Efficient Data Aggregation for Human Activity Detection with Smart Home Sensor Network Using K-Means Clustering Algorithm

Smart home sensor network utilizes various sensors to measure physical and send data to a base station. The pattern of measured data in each room can be considered as an active pattern when activity occurrence in that room and a irrelevant pattern when no activity in the room. In order to improve data aggregation in smart home, we propose human activity pattern-based data aggregation, which applies K-means clustering algorithm based on human activity into cluster heads of cluster-based sensor network. The result of simulation shows that the clustering algorithm can detect active event by calculating the similarity between the active pattern of collected data and human activity according to room usage.