用图神经网络分析无线信号强度异常，效率高且精度优。
Graph Neural Networks Based Anomalous RSSI Detection
- 将时序信号转为图结构，用图注意力网络检测单点异常
- 相比现有方法精度相当，参数量减少约171倍
- 适合需要低资源部署的物联网异常监测场景
现代基础设施广泛部署信息与通信技术，形成大规模物联网网络。为保障系统稳定运行，需主动检测链路故障或异常行为，避免业务中断。本文提出一种基于图神经网络的无线链路异常检测新方法：将时间序列数据转化为图结构，并训练一种新型图注意力网络架构，可精准识别时间序列中单个测量值的异常。该模型在性能上达到当前最优水平，同时计算效率显著提升，可训练参数仅约为现有方法的1/171。
原文摘要 · Abstract (English)
In today's world, modern infrastructures are being equipped with information and communication technologies to create large IoT networks. It is essential to monitor these networks to ensure smooth operations by detecting and correcting link failures or abnormal network behaviour proactively, which can otherwise cause interruptions in business operations. This paper presents a novel method for detecting anomalies in wireless links using graph neural networks. The proposed approach involves converting time series data into graphs and training a new graph neural network architecture based on graph attention networks that successfully detects anomalies at the level of individual measurements of the time series data. The model provides competitive results compared to the state of the art while being computationally more efficient with ~171 times fewer trainable parameters.
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