Sensor networks have recently found many popular applications in a number of different settings. Sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. In this paper, we propose a framework that computes in a distributed fashion an approximation of multi-dimensional data distributions in order to enable complex applications in resource-constrained sensor networks.We motivate our technique in the context of the problem of outlier detection. We demonstrate how our framework can be extended in order to identify either distance- or density-based outliers in a single pass over the data, and with limited memory requirements. Experiments with synthetic and real data show that our method is efficient and accurate, and compares favorably to other proposed techniques. We also demonstrate the applicability of our technique to other related problems in sensor networks.
['Sharmila Subramaniam', 'Themis Palpanas', 'Dimitris Papadopoulos', 'Vana Kalogeraki', 'Dimitrios Gunopulos']
Online outlier detection in sensor data using non-parametric models