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）是核心监测应用的基石，但在不利环境中的部署会增加数据完整性和系统可靠性的风险。传统故障检测方法通常难以有效平衡精度与能耗，且可能无法充分利用WSN数据固有的复杂时空关联性。本文提出HiFiNet——一种新型分层故障识别框架，通过两阶段流程应对上述挑战。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序特征提取，并输出单个传感器节点的初始故障类别预测。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。该方法通过捕捉局部时序模式与全网空间依赖关系，能够生成更精确的预测。为验证该方法，我们通过在Intel Lab数据集和NASA MERRA-2再分析数据中引入预设的特定故障，构建了合成WSN数据集。实验结果表明，HiFiNet在准确率、F1分数和精确率上显著优于现有方法，展示了其在识别多种故障类型时的鲁棒性和有效性。此外，该框架的设计支持诊断性能与能耗之间的可调节权衡，使其能适应不同运行需求。