Edge Computing with AI-Driven Decision Frameworks: Leveraging Artificial Intelligence for Real-Time Analytics, Scalability, and Autonomous Decision-Making in Distributed Systems

Abstract

This research explores the integration of AI-driven decision frameworks into edge computing to address limitations of traditional edge systems, such as lack of advanced decision-making capabilities, data silos, and scalability issues. Edge computing, which processes data near its source, is essential for real-time applications like autonomous vehicles and industrial automation. AI-driven frameworks, leveraging machine learning and deep learning techniques, offer enhanced decision-making through real-time analytics, predictive maintenance, and anomaly detection. The study aims to identify effective AI methodologies for edge computing and evaluate their impact on performance, efficiency, scalability, and security. By examining AI techniques such as federated learning, edge AI chips, and real-time analytics, the research highlights both the opportunities and challenges of integrating AI in edge environments. Ultimately, this integration promises to revolutionize industries by enabling efficient, real-time data processing and improving overall system responsiveness and decision-making accuracy.