AI-Driven Edge Computing in Smart City IoT Infrastructures
Abstract
The increasing complexity of urban infrastructures and the rapid growth of Internet of Things (IoT) devices present significant challenges for real-time data processing, resource management, and scalability within smart cities. Traditional cloud computing models face limitations in latency and bandwidth, primarily due to their centralized data processing architecture. AI-driven edge computing emerges as a compelling solution, bringing computation closer to data sources, allowing faster decision-making, and reducing network congestion. This paper delves into integrating AI with edge computing in smart city IoT infrastructures, emphasizing how AI enhances data processing, optimizes resource allocation, and strengthens security at the edge. This paper highlights the transformative role of AI in addressing challenges like latency, bandwidth limitations, and data privacy concerns through a comprehensive review of current research and case studies. The results show that AI-powered edge computing can significantly boost the performance and sustainability of various smart city services, such as traffic management, energy efficiency, and environmental monitoring.
Keywords:
Edge computing, Internet of things, Smart city, AI optimization, Data privacy, Real-time analyticsReferences
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