Discovery of Frequent Distributed Event Patterns in Sensor Networks

Today it is possible to deploy sensor networks in the real world and collect large amounts of raw sensory data. However, it remains a major challenge to make sense of sensor data, i.e., to extract high-level knowledge from the raw data. In this paper we present a novel in-network knowledge discovery technique, where high-level information is inferred from raw sensor data directly on the sensor nodes. In particular, our approach supports the discovery of frequent distributed event patterns, which characterize the spatial and temporal correlations between events observed by sensor nodes in a confined network neighborhood. One of the key challenges in realizing such a system are the constrained resources of sensor nodes. To this end, our solution offers a declarative query language that allows to trade off detail and scope of the sought patterns for resource consumption. We implement our proposal on real hardware and evaluate the trade-off between scope of the query and resource consumption.