Adaptive Sampling for Spatiotemporal Anomaly Monitoring in Wireless Sensor Networks

2026-07-16

AI Summary

This study addresses the challenge of missing short-duration, localized, and diffusive spatiotemporal anomalies in wireless sensor networks due to sparse sampling. To overcome this limitation, the authors propose a sentinel-assisted adaptive sampling framework that employs Kalman filter–based uncertainty-aware sparse sampling during normal periods and activates sentinel nodes to perform hybrid generalized likelihood ratio (GLR) tests upon anomaly suspicion. Upon detection, these sentinels trigger one-hop neighborhood wake-up and initiate alert-driven sensing recovery. By integrating prediction-driven sampling, sentinel-based GLR detection, and localized alert propagation, the method significantly enhances anomaly window visibility while maintaining energy efficiency. Evaluated on the Intel Berkeley temperature dataset, the approach increases the anomaly window sampling rate from 0.439 to 0.933 and reduces total energy cost by 15.4% and 2.1% compared to AAS and e-Sampling, respectively.

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.

Problem

• spatiotemporal anomaly
• wireless sensor networks
• sparse sampling
• anomaly monitoring
• adaptive sampling

Innovation

• adaptive sampling
• sentinel-assisted sensing
• Kalman filter
• GLR-based detection
• spatiotemporal anomaly