Anomaly detection in streaming data from air quality monitoring system

Abstract

Detection of abnormalities is an important aspect of air quality monitoring. Wireless Sensor Networks (WSNs) provide a flexible and low-cost solution for air quality monitoring. However, considering the limited resources available in these networks in terms of power, memory and computational resources, obtaining a high anomaly detection rate while prolonging the life span of these networks is a challenging task. In recent years, both parametric and non-parametric algorithms are put forward to tackle this challenge. In order to save energy and memory, researchers have been investigating the iterative detection algorithms. In this thesis, we proposed a new efficient parametric iterative algorithm, in which the cumulative sum of likelihood ratio is calculated then we compare the cumulative sum with a manually defined control limit. We also evaluate effectiveness of our proposed algorithms both on synthetic data and real sensor data and compare it with a recently proposed algorithm. In evaluation on synthetic data, we design different experimental cases with respect to real environment and point out principles in selection of the two algorithms in practice. In evaluation on real data, we analyse and discuss the result and compare the effectiveness and efficiency of the two algorithms.