Detecting time-related changes in Wireless Sensor Networks using symbol compression and Probabilistic Suffix Trees

Abstract

Our research focuses on anomaly detection problems in unknown environments using Wireless Sensor Networks (WSN). We are interested in detecting two types of abnormal events: sensory level anomalies (e.g., noise in an office without lights on) and time-related anomalies (e.g., freezing temperature in a mid-summer day). We present a novel, distributed, machine learning based anomaly detector that is able to detect timerelated changes. It consists of three components. First, a Fuzzy Adaptive Resonance Theory (ART) neural network classifier is used to label multi-dimensional sensor data into discrete classes and detect sensory level anomalies. Over time, the labeled classes form a sequence of classes. Next, a symbol compressor is used to extract the semantic meaning of the temporal sequence. Finally, a Variable Memory Markov (VMM) model in the form of a Probabilistic Suffix Tree (PST) is used to model and detect time-related anomalies in the environment. To our knowledge, this is the first work that analyzes/models the temporal sensor data using a symbol compressor and PST in a WSN. Our proposed detection algorithm is distributed, "light-weight", and practical for resource constrained sensor nodes. We verify the proposed approach using a volcano monitoring dataset. Our results show that this approach yields the same performance as the traditional Markov models with much less cost.