Edge-Enabled Communication Networks: Architecture, Challenges, and Applications for Low-Latency Systems

Abstract

The growing use of real-time applications such as autonomous vehicles, augmented reality, smart healthcare systems, and industrial automation has created significant challenges for traditional cloud-based communication networks. These applications require very fast data processing, minimal delay, high reliability, and efficient handling of large amounts of data. However, conventional centralized systems often struggle to meet these requirements because data has to travel to distant data centers, which can cause delays and reduce overall performance. To address these limitations, edge-enabled communication networks have emerged as an effective solution. In this approach, computing and storage resources are placed closer to end users, allowing data to be processed locally rather than being sent to centralized cloud servers. This helps in reducing latency, improving response time, and enhancing the overall efficiency of the network. This review paper provides a detailed overview of edge-enabled communication networks by examining their architecture, key technologies, practical applications, and the challenges involved in their implementation. It also explains how edge computing works together with advanced technologies such as 5G and future networks, Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Artificial Intelligence (AI) to improve network performance. In addition, the paper discusses important issues like security, scalability, resource management, and interoperability that need to be addressed. Finally, it outlines future research directions that can help in developing communication systems that are more efficient, secure, and capable of supporting low-latency applications.