Outlier detection in wireless sensor networks is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in wireless sensor networks is to adaptively identify outliers in an online manner with a high accuracy while maintaining the resource consumption of the network to a minimum. In this paper, we propose one-class support vector machine-based outlier detection techniques that sequentially update the model representing normal behavior of the sensed data and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection techniques achieve high detection accuracy and low false alarm rate.