AI Applications in Real-Time Edge Processing: Leveraging Artificial Intelligence for Enhanced Efficiency, Low-Latency Decision Making, and Scalability in Distributed Systems

Abstract

This study explores the impact of innovative AI applications in real-time edge processing, a paradigm that processes data near its source rather than relying on centralized cloud-based models. Edge processing is crucial for applications where low latency, immediate feedback, and action are necessary, such as autonomous vehicles, healthcare monitoring, industrial automation, and smart cities. The research aims to assess the performance, identify challenges, explore optimization strategies, and address security and privacy concerns associated with edge processing. By examining the architecture of edge devices, real-time processing requirements, and comparing edge computing with cloud-based solutions, the study highlights the advantages of reduced latency, enhanced privacy, and bandwidth efficiency, while acknowledging limitations like resource constraints and management complexity. The paper also delves into the application of machine learning and deep learning models, such as decision trees, support vector machines, convolutional neural networks, and recurrent neural networks, tailored for edge devices. Optimization techniques like pruning and quantization are discussed to make these AI algorithms feasible for edge deployment. Through this comprehensive analysis, the study provides insights into the potential and challenges of integrating AI with edge processing to enhance real-time decision-making capabilities across various domains.