对无人机辅助移动边缘计算系统的分析和预测
Xiong Wang1, Zhijun Yang1,2,3, Hongwei Ding1
1School of Information Science and Engineering, Yunnan University, Kunming, China.
Mathematical biosciences and engineering : MBE
|December 21, 2023
概括
这项研究将反向传播神经网络与移动边缘计算 (MEC) 集成,以优化任务调度. 无人机辅助的MEC模型显著降低了延迟和队列长度,提高了物联网 (IoT) 的网络性能.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 对物联网 (IoT) 的需求不断增长,需要低延迟网络.
- 移动边缘计算 (MEC) 通过将计算卸载到边缘服务器提供解决方案.
- 现有的MEC模型在优化任务调度和处理方面面临挑战.
研究的目的:
- 将回向传播 (BP) 神经网络与MEC集成,以提高网络性能.
- 开发一个无人机辅助的MEC模型,以优化任务调度和处理.
- 解决网络挑战,包括延迟,速度和吞吐量.
主要方法:
- 引入了一个无人机辅助的MEC模型,具有同步和异步计算卸载模式.
- 利用马尔科夫链和概率生成函数用于同步模式参数计算.
- 开发了一个BP神经网络,以计算异步模式中的平均队列长度和延迟.
主要成果:
- BP神经网络的结果与概率生成函数的理论值非常相匹配.
- 拟议的无人机辅助MEC模型与同步模式相比,表现出优越的性能.
- 实现了显著的延迟减少 (约. 11.72%) 和队列长度 (大约. 9.45%).这是一个很大的问题.
结论:
- 结合BP神经网络和MEC,有效优化了任务调度和处理.
- 无人机辅助的MEC方法显著提高了网络速度,减少了延迟,并提高了吞吐量.
- 该模型为物联网应用中的低延迟网络需求提供了可行的解决方案.
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