02164nas a2200145 4500008004100000245005300041210005000094260004500144490000800189520171600197653002401913653002401937100001701961856004001978 2013 eng d00aA Semantics-based Approach to Machine Perception0 aSemanticsbased Approach to Machine Perception aDaytonbWright State Universityc12/20130 vPhD3 aMachine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices. Advances in sensing technology hold the promise to revolutionize our ability to observe and understand the world around us. Yet the gap between observation and understanding is vast. As sensors are becoming more advanced and cost-effective, the result is an avalanche of data of high volume, velocity, and of varied type, leading to the problem of too much data and not enough knowledge (i.e., insights leading to actions). Current estimates predict over 50 billion sensors connected to the Web by 2020.1 While the challenge of data deluge is formidable, a resolution has profound implications. The ability to translate low-level data into high-level abstractions closer to human understanding and decision-making has the potential to disrupt data-driven interdisciplinary sciences, such as environmental science, healthcare, and bioinformatics, as well as enable other emerging technologies, such as the Internet of Things. The ability to make sense of sensory input is called perception; and while people are able to perceive their environment almost instantaneously, and seemingly without effort, machines continue to struggle with the task. Machine perception is a hard problem in computer science, with many fundamental issues that are yet to be adequately addressed, including: (a) annotation of sensor data, (b) interpretation of sensor data, and (c) efficient implementation and execution. This dissertation presents a semantics-based machine perception framework to address these issues.10aSemantic Perception10aSemantic Sensor Web1 aHenson, Cory uhttp://knoesis.wright.edu/node/2265