DEEP LEARNING FOR EDGE COMPUTING IN INDIAN SMART CITIES, REAL TIME ANALYTICS ON LOW POWER DEVICES

Abstract

The rapid growth of urbanization in India has led to the emergence of smart cities that rely on advanced technologies for efficient infrastructure management and real-time decision-making. One of the key challenges in smart city environments is processing large volumes of data generated by IoT devices such as sensors, cameras, and smart meters. Traditional cloud-based processing introduces latency, bandwidth limitations, and privacy concerns. To address these challenges, this project proposes a deep learning-based edge computing framework for real-time analytics on low-power devices in Indian smart cities. The proposed system leverages lightweight deep learning models optimized for edge devices such as Raspberry Pi, NVIDIA Jetson Nano, and other embedded systems. These models perform real-time data analysis locally, reducing dependency on centralized cloud servers. Applications include traffic monitoring, air quality analysis, smart surveillance, and energy management. Techniques such as model compression, quantization, and pruning are used to ensure efficient execution on resource-constrained devices. The system processes streaming data in real time, enabling faster decision-making and improved responsiveness. The performance of the proposed framework is evaluated using metrics such as latency, accuracy, power consumption, and throughput. Experimental results demonstrate that edge-based deep learning significantly reduces response time and bandwidth usage while maintaining high accuracy. Additionally, the system enhances data privacy by minimizing data transmission to external servers. This project highlights the importance of integrating deep learning with edge computing to build scalable, efficient, and intelligent smart city solutions. The proposed approach is particularly suitable for Indian smart cities, where costeffective and energy-efficient solutions are essential for sustainable urban development