AI-Augmented Edge Computing for Smart City Infrastructure

AI-Augmented Edge Computing for Smart City Infrastructure

1Desai Smit Btech IT & Atmiya University

2Mr Milan Gohel Btech IT & Atmiya University

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Abstract -AI-augmented edge computing is transforming the foundation of smart city infrastructure by decentralizing computational processes and enabling intelligent, real-time decision-making closer to the data source. Unlike conventional cloud-centric models, this paradigm processes data locally through micro data centers and edge devices, thereby reducing latency, minimizing bandwidth usage, and increasing overall system efficiency. Its practical applications span across adaptive traffic control systems that respond dynamically to congestion, energy-optimized smart grids that ensure efficient resource distribution, and localized surveillance systems that enhance public safety. By integrating AI algorithms into edge environments, cities can leverage distributed sensor networks to detect patterns, automate responses, and optimize urban operations with greater agility. This not only alleviates pressure on central networks but also ensures scalability, reliability, and sustainability in increasingly complex metropolitan settings. While challenges such as limited hardware capabilities, security vulnerabilities, and data synchronization issues remain, they are progressively being addressed through robust architectural frameworks and secure communication protocols. Exemplary implementations in cities like Singapore and Zurich underscore the transformative potential of this technology in fostering secure, sustainable, and citizen-centric smart city ecosystems.

Key Words:Smart cities, urban infrastructure, latency reduction, traffic management, embedded systems, smart grids