Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems

Mobile Edge Computing (MEC) systems face critical challenges in optimizing computation offloading decisions while maintaining quality of experience (QoE) and energy efficiency, particularly in dynamic multi-user environments. This paper introduces a novel Adaptive AI-enhanced offloading (AAEO) framework that uniquely integrates three complementary AI approaches: deep reinforcement learning for real-time decision-making, evolutionary algorithms for global optimization, and federated learning for distributed knowledge sharing. The key innovation lies in our hybrid architecture's ability to dynamically adjust offloading strategies based on real-time network conditions, user mobility patterns, and application requirements, addressing limitations of existing single-algorithm solutions. Through extensive MATLAB simulations with 50-200 mobile users and 4-10 edge servers, our framework demonstrates superior performance compared to state-of-the-art methods. The AAEO framework achieves up to a 35% improvement in QoE and a 40% reduction in energy consumption, while maintaining stable task completion times with only a 12% increase under maximum user load. The system's security analysis yields a 98% threat detection rate, with response times under 100 ms. Meanwhile, reliability metrics indicate a 99.8% task completion rate and a mean time to failure of 1,200 h. These results validate the proposed hybrid AI approach's effectiveness in addressing the complex challenges of next-generation MEC systems, particularly in heterogeneous environments requiring real-time adaptation.