Proposal of Online Outlier Detection in Sensor Data Using Kernel Density Estimation

Sensors in different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. Continuous outlier detection in data streams has important applications in fraud detection, network security, environmental monitoring and public health. In this paper, we propose a framework that computes in a distributed manner an approximation of multi-dimensional data distributions in order to enable complex applications in resource-constrained sensor networks. Here we are targeting the problem of outlier detection. We demonstrate how our technique can be used to identify either distance based or density based outliers in a single pass over the data. Our approach takes into consideration various characteristics and features of streaming sensor data.