Deep Learning for Edge Computing in Indian Smart Cities: Real Time Analytics on Low Power Devices

Abstract

With the rapid urbanization of Indian cities, the demand for efficient smart city solutions has increased significantly. Edge computing, combined with deep learning, offers a promising approach to processing vast amounts of data generated by smart city infrastructure in real time, while overcoming challenges related to latency, bandwidth, and privacy. This paper presents a comprehensive framework for deploying deep learning models on low power edge devices tailored for Indian smart city environments. We explore optimized neural network architectures and model compression techniques that enable realtime analytics on resource-constrained hardware such as IoT sensors and edge gateways. Our experimental results demonstrate effective performance in various smart city applications, including traffic monitoring, pollution detection, and public safety, with minimal energy consumption and latency. This study highlights the potential of integrating deep learning with edge computing to create scalable, responsive, and energy-efficient smart city systems in India.