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

MARCH, University of Cincinnati, 2009, Design, Architecture, Art and Planning : Architecture (Master of)

Transient movements instilled by humans and nature, shape and surround our environment through their remnants, which are perceived as constants. Buildings are remnants of the past, which typically develop restrictive boundaries, harness limited amounts of activity, and tend to lack the ability to dynamically react to variations of use. This thesis explores an architectural boundary that responds to, rather than restricts, internal and surrounding fluctuating forces. Architecture can be understood through a conceptual interpretation of space, movement, and boundaries interacting with movement. More specifically, these boundaries harness literal or phenomenal movement, also defined as formal movement that is actual or suggestive. Within the infrastructure of an urban, industrial, and vegetative site, this investigation seeks to accommodate current, revived, and fluctuating movement paths through the deployment of static and variable boundaries.

Committee: Rebecca Williamson (Committee Chair); Elizabeth Riorden (Committee Co-Chair) Subjects: Architecture; History; Interior Design; Recreation; Urban Planning