Edge Computing Integration in 5G Core on Real-Time Data Processing for Smart Applications

Intelligent decision-making for clever apps is made possible by the 5G core's integration of edge computing, which improves real-time data processing capabilities. It greatly lowers latency and boosts efficiency in vital applications like industrial automation, smart cities, and driverless cars by moving processing closer to data sources. However, there are issues with current approaches such as excessive network congestion, longer processing times, and wasteful resource use. These restrictions impair real-time responsiveness and lower smart apps' overall performance. We suggest the Edge-Integrated 5G Smart Processing Framework (E5G-SPF) as a solution to these issues. To maximize real-time data processing, this system uses cutting-edge methods including network slicing, dynamic resource allocation, and edge-based AI inference. The 5G core's multi-access edge computing (MEC) nodes are used to distribute workloads effectively and reduce latency. By enabling ultra-fast data analytics, lowering communication overhead, and enhancing service reliability, the E5G-SPF architecture is intended to improve a variety of smart applications. E5G-SPF employs Deep Learning (e.g., CNNs, LSTMs) for real-time data inference at the edge. Reinforcement Learning (RL) is used for dynamic task scheduling and resource optimization. Federated Learning ensures privacy-preserving model updates across distributed edge nodes. Graph Neural Networks (GNNs) support topology-aware task allocation. Additionally, metaheuristic algorithms combined with ML are used for efficient, adaptive scheduling decisions. The proposed E5G-SPF framework achieves a latency reduction of up to 85%, lowering it from 60 to 9 ms, and improves processing speed by 72% compared to conventional cloud models. These enhancements enable real-time responsiveness for critical smart applications. Experimental results demonstrate that the E5G-SPF framework significantly improves processing speed, reduces end-to-end latency, and enhances resource efficiency compared to traditional cloud-based approaches. These findings confirm its potential in transforming next-generation smart applications by ensuring real-time data processing within the 5G core.