基于LSTM和无线传感器网络的注意力机制的双层投票控制系统的性能分析
Zhijun Yang1,2,3, Wenjie Huang2, Hongwei Ding2
1Educational Instruments and Facilities Service Center, Educational Department of Yunnan Province, Kunming 650223, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
一个新的两级投票系统提高了物联网 (IoT) 网络性能. 该系统提高了服务质量和网络公平性,优于现有模型,并使用长短期存储器 (LSTM) 网络实现了准确的性能预测.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 面临着越来越大的网络规模和服务需求带来的挑战.
- 预测网络性能对于优化资源配置和确保服务质量至关重要.
研究的目的:
- 为物联网网络提出一个连续时间的详尽限制 (K=2) 两级投票控制系统.
- 分析和预测这个新的投票系统的表现.
- 将其效率与现有网络模型进行比较.
主要方法:
- 开发了一个双层投票系统,在中央站点提供全面服务,在公共站点提供有限服务 (K=2).
- 使用概率生成函数和马尔科夫链,推导出平均队列长度,延迟和周期周期的确切表达式.
- 使用MATLAB进行模拟实验以验证理论发现.
- 构建了一个长期短期记忆 (LSTM) 神经网络,具有用于性能预测的注意力机制.
主要成果:
- 理论和模拟结果显示强烈一致,证实了系统的合理性.
- 拟议的系统在一级的详尽和有限 (K=2) 服务上分别显示了7.3%和12.2%的性能改进.
- 该系统为中央站点提供了更高的优先级,与封闭式-详尽的服务模型相比,显示了更少的长度和延迟.
- 与K=1模型相比,它增加了数据包传输和延迟,确保了可靠的无线网络服务.
结论:
- 详尽限定的 (K=2) 两级投票系统有效地平衡了中央站点的服务质量和公共站点的公平性.
- 将LSTM网络与注意力机制集成为预测系统性能提供了一种快速而准确的方法,特别是在大型系统中.
- 这种方法为具有严格延迟要求的无线网络服务提供了稳定可靠的保证.
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