Visualizing anomalies in sensor networks

Abstract

Diagnosing a large-scale sensor network is a crucial but challenging task due to the spatiotemporally dynamic network behaviors of sensor nodes. In this demo, we present Sensor Anomaly Visualization Engine (SAVE), an integrated system that tackles the sensor network diagnosis problem using both visualization and anomaly detection analytics to guide the user quickly and accurately diagnose sensor network failures. Temporal expansion model, correlation graphs and dynamic projection views are proposed to effectively interpret the topological, correlational and dimensional sensor data dynamics and their anomalies. Through a real-world large-scale wireless sensor network deployment (GreenOrbs), we demonstrate that SAVE is able to help better locate the problem and further identify the root cause of major sensor network failures.