AI-Augmented Data Reliability Engineering in Edge Computing Networks for Mission-Critical Applications

Abstract

In mission-critical applications, ensuring data reliability within edge computing networks is paramount for maintaining system performance and operational integrity. This paper presents an innovative approach to augmenting data reliability engineering through the integration of artificial intelligence (AI) techniques. By leveraging AI-driven models, this study addresses the challenges associated with data integrity, fault tolerance, and real-time processing in edge computing environments. We propose a comprehensive framework that combines predictive analytics, anomaly detection, and automated error correction to enhance data reliability in edge networks. The proposed framework employs machine learning algorithms to predict potential data failures and anomalies before they impact system performance. Real-time monitoring and adaptive error correction mechanisms are integrated to ensure continuous data integrity and minimize downtime. Through extensive experiments and simulations, we demonstrate the effectiveness of the AI-augmented approach in improving data reliability and system robustness. The results indicate significant improvements in fault detection accuracy, data consistency, and system resilience compared to traditional methods. This paper contributes to the field of data reliability engineering by presenting a novel AI-enhanced solution tailored for edge computing networks in mission-critical applications. The findings provide actionable insights for deploying AI-driven data reliability strategies, offering a pathway for enhancing operational reliability and efficiency in edge computing environments.