适应性人工智能增强的计算卸载与机器学习用于QoE优化和节能移动边缘系统
Dinesh Kumar Nishad1, Vandna Rani Verma2, Pushkar Rajput2
1Department of Electrical Engineering, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, India. dineshnishad@rediffmail.com.
Scientific reports
|May 1, 2025
概括
本研究介绍了移动边缘计算 (MEC) 系统的自适应人工智能增强卸载 (AAEO) 框架. 在动态的多用户环境中,AAEO框架显著提高了体验质量 (QoE) 和能源效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 电信工程 电信工程 电信工程
背景情况:
- 移动边缘计算 (MEC) 系统需要高效的计算卸载策略.
- 动态的多用户环境对优化体验质量 (QoE) 和能源效率提出了挑战.
研究的目的:
- 为MEC系统引入一种新的自适应人工智能增强卸载 (AAEO) 框架.
- 在动态的MEC环境中解决单个算法解决方案的局限性.
主要方法:
- 深度强化学习,进化算法和联合学习的整合.
- 为动态卸载战略调整开发混合架构.
- 使用MATLAB进行广泛的模拟,移动用户和边缘服务器数量不同.
主要成果:
- 实现了高达35%的QE改进和40%的能源消耗降低.
- 保持稳定的任务完成时间,在最大用户负载下仅增加12%.
- 显示了98%的威胁检测率,响应时间低于100毫秒,任务完成率为99.8%.
结论:
- 拟议的混合人工智能方法有效地应对异构环境中的复杂MEC挑战.
- AAEO框架在QOE,能源效率和可靠性方面提供了卓越的性能.
- 实时适应能力对于下一代MEC系统至关重要.
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