Optimized task offloading with energy efficient communication and optimal offloading network: a mobility and energy-efficient approach for augmented reality in mobile edge computing

Abstract

Mobile edge computing enables the efficient execution of compute-intensive tasks by offloading them to edge servers. However, frequent user mobility in 5 G urban networks leads to increased latency, energy consumption, and resource wastage due to continuous handovers. To address these challenges, Energy Efficient Communication and Optimal Offloading Network, a framework is proposed that combines user mobility prediction and hybrid optimization for task offloading. Energy Efficient Communication and Optimal Offloading Network utilizes a modified Long Short-Term Memory model to predict user movement with high accuracy, achieving an accuracy improvement from 65 % to 95 % over ten iterations. Additionally, a Hybrid Grey Wolf Optimization Algorithm optimizes task allocation, resulting in a 30 % reduction in energy consumption and a 25 % improvement in server utilization compared to baseline methods. The framework achieves latency as low as 5 milliseconds for augmented reality tasks while maintaining scalability in high-traffic 5 G environments. The proposed model also outperforms baseline approaches in terms of task completion time, throughput, and communication efficiency, and it achieves a 94.5 % offloading success rate and 98 % augmented reality delay compliance. The proposed model provides a scalable and useful solution for real-time Augmented Reality by combining energy-constrained task allocation with mobility-aware predictions.