BOOSTING 5G NETWORK EDGE COMPUTING TO REDUCE LATENCY AND IMPROVE RESPONSE TIMES

The rapid deployment of 5G networks promises ultra-low latency and high-speed communication, yet meeting the stringent requirements of latency-sensitive applications remains a challenge. Traditional cloud computing architectures struggle to deliver real-time processing due to the physical distance between data centers and end-users. This paper addresses the problem of reducing latency in 5G networks by enhancing edge computing capabilities. Our contribution lies in developing an optimized edge computing framework that dynamically allocates resources and offloads tasks to the network edge, thereby reducing response times. While previous works focus primarily on cloud-based solutions or static edge configurations, this paper introduces a hybrid approach that combines adaptive multi-tier edge orchestration with blockchain coordination (AMTEO-BC) for enhanced security and reliability by integrating edge computing with machine learning models to predict and adapt to real-time network condition cases to achieves faster data processing, reduced network congestion, and improved overall system performance. By leveraging advanced techniques such as distributed caching, task offloading, and intelligent resource allocation, we aim to bring computation and storage closer to the network edge, thereby minimizing the distance data travel. Simulation results demonstrate a significant reduction in latency compared to existing methods, offering a practical solution for time-critical 5G applications such as autonomous vehicles and real-time analytics. The Hierarchical Distributed Edge Computing Model (HDEC) could be a game-changer in reducing latency in 5G edge computing, especially for applications that require near-instantaneous response times