Event Detection through Differential Pattern Mining in Internet of Things

Detecting an event of interest, e.g., damage in aerospace vehicles from the continuous arriving data in Internet of Things (IoT) is challenging due to the detection quality. Traditional data mining schemes are employed to reduce data that often use metrics, association rules, and binary values for frequent patterns as indicators for finding interesting knowledge. However, these may not be directly applicable to the network due to certain constraints (communication, computation, bandwidth). We discover that, the indicators may not reveal meaningful information for event detection. In this paper, we propose a comprehensive data mining framework for event detection in IoT named DPminer, which functions in a distributed and parallel manner (data in a partitioned database processed by one or more sensor processors) and is able to extract a pattern of sensors that may have event information with a low communication cost. To achieve this, we introduce a new sensor behavioral pattern mining technique called differential sensor pattern (DSP) which considers different frequencies and values (non-binary) with a set of sensors. We present an algorithm for data preparation and then use a highly-compact data tree structure (called DP-Tree) for generating the DSP. Evaluation results show that DPminer can be very useful for networked sensing with a superior performance in terms of communication cost and detection quality compared to existing data mining schemes.