Adaptive AI in Sensor Informatics

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.

• Draws on the latest research and methods to provide valuable insight into the efficiency of AI-based systems, particularly within the realm of wireless sensors and related domains
• Explores and explains the critical role played by adaptive AI and sensor informatics in healthcare, finance, and autonomous vehicles, where the synergy of AI and sensor data plays a pivotal role
• Presents relevant case studies, practical demonstrations, and empirical evidence to substantiate the efficacy of AI-enabled sensor systems