XGate:可解释的增强学习,以实现物联网传感器网络中透明和可靠的API流量管理
Jianian Jin1, Suchuan Xing2, Enkai Ji3
1Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
XGate是一个可解释的强化学习框架,通过提供透明,人类可以理解的决策来增强物联网 (IoT) 传感器网络流量管理. 这导致性能提高,并增加了运营商对管理复杂的API流量的信心.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 物联网 (IoT) 设备及其API的普及使传感器网络流量管理变得复杂.
- 现有的交通管理解决方案往往缺乏透明度,阻碍了大规模部署的有效控制.
研究的目的:
- 推出XGate,一个新的可解释的强化学习框架,用于传感器网络中透明的API流量管理.
- 解决需要平衡最佳路由决策与网络管理员可解释性的需求.
主要方法:
- 集成基于变压器的注意力机制,以反事实推理为可解释的决策.
- 为传感器网络API流量量身定制的强化学习框架的开发.
- 通过对大型传感器API流量数据集和用户研究进行广泛的实验进行评估.
主要成果:
- 与最先进的黑子方法相比,XGate实现了23.7%更低的延迟和18.5%更高的吞吐量.
- 用户研究表明,操作员信任度提高了67%,干预时间减少了41%.
- 理论分析证实了对解释忠实性和计算效率的概率性保证.
结论:
- XGate为物联网基础设施的可靠人工智能提供了重大进展,提供了透明的决策.
- 该框架可以在动态传感器网络环境中提高性能,而不会牺牲可解释性.
- XGate为网络管理员提供了对复杂流量管理决策的可理解见解.
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