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Adaptive AI Models for Real-Time Data Mobility and Security in Urban IoT Networks
Abstract
Abstract Intelligent and contextual decisions at the network edge are opening up exciting new possibilities in real-time data movement as well as urban IoT network safe. Diverse machine learning techniques are deployed in these models which are constantly evolving. They enable the live processing and analysis of data streams from a wide variety of IoT sensors located in an urban environment: this also facilitates adaptation to the rapidly changing conditions surrounding us as well as adapting itself quickly to widely different application needs. The collaborative deployment of large AI models (LAMs) throughout edge devices allows the system to learn across multiple modalities and scenarios. In this way, it increases scalability for smart urban services. Thus, adaptive AI frameworks not only fuse in stringent security measures such as anomaly detection and federated learning but also satisfy the requirements for real-time threat identification as well as data privacy protection under resource-constrained conditions in dynamic networks.
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