Edge and Cloud Computing in Engineering: Trends, Challenges, and Future Directions

Abstract

The rapid evolution of edge and cloud computing has revolutionized the landscape of modern engineering systems by enabling unprecedented computational efficiency, data accessibility, and real-time decision-making. In engineering applications—ranging from smart manufacturing to autonomous systems—these technologies provide scalable infrastructures capable of managing vast streams of sensor and operational data. This paper presents a comprehensive exploration of current trends, challenges, and future directions in the integration of edge and cloud computing within engineering domains. Through a systematic literature analysis, the study identifies key developments such as edge intelligence, hybrid cloud-edge architectures, and AI-driven optimization models, all of which are reshaping computational workflows. Moreover, it examines the technical barriers that hinder large-scale adoption, including latency constraints, data privacy concerns, interoperability issues, and energy efficiency limitations. The paper also highlights emerging paradigms such as fog computing, serverless edge orchestration, and quantum-enhanced cloud systems, which promise to transform engineering design, monitoring, and control systems. Ultimately, this study argues that the convergence of edge and cloud computing represents a critical enabler for next-generation engineering innovations—bridging the gap between high-performance computing and real-time, data-driven intelligence.