We propose an online and local outlier detection technique with low resource consumption based on an unsupervised centered quarter-sphere support vector machine for wireless sensor networks. Using synthetic data, we demonstrate that our technique achieves better mining performance in terms of parameter selection using difference kernel functions compared to an earlier o²ine outlier detection technique