A metric-correlation-based distributed fault detection approach in wireless sensor networks

Fault detection in wireless sensor networks is a crucial and challenging task. Many detection approaches relying on specific rules or inference models have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, these approaches may require high communication overhead or computational cost, and many potential faulty sensors that may not generate anomalous sensor readings remain undetected. In this paper, we propose a metric-correlation-based distributed fault detection (MCDFD) approach. It is motivated by the fact that the correlations between sensor nodes' system metrics usually perform regularly, whereas abnormity of such correlations indicates failures. MCDFD explores sensor nodes' internal metric correlations using correlation value matrixes. An improved cumulative summation (CUSUM) algorithm is used to track gradual changes or abrupt changes. Once any changes occur in correlation value time sequences, potential failures can be detected. The apply of metric correlations has made MCDFD with high-energy efficiency and low computational complexity, since no communication overhead is incurred and CUSUM algorithm is simple for computation. Simulation results demonstrate MCDFD performs well in respects of higher detection accuracy and lower false positive rate even under high node failure ratios and dense distribution conditions.