Wireless Sensor Networks (WSN) are the backbone of essential monitoring applications, but their deployment in unfavourable conditions increases the risk to data integrity and system reliability. Traditional fault detection methods often struggle to effectively balance accuracy and energy consumption, and they may not fully leverage the complex spatio-temporal correlations inherent in WSN data. In this paper, we introduce HiFiNet, a novel hierarchical fault identification framework that addresses these challenges through a two-stage process. Firstly, edge classifiers with a Long Short-Term Memory (LSTM) stacked autoencoder perform temporal feature extraction and output initial fault class prediction for individual sensor nodes. Using these results, a Graph Attention Network (GAT) then aggregates information from neighboring nodes to refine the classification by integrating the topology context. Our method is able to produce more accurate predictions by capturing both local temporal patterns and network-wide spatial dependencies. To validate this approach, we constructed synthetic WSN datasets by introducing specific, predefined faults into the Intel Lab Dataset and NASA's MERRA-2 reanalysis data. Experimental results demonstrate that HiFiNet significantly outperforms existing methods in accuracy, F1-score, and precision, showcasing its robustness and effectiveness in identifying diverse fault types. Furthermore, the framework's design allows for a tunable trade-off between diagnostic performance and energy efficiency, making it adaptable to different operational requirements.

翻译：无线传感器网络（WSN）是重要监测应用的基石，但其在恶劣环境下的部署增加了数据完整性和系统可靠性的风险。传统故障检测方法常难以有效平衡准确度与能耗，且未能充分利用WSN数据中固有的复杂时空相关性。本文提出HiFiNet，一种通过两阶段流程解决上述挑战的新型层次化故障识别框架。首先，采用长短期记忆（LSTM）堆叠自编码器的边缘分类器执行时间特征提取，并输出单个传感器节点的初始故障类别预测；随后，基于这些结果，图注意力网络（GAT）通过整合拓扑上下文，聚合邻域节点信息以优化分类。该方法通过捕获局部时间模式与全网空间依赖性，能够生成更准确的预测。为验证该方案，我们在Intel Lab数据集和NASA MERRA-2再分析数据中引入预设特定故障，构建合成WSN数据集。实验结果表明，HiFiNet在准确率、F1分数和精确率方面显著优于现有方法，展现出识别多类型故障的鲁棒性与有效性。此外，该框架的设计支持诊断性能与能效之间的可调权衡，使其能够适应不同运行需求。