Human-AI Enabled Edge Computing for Data Processing: A Comprehensive Analysis

Abstract

The exponential growth in data generation and processing requirements has driven the need for more efficient and intelligent computational approaches at the edge of networks. This comprehensive article investigates the integration of human expertise with AI-enabled edge computing systems, focusing on optimization strategies, implementation frameworks, and real-world applications. Through extensive analysis of implementation cases and performance metrics, the research demonstrates significant improvements in processing efficiency, with systems achieving a reduction in latency and improvement in decision accuracy through human-AI collaboration. The article presents a detailed framework for implementing these hybrid systems, addressing critical aspects including technical architecture requirements, integration guidelines, and risk mitigation strategies. Results indicate substantial benefits in operational efficiency, resource utilization, and decision-making capabilities across various industrial applications. While identifying implementation challenges and limitations, the article provides strategic recommendations for successful deployment and highlights opportunities for future advancement in the field. The article contributes significantly to understanding how organizations can effectively leverage human-AI collaboration in edge computing environments to address the growing demands of data processing in modern digital infrastructure.