Edge Intelligence: AI-Driven Decision Making at the Network Edge for Enhanced Data Reliability

Abstract

In the rapidly evolving landscape of edge computing, ensuring data reliability at the network edge is crucial for maintaining the performance and integrity of distributed systems. This paper presents a comprehensive study on leveraging artificial intelligence (AI) to enhance decision-making processes at the network edge, focusing on improving data reliability. We propose an innovative framework that integrates AI-driven analytics with edge computing technologies to enable realtime data processing and decision-making. By deploying machine learning models directly at the edge, our approach aims to optimize data reliability through proactive fault detection, anomaly detection, and adaptive resource management. Our methodology involves a combination of supervised and unsupervised learning techniques to address various aspects of data reliability. We use real-world datasets from edge network environments to train and evaluate AI models, examining their performance in terms of accuracy, latency, and resource efficiency. Key contributions include the development of novel algorithms for real-time decision-making and the demonstration of their effectiveness in practical scenarios. The results indicate that AI-driven decision-making at the edge significantly enhances data reliability by reducing latency and improving fault detection accuracy. The proposed framework demonstrates superior performance compared to traditional approaches, offering valuable insights into the practical application of AI in edge computing environments.