AI 中文总结
针对无线传感器网络长期监测中稀疏采样易漏异常的问题，提出哨兵辅助自适应采样框架，结合卡尔曼滤波器稀疏采样、哨兵广义似然比检验检测和局部警报传播，提高异常窗口采样率，降低成本，提升异常监测效果。
AI 中文摘要
无线传感器网络中的长期环境监测常采用稀疏采样来延长网络寿命，但稀疏传感可能会错过短暂、局部且可能扩散的异常。本文提出一种哨兵辅助的自适应采样框架，作为无线传感器网络异常监测的协作传感控制管道。正常时期，节点由卡尔曼滤波器预测不确定性驱动进行稀疏传感；异常时期，连续采样的哨兵节点基于混合广义似然比检验并使用节点相对阈值进行检测，局部检测通过具有恢复感知警报控制的一跳邻域唤醒。在英特尔伯克利研究实验室温度数据集上的实验表明，该方法在主要实验中将异常窗口采样率从0.439提高到0.933，还优于其他方法，同时分别降低总成本15.4%和2.1%。结果表明，集成基于卡尔曼滤波器的稀疏采样、哨兵广义似然比检验检测和局部警报传播可提高异常窗口可见性，同时保持较低的采样成本权衡。
英文摘要
Long-term environmental monitoring in wireless sensor networks (WSNs) often uses sparse sampling to extend network lifetime, but sparse sensing can miss short-lived, localized, and potentially diffusive anomalies. This paper proposes a sentinel-assisted adaptive sampling framework as a cooperative sensing-control pipeline for WSN anomaly monitoring. During normal periods, nodes perform sparse sensing driven by Kalman filter (KF) predictive uncertainty. During anomalous periods, continuously sampled sentinel nodes perform hybrid GLR-based detection with node-relative thresholds, and local detections trigger one-hop neighborhood wake-up with recovery-aware alert control. Experiments on the Intel Berkeley Research Lab temperature dataset with abrupt random spatiotemporal anomalies show that the proposed method raises the anomaly-window sampling ratio (AWSR) from 0.439 to 0.933 in the main experiment. It also improves AWSR over Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee (AAS) and Adapted e-Sampling while reducing total cost by 15.4\% and 2.1\%, respectively. These results show that integrating KF-based sparse sampling, sentinel GLR detection, and local alert propagation improves anomaly-window visibility while maintaining a lower sampling-cost trade-off.
CommentsAccepted paper for IEEE ISSC 2026 conference, Limerick, Ireland
