A Review of Edge Computing for Real-Time Engineering Applications

Abstract

Edge computing has emerged as a transformative and rapidly evolving paradigm in modern distributed computing systems, fundamentally reshaping how data is processed, analyzed, and utilized in latency-sensitive environments. With the exponential growth of data generated by Internet of Things (IoT) devices, sensors, cyber-physical systems, and intelligent embedded platforms, traditional cloud-centric architectures are increasingly challenged in meeting the strict requirements of real-time processing, low latency, and high reliability. In response to these limitations, edge computing shifts computational capabilities closer to the data source, enabling localized processing at edge devices such as smart sensors, industrial controllers, gateways, and micro data centers. Unlike conventional centralized cloud computing models, edge computing significantly reduces communication latency by minimizing the distance data must travel for processing. This proximity-based processing approach not only enhances response times but also reduces bandwidth consumption by filtering, aggregating, and analyzing data locally before transmitting only essential information to the cloud. As a result, edge computing improves network efficiency, reduces operational costs, and ensures more resilient system performance, particularly in environments where continuous connectivity cannot be guaranteed. This review provides a comprehensive examination of edge computing in the context of real-time engineering applications, where instantaneous decision-making is critical. Key application domains discussed include industrial automation systems, where edge-enabled predictive maintenance and real-time control improve productivity and reduce downtime; autonomous systems such as self-driving vehicles and drones, which rely on edge intelligence for split-second environmental perception and decision-making; smart grid systems, where real-time monitoring and distributed energy management enhance power efficiency and stability; healthcare monitoring systems, where wearable and implantable devices utilize edge analytics for continuous patient observation and emergency detection; and intelligent transportation systems, where edge-based traffic analysis optimizes flow and reduces congestion. Finally, the review outlines future research directions aimed at overcoming these challenges and advancing the capabilities of edge computing. These include the development of intelligent resource management algorithms, enhanced cybersecurity frameworks, energy-efficient edge architectures, and deeper integration with artificial intelligence, blockchain, and digital twin technologies. Collectively, these advancements are expected to drive the next generation of intelligent, autonomous, and highly responsive engineering systems capable of operating reliably in dynamic and complex environments.