Removing spatial outliers in PS applications

Abstract

In this paper we study the problem of sensor data verification in Participatory Sensing (PS) systems using an air quality/pollution monitoring application as a validation example. Data verification, in the context of PS, consists of the process of removing spatial outliers to properly reconstruct the variables of interest. We propose a hybrid neighborhood-aware algorithm for outlier detection that considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The algorithm utilizes the Delaunay triangulation and Gaussian Mixture Models to build neighborhoods based on the spatial and non-spatial attributes of each location. Our experimental results show that our hybrid algorithm performs as good as the best estimator while considerably reducing the execution time.