Optimizing Edge Computing for Big Data Processing in Smart Cities

The surge of big data and IoT in smart cities requires effective computational models to process massive amounts of real-time data. Edge computing emerges as an innovative solution by minimizing latency, improving security, and maximizing energy efficiency. This paper investigates the convergence of AI-based edge computing for big data processing through a study of four sophisticated algorithms: Federated Learning, TinyML, Edge-Optimized CNNs, and Adaptive Data Compression. Experimental analysis proved a decrease of 37% in latency, 42% increase in computational performance, and 29% decrease in energy usage than that of common cloud-based computation. In addition, a multilayered data fusion mechanism increased data quality by 21%, facilitating smart city decision-making. The analysis also compares contemporary techniques and expounds on how cloud-edge interaction could be a boon for improving the infrastructure in smart cities. Findings validate that edge computing improves real-time analytics, transportation safety, and sustainable resource management. Yet, security threats and scalability challenges need more investigation. Future research should concentrate on blockchain-based edge security models and energy-aware AI architectures to provide hassle-free smart city deployment. This research concludes that edge computing is the key to the next generation of smart urban infrastructure, encouraging efficiency, sustainability, and intelligent automation.