Sequential Behavior Pattern Discovery with Frequent Episode Mining and Wireless Sensor Network

By recognizing patterns in occupants' daily activities, building systems are able to optimize and personalize services. Established technologies are available for data collection and pattern mining, but they all share the drawback that the methodology used for data collection tends to be ill suited for pattern recognition. For this research, we developed a bespoke WSN and combined it with a compact data format for frequent episode mining to overcome this obstacle. The proposed framework has been evaluated with both synthetic data from a smart home simulator and with real data from a self-organizing WSN in a student's home. We are able to demonstrate that the framework is capable of discovering sequential patterns in heterogeneous sensor data. With corresponding scenarios, patterns in daily activities can be deduced. The framework is self-contained, scalable, and energy-efficient, and is thus applicable in multiple building system settings.