Optimizing Deep Learning Models for Real-Time Edge Computing in Smart Cities

Abstract

The rapid development of smart cities relies heavily on real-time data processing to enhance urban management systems, from traffic control to public safety. The vast amounts of data generated by IoT devices and sensors present significant challenges, including high latency, bandwidth limitations, and energy consumption. Edge computing addresses these issues by bringing computational resources closer to the data source, enabling faster decision-making. Deploying deep learning models on edge devices, which are often resource-constrained, requires careful optimization. This paper explores key techniques for optimizing deep learning models for real-time edge computing in smart cities, including model compression, quantization, pruning, and the use of specialized hardware such as GPUs and TPUs. We also discuss the integration of edge AI with cloud computing to balance the computational load, ensuring efficient operation in smart city environments. By addressing challenges related to data privacy, device heterogeneity, and energy efficiency, the paper provides a comprehensive overview of current advancements and future directions in this field. Optimized deep learning models are critical to realizing the potential of smart cities, enabling more responsive, efficient, and sustainable urban systems.