SECURE: Semantics Empowered resCUe enviRonmEnt


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Slides for Demo at SSN-2011 Workshop at ISWC2011.

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  • Machines interpreting the sensor data and sending only abstractions to the humans would be ideal. Else, the human has to interpret the sensor data which is not intuitive for humans.
  • Note: No free food! Someone has to do the processing!Machines/HumansIf traffic signal is red, then stop.If there are pedestrians crossing the road, then stop.If the speed limit is less than the current speed, slowdown.
  • SECURE: Semantics Empowered resCUe enviRonmEnt

    1. 1. SECURE: Semantics Empowered resCUeenviRonmEnt demo @ SSN-ISWC2011 P. Desai, C. Henson, P. Anandtharam, A. Sheth Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis), Wright State University, Dayton, Ohio Semantic Sensor Web @ Kno.e.sis
    2. 2. Introduction• Timely response of first responders is crucial in rescue operations.• First responders inundated with streams of data from sensors (machine + citizen). – “Emergency responders have to assimilate large amounts of information in very short periods of time” [Cowlard et. al].• Streams of data when interpreted with domain knowledge, results in abstractions.• Abstractions (intuitive to humans) makes first responders respond quickly in rescue environments. Cowlard, Adam and Jahn, Wolfram and Abecassis-Empis, Cecilia and Rein, Guillermo and Torero, Jos√©, Sensor Assisted Fire Fighting, In the Journal of Fire Technology, Volume 46, pp. 719-741, 2010
    3. 3. Motivation– Environment ignorant • Machines without any sensors
    4. 4. Motivation– Environment sensing • Machines with sensors Photo courtesy NASA The autonomous Urbie is designed for various urban operations, including military reconnaissanceand rescue operations.
    5. 5. Motivation– Environment comprehending • Machine with sensors + perceiving background knowledge + comprehending background knowledge Traffic signalsGoogle’s car that wonthe DARPA challenge. pedestrians Stimuli and others on roads. Speed restrictions
    6. 6. Project Focus• Building a rescue robot (mobile-platform) with many sensors.• Data Collection and annotation using SSN ontology.• Analysis to be carried out for situational awareness using perception ontology [2].• Visualization of the emergency situation in terms of abstractions.
    7. 7. System Architecture Robot (Mobile Platform) With Sensors Data CollectionEvents inenvironment Annotation Visualization Paper on Fire Perceptual Analysis
    8. 8. Data Collection + Annotation • Collection of data from sensors on the robot. – Position data: Observation form position sensors. – Sensor data: Observation from environment sensors. • Annotation of raw sensor data. – Use of SSN ontology which has concepts to describe sensors and their observations. Raw Sensor Data Robot (Mobile Platform) With Sensors Position data Position Data Stream Sensor Data (CO2, Temperature, IR, CO data.) Annotated AnnotatedPaper on Fire Data (triple Data Stream store)
    9. 9. Perceptual Analysis• Perceptual ontology used to derive abstractions from annotated sensor data.• Domain knowledge is used to derive these abstractions. Annotated Sensor Data Abstraction Stream Perceptual Reasoning Domain Knowledge Images:
    10. 10. Visualization• Visualization serves as a dashboard for presenting real-time: – Raw sensor data – Position Data – Derived abstractions – Video of the robot
    11. 11. Demo SECURE Online Demo: watch?v=smu9mPFFyNs Local
    12. 12. Conclusions• Robot with perceptual abilities give out abstractions that are intuitive to humans.• Demonstrated a real-time physical system that uses domain knowledge to process heterogeneous sensor data.• Demonstrated visualization of events (as abstractions) in an emergency situation in real-time.
    13. 13. References[1] Cory Henson, KrishnaprasadThirunarayan, AmitSheth, Pascal Hitzler, Representation of Parsimonious Covering Theory in OWL-DL, In: Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, United States, June 5-6, 2011.[2] Cory Henson, KrishnaprasadThirunarayan, AmitSheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, 2012. (accepted).Demos, Papers and more at: http://semantic-sensor-web.comSemantic Sensor Web @ Kno.e.sis