Characterizing unpredictable patterns in Wireless Sensor Network data

Wireless Sensor Network (WSN) monitoring takes a primary role in many industrial and research processes. Huge amounts of WSN sensor readings are nowadays available and can be analyzed to discover fruitful knowledge. This paper focuses on analyzing historical WSN sensor readings to identify the combinations of sensors whose readings show an unexpected trend. Although significant variations of single sensor readings may be easily detected, discovering correlations between multiple sensor readings is challenging without using advanced data analytics tools. To tackle this issue, we present an itemset-based data mining approach to analyzing WSN data. It identifies the combinations of sensors (of arbitrary size) whose readings are unexpectedly low in a given time period. Since the readings acquired by multiple sensors may decrease in an alternate fashion, the discovered patterns provide new information compared to single sensor analysis. To make the mined patterns manageable by domain experts for manual inspection, the mining algorithm is driven by spatial constraints defined on the WSN topology. The experimental results, achieved on real WSN data, demonstrate the effectiveness of the proposed approach in detecting heating system malfunctioning.