Harnessing Edge AI for Real-Time Decision Making: A New Frontier in Smart Infrastructure

The evolution of smart infrastructure demands unprecedented speed and intelligence in decision-making processes. This research comprehensively explores the integration of Edge Artificial Intelligence (Edge AI) to enable real-time analytics and autonomous responses within critical infrastructure systems. By processing data locally on edge devices, Edge AI drastically reduces latency, enhances privacy, and alleviates bandwidth constraints inherent in cloud-centric approaches. We present a novel framework for Edge AI deployment, validated through case studies in smart transportation networks and energy grids, demonstrating 92.3% latency reduction and 40% bandwidth optimization compared to traditional cloud models. Our methodology combines lightweight deep neural networks optimized for edge hardware with federated learning techniques for distributed model training. Results confirm significant improvements in incident response times, predictive maintenance accuracy, and operational efficiency. Challenges including hardware heterogeneity, security vulnerabilities, and model compression trade-offs are critically analyzed. This work establishes Edge AI as an indispensable paradigm for next-generation infrastructure, providing actionable insights for researchers and practitioners to advance resilient, responsive urban ecosystems.