A Sensor Network-Based Data Stream Clustering Algorithm for Pervasive Computing

Pervasive computing is characterized by the integration with communication and digital media technology embedded to the people's living space. People can transparently access the digital service anywhere. Wireless sensor networks are a novel technology and have broad application prospects. With the maturity of the wireless sensor networks technology, pervasive computing is becoming a reality. It is become a new technology challenge to process the data streams of sensor networks for pervasive environment efficiently and to find useful knowledge in these data streams. A k-means data stream clustering algorithm based on sensor networks is presented. The main idea of this algorithm is to select the initial centroids according to the aggregation gain of the node, then to cluster the data stream using the average square error. The experimental results are showed that this algorithm is effective and efficient.