Optimization of Smart Grid and Urban Traffic Systems through 5G, Machine Learning, and Autonomous Technologies: Addressing Security, Predictive Maintenance, and Resource Management Challenges

Abstract

Optimization of smart grid and urban traffic systems is crucial in the context of increasing urbanization and the growing demand for sustainable, efficient infrastructure. This paper explores the optimization of smart grid and urban traffic systems through advancements in 5G, machine learning, and autonomous technologies. As 5G networks become integral to IoT and industrial applications, the need for secure, efficient, and adaptive systems is more critical than ever. The study delves into secure authentication mechanisms for remote monitoring, predictive maintenance strategies utilizing big data, and autonomous navigation improvements in GPS-denied environments. Key challenges such as data integration, dynamic resource management for Network Function Virtualization (NFV), and the integration of UAVs with V2X communications for enhanced urban traffic monitoring are addressed. The findings highlight the interconnected nature of these technologies and the need for holistic approaches to advance smart city and industrial infrastructures. This review synthesizes recent research contributions and offers insights into the current state and future directions in these fields.