Synergistic Integration of 5G-Enabled Cloud Native Infrastructures With Advanced Technologies for Urban Safety Enhancement

Abstract

Urban safety remains an important concern in modern cities, which makes it necessary for advanced approaches to address the progressing challenge such as fire risk assessment and security enhancement. This study explores the important integration of 5G-enabled cloud-native infrastructures with the Internet of Things (IoT), and Artificial Intelligence (AI) algorithm for empowering urban safety. Kubernetes is used as an essential component of the Cloud Native Computing Foundation (CNCF) ecosystem, for managing scalable and resilient infrastructures. This integration of 5G-enabled systems to cloud-native infrastructures proposes an efficient solution that offers high-speed connectivity, low latency, and the scalability required for real-time data processing and communication. In the proposed work, IoT devices are strategically deployed across urban areas to achieve comprehensive coverage and facilitate real-time data collection and monitoring, ideally integrated with Kubernetes-managed clusters to ensure data transmission and processing. Machine Learning (ML) and Deep Learning (DL) based model is trained on the collected data from the field, using Kubernetes for scalable and automated deployment providing real-time data analysis, accurate prediction, and decision-making. The proposed 5G integrated framework provides proactive risk management and enhanced emergency response strategies. The integration of 5G-enabled systems led to a significant reduction in latency, typically to 20 milliseconds, enhancing real-time performance and decision-making processes. This study introduces a framework designed to reduce latency to 20 ms and improve data transmission rates by 25%. Real-world applications, including fire detection and traffic management, demonstrated the system’s effectiveness, achieving sensitivity and recall rates of 97% and 96%, respectively. These results underscore the potential of the proposed framework to enhance urban safety while addressing scalability and integration challenges for future implementations.