Master of Science (MS), Wright State University, 2013, Computer Science

With sensors, storage, and bandwidth becoming ever cheaper, there has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of sensors collecting data about our environment, finding relevant sensors on the Web and then interpreting their observations is a non-trivial challenge. The Open Geospatial Consortium (OGC) defines a web service specification known as the Sensor Observation Service (SOS) that is designed to standardize the way sensors and sensor data are discovered and accessed on the Web. Though this standard goes a long way in providing interoperability between sensor data producers and consumers, it is predicated on the idea that the consuming application is equipped to handle raw sensor data. Sensor data consuming end-points are generally interested in not just the raw data itself, but rather actionable information regarding their environment. The approaches for dealing with this are either to make each individual consuming application smarter or to make the data served to them smarter. This thesis presents an application of the latter approach, which is accomplished by providing a more meaningful representation of sensor data by leveraging semantic web technologies. Specifically, this thesis describes an approach to sensor data modeling, reasoning, discovery, and query over richer semantic data derived from raw sensor descriptions and observations. The artifacts resulting from this research include: - an implementation of an SOS service which hews to both Sensor Web and Semantic Web standards in order to bridge the gap between syntactic and semantic sensor data consumers and that has been proven by use in a number of research applications storing large amounts of data, which serves as - an example of an approach for designing applications which integrate syntactic services over semantic models and allow for interactions with external reasoning systems. As more sensors and observations move o (open full item for complete abstract)

Committee: Krishnaprasad Thirunarayan Ph.D. (Advisor); Amit Sheth Ph.D. (Committee Member); Bin Wang Ph.D. (Committee Member) Subjects: Computer Science; Geographic Information Science; Information Systems; Remote Sensing; Systems Design; Web Studies

Master of Science (MS), Wright State University, 2011, Computer Science

The emergence of dynamic information sources - like social, mobile and sensors, has led to ginormous streams of real time data on the web also called, the era of Big Data [1]. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. Gigaom article on Big data shows, how the total information generated by these dynamic information sources has completely surpassed the total storage capacity. Thus keeping in mind the problem of ever-increasing data, this thesis focuses on semantically integrating and analyzing multiple, multimodal, heterogeneous streams of weather data with the goal of creating meaningful thematic abstractions in real-time. This is accomplished by implementing an infrastructure for creating and mining thematic abstractions over massive amount of real-time sensor streams. Evaluation section shows 69% data reduction with this approach.

Committee: Amit Sheth PhD (Advisor); Ramakanth Kavaluru PhD (Committee Member); Krishnaprasad Thirunarayan PhD (Committee Member) Subjects: Computer Science; Geographic Information Science

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

Machine 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. 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. The tangible primary contributions created to support the thesis of this dissertation include the development of a Semantic Sensor Observation Service (SemSOS) for accessing and querying sensor data on the Web, an ontology of perception (Intellego) that provides a formal semanti (open full item for complete abstract)

Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Payam Barnaghi Ph.D. (Committee Member); Satya Sahoo Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Science

Master of Sciences, Case Western Reserve University, 2020, EECS - Computer Engineering

The internet of things (IoT) is rapidly becoming part of everyday life. The internet of things can be anything from smart assistants, or smart devices such LED light bulbs, electric outlets to widely used wireless sensor networks. Electrical devices inside any household has potential to become part of wireless mesh network where each device is monitored for their operation and electrical energy consumption. Still, monitoring electric consumption inside a household is still not actively utilized under internet of things. Majority of the houses are equipped with smart energy meters which transmit weekly or monthly power usage to electrical companies. These readings are reflected in the electric bill every month and provide very crude and irrelevant information to pinpoint energy activities in the desired `meshes' of individual rooms of any household and therefore cannot meet the growing expectation and requirements for abundance and accuracy of the data, for efficient electrical energy management. After a comprehensive survey of existing energy monitoring devices and systems, a few technologies have come across which focus either on single device or on overall household. These technologies will not be able to pinpoint every device in a household. Apart from the surface level monitoring, these devices tend to be expensive as they come with subscription and added devices for complete support. To compete with such technologies, an electric energy monitoring system is proposed. This system has three layers of software and hardware components. The first layer is sensors. These sensors make use of existing wireless sensor network mesh technology. Each sensor is a low-cost Bluetooth low energy (BLE) based module which monitors electrical devices. The second layer is gateway. The gateway acts as the middle man between sensor and the third layer which is server. Gateway grabs data from the sensors and translates it to server compatible language package and sends it to the se (open full item for complete abstract)

Committee: Philip Feng (Advisor); Christos Papachristou (Committee Member); Kenneth Loparo (Committee Member) Subjects: Computer Engineering

MS, University of Cincinnati, 2009, Engineering : Electrical Engineering

Pouching machines are used to produce packaging for certain coustomer goods. There are consumer goods that are more effectively packaged in a pouch, such as spices, gravy mixes, drink mixes, and candy. There are a wide variety of pouch sizes and styles. Generally pouching machines are designed for one specific size pouch, and one specific product. This thesis discusses the current process, the need for adjustability, the new concept for an adjustable machine, and the continued improvement of the sensor and control network. The project was begun by an internationally known packaging company with its headquarters within the Greater Cincinnati area.

Committee: Emmanuel Fernandez PhD (Committee Chair); Arthur Helmicki PhD (Committee Member); Ali Minai PhD (Committee Member) Subjects: Electrical Engineering