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The main challenge faced by outlier detection techniques designed for wireless sensor networks is achieving high detection rate and low false alarm rate while maintaining the resource consumption in the network to a minimum. In this paper, we propose an online outlier detection technique with low computational complexity and memory usage based on an unsupervised centered quarter-sphere support vector machine for real-time environmental monitoring applications of wireless sensor networks. The proposed approach is completely local and thus saves communication overhead and scales well with increase of nodes deployed. We take advantage of spatial correlations that exist in sensor data of adjacent nodes to reduce the false alarm rate in real-time. Experiments with both synthetic and real data collected from the Intel Berkeley Research Laboratory show that our technique achieves better mining performance in terms of parameter selection using different kernel functions compared to an earlier offline outlier detection technique designed for wireless sensor networks.