Adaptive Sampling for Spatiotemporal Anomaly Monitoring in Wireless Sensor Networks
2026-07-16 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors developed a new method to help wireless sensor networks better detect short and local unusual events while saving energy. Normally, the sensors take fewer measurements using a smart prediction method called a Kalman filter. When something unusual happens, special sensors called sentinels watch more closely and alert nearby sensors to start paying attention too. Their tests showed this method catches more anomalies and costs less energy than some existing approaches. Overall, the authors' approach balances saving battery life with spotting important events more reliably.
wireless sensor networkssparse samplingKalman filteranomaly detectionsentinel nodesGeneralized Likelihood Ratio (GLR)adaptive sensingenergy efficiencyalert propagationspatiotemporal anomalies
Authors
Guoqing Lu, Yixuan Sun, Yiwen Jiang, Bernard Butler
Abstract
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.