EDISKCO: Energy Efficient Distributed In-Sensor-Network K-center Clustering with Outliers
Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks one aims at grouping sensor measurements in groups of similar measurements. As sensor networks have limited resources in terms of available memory and energy, a major task sensor clustering is efficient computation on sensor nodes. As a dominating energy consuming task, communication has to be reduced for a better energy efficiency. Considering memory, one has to reduce the amount of stored information on each sensor node.
For in-network clustering, k-center based approaches provide k representatives out of the collected sensor measurements. We propose EDISKCO, an outlier aware incremental method for efficient detection of k-center clusters. Our novel approach is energy aware and reduces amount of required transmissions while producing high quality clustering results. In thorough experiments on synthetic and real world data sets, we show that our approach outperforms a competing technique in both clustering quality and energy efficiency. Thus, we achieve overall significantly better life times of our sensor networks.
|Authors:||Hassani M., Müller E., Seidl T.|
|Published in:||Proc. 3rd International Workshop on Knowledge Discovery from Sensor Data (SensorKDD 2009) in conjunction with 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, France|
|Publisher:||ACM - New York, NY, USA|
|Type:||Conference papers (peer reviewed)|
|Research topic:||Data Analysis and Knowledge Extraction|