Doctor of Philosophy (PhD), Wright State University, 2013, Computer Science and Engineering PhD

A sensor network serves as a vital source for collecting raw sensory data. Sensor data are later processed, analyzed, visualized, and reasoned over with the help of several decision making tools. A decision making process can be disastrously misled by a small portion of anomalous sensor readings. Therefore, there has been a vast demand for mechanisms that identify and then eliminate such anomalies in order to ensure the quality, integrity, and/or trustworthiness of the raw sensory data before they can even be interpreted. Prior to identifying anomalies, it is essential to understand the various anomalous behaviors prevalent in a sensor network deployment. Therefore, we begin this work by providing a comprehensive study of anomalies that exist in a sensor network deployment, or are likely to exist in future deployments. After this thorough systematic analysis, we identify those anomalies that, in fact, hinder the quality and/or trustworthiness of the collected sensor data. One approach towards the reduction of the negative impact of misleading sensor readings is to perform off-line analysis after storing a large amount of sensor data into a centralized database. To this end, in this work, we propose an off-line abnormal node detection mechanism rooted in machine learning and data mining. Our proposed mechanism achieves high detection accuracy with low false positives. The major disadvantage of a centralized architecture is the tremendous amount of energy wasted while communicating the sensor readings. Therefore, we further propose an on-line distributed anomaly detection framework that is capable of accurately and rapidly identifying data-centric anomalies in-network, while at the same time maintaining a low energy profile. Unlike previous approaches, our proposed framework utilizes a very small amount of data memory through on-line extraction of few statistical features over the sensor data stream. In addition, previous detection mechanisms leverage sensor (open full item for complete abstract)

Committee: Bin Wang Ph.D. (Advisor); Yong Pei Ph.D. (Committee Member); Keke Chen Ph.D. (Committee Member); Shu Schiller Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science