Distributed Mining of Spatio-Temporal Event Patterns in Sensor Networks

Many sensor network applications are concerned with discovering interesting patterns among observed real-world events. Often, only limited apriori knowledge exists about the patterns to be found eventually. Here, raw streams of sensor readings are collected at the sink for later offline analysis – resulting in a large communication overhead. In this position paper, we explore the use of in-network data mining techniques to discover frequent event patterns and their spatial and temporal properties. With that approach, compact event patterns rather than raw data streams are sent to the sink. We also discuss various issues with the implementation of our proposal and report our experience with preliminary experiments.

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