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===Trust on Semantic Sensor Web===
 
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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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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.<br/>
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[Trust]]: Trust in Interpersonal, Social, and Sensor Networks
 
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Revision as of 19:02, 2 February 2011

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.

Research Topics

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.

Semantic Sensor Observation Service

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.

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.

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]]: Trust in Interpersonal, Social, and Sensor Networks

People


Publications


Presentations

  • Sensor Discovery on Semantic Sensor Web: Cory Henson delivers talk at the first W3C Semantic Sensor Network Incubator Group (SSN-XG) Face-to-Face meeting, 25-29 October 2009, Washington DC.
  • Semantic Annotation of Sensor Data: Cory Henson delivers talk at the first W3C Semantic Sensor Network Incubator Group (SSN-XG) Face-to-Face meeting, 25-29 October 2009, Washington DC.
  • Semantic Sensor Web: Amit Sheth delivers talk at Advancing Digital Watersheds and Virtual Environmental Observatories II session of AGU Fall Meeting, San Franscisco, December 17, 2008.
  • Semantic Sensor Web: Amit Sheth delivers talk at ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP, Melbourne, Australia, August 1, 2008.
  • Semantic Sensor Web: Cory Henson delivers talk at the Semantic Technology Conference, May 18-22, 2008, San Jose, California.
  • Semantic Sensor Web: Amit Sheth delivers invited talk at the Sensor Web Enablement (SWE) WG of the Open Geospatial Consortium (OGC) held in St. Louis, MO, March 26, 2008.
  • Semantic Sensor Web: Amit Sheth delivers talk at the Semantic Interoperability Community of Practice Special Conference, February 5, 2008, Falls Church, VA.
  • Semantic Sensor Web: Cory Henson delivers talk at the Sensor Standards Harmonization WG Meeting, January 15, 2008, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland.
  • Video on the Semantic Sensor Web: Amit Sheth delivers talk at the W3C Video on the Web Workshop, December 12-13, 2007, San Jose, CA, and Brussels, Belgium.
  • Semantic Web techniques empower perception and comprehension in Cyber Situational Awareness: Amit Sheth delivers talk at the ARO Cyber Situational Awareness Workshop, November 14-15, 2007, Fairfax, VA.


Ontologies and Datasets


Prototypes, Demos and Tools

  • Real Time Feature Streams: prototype
  • Trusted Perception Cycle: demo
  • Sensor Discovery On Linked Data: prototype, demo
  • Semantic Sensor Observation Service (MesoWest): demo
  • Semantic Sensor Observation Service (Buckeye Traffic): demo
  • Video on the Semantic Sensor Web: demo


Related Material

Computing for Human Experience


Contact Information: Cory Henson

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