are designed specifically for wireless sensor networks and related domains. It sheds light on how Adaptive AI can provide decisions made by AI models, facilitating effective collaboration between humans and AI within the context of wireless sensor networks. The book serves as a comprehensive guide for academics, professionals, and students interested in the intersection of adaptive AI and wireless sensor networks. It examines the challenges and opportunities inherent in deploying Adaptive AI in these contexts and offers practical insights into methods, approaches, and best practices for developing and deploying AI models that are both understandable and reliable within wireless sensor networks.
Contents
1. AI in Sensor Technology
2. AI for Sensor Data Analysis
3. Role of AI in Wireless Sensor Network (WSN)
4. AI driven-Smart data processing in WSN
5. Energy Efficient communication with adaptive AI models
6. Adaptive AI techniques for the deployment of WSN
7. AI in Smart Sensing and Things
8. AI in Sensor Cloud
9. AI powered Edge computing for IoT
10. Adaptive AI in techniques for Industrial Internet of Things
11. AI in Urban Infrastructure Monitoring
12. Real-World Applications
13. Future Directions and Emerging Trends of Adaptive AI in WSN, IoT, IIoT
