语义意识强化和集体学习用于物联网系统中的信号管理和异常检测
Samad Wali1, Muhammad Irfan Khan2, Mudassar Imran3
1General Education Centre, Quanzhou University of Information Engineering, Quanzhou, 362000, Fujian, China.
Scientific reports
|November 29, 2025
概括
这项研究介绍了一种使用深度强化学习和物联网 (IoT) 网络的随机森林的语义意识框架. 它提高了信号质量,并在动态环境中有效检测异常.
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:
- 大规模的物联网 (IoT) 网络由于动态环境而面临信号质量和异常检测方面的挑战.
- 不同质的物联网环境需要先进的解决方案来实现可靠的通信和及时的问题识别.
研究的目的:
- 提出一个语义意识的混合框架,集成深度强化学习 (DRL) 和随机森林 (RF),用于物联网网络中的智能信号优化和异常检测.
- 通过统一信号优化,连接状态验证和实时异常检测来增强网络管理.
主要方法:
- 开发了一个混合框架,将深度Q网络 (DQN) 结合起来用于信号分配,RF用于连接状态预测,以及隔离森林 (IF) 用于异常检测.
- 利用了含义上丰富的功能,包括收到信号强度指标 (RSSI),主动基站和地理空间时间上下文.
- 评估了Sigfox和LoRaWAN数据集的框架.
主要成果:
- 该DQN代理实现了超过62,000的累计奖励,证明了有效的信号优化.
- 射频分类器在预测连接状态时达到99.98%的准确性.
- IF模块在异常检测方面实现了99.97%的准确性,精度,回忆和F1得分为99.95%,检测到719个异常.
结论:
- 拟议的语义意识框架为异质物联网环境提供了强大的,上下文意识的网络管理.
- 它有效地将优化,验证和异常检测在闭环中统一起来,提高了通信效率和可靠性.
- 该框架适用于弹性下一代物联网和6G支持系统,支持自适应实时操作.
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