A hypergrid based adaptive learning method for detecting data faults in wireless sensor networks

Abstract

In wireless sensor networks (WSNs), data anomalies/faults often occur due to the limited resources and unreliability of sensor nodes. Many traditional anomaly detection methods are designed in a batch manner, but for the nature of streaming data in WSNs, continuous anomaly detection method is preferred. Existing methods often detect only a single type of faults but cannot detect multiple types of faults that actually are more common in the sensor data. Therefore, this paper provides a Hypergrid based Adaptive Detection of Faults (HADF) method, which adopts hypergrid and statistical analysis to recognize three types of faults in the sensor data, including outliers, stuck-at faults, and noisy faults. HADF is a distributed method running on sensor nodes, which can reduce the influence of concept drift in unstable streaming data through combining both lazy leaning and continuous learning to adaptively update its normal profile. In the experimental study, we have manually inserted different types of faults into two real-world datasets, and the results demonstrate that HADF achieves higher accuracy with reasonable efficiency for detecting the data faults than four counterpart methods.