Anomaly detection in sensor networks identifies unusual patterns or outliers in data streams generated by distributed sensors. It typically involves analyzing sensor data to distinguish between normal behavior and deviations caused by faults, environmental changes, or security breaches. The process combines statistical methods, machine learning, and domain-specific rules to flag anomalies in real time or during post-processing. For example, a temperature sensor in a factory might suddenly report values far outside the expected range, signaling equipment failure or a fire. The system must differentiate between sensor errors (e.g., a faulty device) and genuine events to trigger appropriate responses.
Common techniques include threshold-based checks, clustering, and time-series analysis. Thresholds define acceptable ranges for sensor values, but they require calibration to avoid false alarms. Clustering algorithms like DBSCAN group similar data points, isolating outliers that don’t fit any cluster. For time-series data, methods like ARIMA or LSTM networks predict expected values and flag deviations. In distributed setups, sensors may collaborate locally: for instance, a network of humidity sensors in a farm could compare readings with neighbors to detect localized anomalies (e.g., a leaking irrigation pipe) without relying on a central server. Edge devices often preprocess data to reduce bandwidth, using lightweight models like decision trees to filter obvious outliers before sending data for deeper analysis.
Practical challenges include handling noisy data, resource constraints, and adapting to changing conditions. Sensors in harsh environments (e.g., industrial settings) may produce erratic readings due to interference, requiring noise filtering via moving averages or wavelet transforms. Resource-limited sensors might use simplified models, while cloud-based systems apply deep learning for complex patterns. Concept drift—where “normal” behavior evolves over time—is addressed by periodically retraining models. For example, a traffic monitoring system might adjust its anomaly thresholds during seasonal weather changes. Developers must balance detection accuracy with computational efficiency, often using hybrid approaches like federated learning to train models across sensors without centralized data collection.