Difference between revisions of "Cory Andrew Henson"

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===Trust on Semantic Sensor Web===
 
===Trust on Semantic Sensor Web===
 
Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.
 
Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.
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===Semantic Sensor Observation Service===
 
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Sensor Observation Service (SOS) is a Web service specification defined by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) group in order to standardize the way sensors and sensor data are discovered and accessed on the Web. This standard goes a long way in providing interoperability between repositories of heterogeneous sensor data and applications that use this data. Many of these applications, however, are ill equipped at handling raw sensor data as provided by SOS and require actionable knowledge of the environment in order to be practically useful. There are two approaches to deal with this obstacle, make the applications smarter or make the data smarter. We propose the latter option and accomplish this by leveraging semantic technologies in order to provide and apply more meaningful representation of sensor data. More specifically, we are modeling the domain of sensors and sensor observations in a suite of ontologies, adding semantic annotations to the sensor data, using the ontology models to reason over sensor observations, and extending an open source SOS implementation with our semantic knowledge base. This semantically enabled SOS, or SemSOS, provides the ability to query high-level knowledge of the environment as well as low-level raw sensor data.
 
  
 
===Analysis of Streaming Sensor Data===
 
===Analysis of Streaming Sensor Data===

Revision as of 04:26, 24 November 2011

Cah.jpeg

Researcher at Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing
Ph.D. Candidate at Wright State University, Computer Science and Engineering

Research Interests

Primary research focus is on formal knowledge representation and ontology modeling in order to reason over sensor data and manage situational awareness.

Current Projects

Perception and Analysis of Sensor Data

Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. This is due, in part, to the bi-directional information flow between our sensory organs and analytical brain. Drawing inspiration from cognitive models of perception, we can improve machine perception by allowing communication from processes that analyze observations to processes that generate observations. Such a perceptual system provides effective utilization of resources by decreasing the cost and number of observations needed for achieving situation awareness.

Semantic Sensor Web

Millions of sensors around the globe currently collect avalanches of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with such diverse capabilities as range, modality, and maneuverability. It is possible today to utilize networks with multiple sensors to detect and identify objects of interest up close or from a great distance. The lack of integration and communication between these networks, however, often leaves this avalanche of data stovepiped and intensifies the existing problem of too much data and not enough knowledge. With a view to alleviating this glut, we propose that sensor data be annotated with semantic metadata to provide contextual information essential for situational awareness. In particular, we present an approach to annotating sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the W3C and Open Geospatial Consortium (OGC) and extends them with semantic Web technologies to provide enhanced descriptions and access to sensor data.

Semantic Modeling and Annotation of Sensor Data

Ontologies and other semantic technologies can be key enabling technologies for sensor networks because they will improve semantic interoperability and intergration, as well as facilitate reasoning, classification and other types of assurance and automation not included in the OGC standards. A semantic sensor network will allow the network, its sensors and the resulting data to be organised, installed and managed, queried, understood and controlled through high-level specifications. Ontologies for sensors will provide a framework for describing sensors. These ontologies will allow classification and reasoning on the capabilities and measurements of sensors, provenance of measurements and may allow reasoning about individual sensors as well as reasoning about the connection of a number of sensors as a macroinstrument. The sensor ontologies will, to some degree, reflect the OGC standards and, given ontologies that can encode sensor descriptions, understanding how to map between the ontologies and OGC models is an important consideration. Semantic annotation of sensor descriptions and services that support sensor data exchange and sensor network management will serve a similar purpose as that espoused by semantic annotation of Web services. This research is conducted through the W3C Semantic Sensor Network Incubator Group (SSN-XG) activity.

Trust on Semantic Sensor Web

Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.

Analysis of Streaming Sensor Data

Sensors are increasingly being deployed for continuous monitoring of physical phenomena, resulting in avalanche of sensor data. Current sensor data streams provide summaries (e.g., min., max., avg.) of how phenomena change over time; however, such summaries are of little value to decision makers attempting to attain an insight or an intuitive awareness of the situation. Feature-streams, on the other hand, provide a higher-level of abstraction over the sensor data and provide actionable knowledge useful to the decision maker. This work presents an approach to generate feature-streams in real-time. This is accomplished through the application of ontological domain knowledge in order to integrate multiple, multimodal, heterogeneous low-level sensor data streams and infer the existence of real-world events like Blizzard, RainStorm etc. The generated feature-streams are publicly accessible on the Linked Open Data (LOD) Cloud.

Publications

Google Scholar Index

2011

2010

2009

2008


Program Committee Member

  • SESA 2011: The 12th International Conference on Distributed Computing and Networking (ICDCN 2011), Workshop on Sensor-Enabled Situational Awareness
  • ESWC 2011: The 2011 Extended Semantic Web Conference
  • GEOProcessing 2011: The Third International Conference on Advanced Geographic Information Systems, Applications, and Services
  • SSN 2010: The 2010 International Semantic Web Conference (ISWC 2010), 3rd International Workshop on Semantic Sensor Networks
  • SWE 2010: The 2010 International Symposium on Collaborative Technologies and Systems (CTS 2009), Workshop on Sensor Web Enablement
  • ESWC 2010: The 2010 Extended Semantic Web Conference
  • SSN 2009: The 2009 International Semantic Web Conference (ISWC 2009), 2nd International Workshop on Semantic Sensor Networks
  • GeoS 2009: The Third International Conference on Geospatial Semantics
  • SWE 2009: The 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Workshop on Sensor Web Enablement
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