Situation Awareness in Cyber-Physical Systems using Indoor Localization and Semantic Data Abstraction

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Situation Awareness in Cyber-Physical Systems using Indoor Localization and Semantic Data Abstraction

  1. 1. Situation Awareness in Cyber-Physical Systems using Indoor Localization and Semantic Data Abstraction Presenter Pratikkumar Desai Advisor Dr. Kuldip Rattan PhD Seminar - 11/2/2012 Department ofElectrical Engineering
  2. 2. Outline • Introduction • Cyber-Physical System • Situation Awareness • Motivation • Problem Statement • Semantic Sensor Web • Indoor Localization Domain: Cyber-physical Systems Application: Situation Awareness Department ofElectrical Engineering
  3. 3. Cyber-Physical Systems Cyber : Computation, communication, and control that are discrete, logical, and switched Physical : Natural and human-made systems governed by the laws of physics and operating in continuous time Cyber-Physical Systems (CPS) : Systems in which the cyber and physical systems are tightly integrated at all scales and levels What it is not? • Not desktop computing • Not traditional embedded/real-time systems • Not today‟s sensor nets Department ofElectrical Engineering
  4. 4. CPS Examples eHealth Military Smart Home Department ofElectrical Engineering
  5. 5. Situation Awareness Projection of the future state or actions Comprehension of the current situation Perception of the elements in the current situation Department ofElectrical Engineering
  6. 6. Motivation Scenario First responders (Hazardous condition) • Responders are using sensor equipped mobile robot. • Building is equipped with a system which can provide location. • Need to Identify the situation from available sensor data. • Need to identify location of the event. Department ofElectrical Engineering
  7. 7. Perceptions & Comprehensions • Sensors on Mobile Robot: • Fire: • Temperature • Gas • Pressure • Liquid • Humidity • Wood • CO2 • Mix • CO • Dry heat • Chemical sensors • Dry Ice • Sensors in home: • False Alarm • Fire alarm • Chemical leakage • Thermostat • Human observation • Background knowledge Department ofElectrical Engineering
  8. 8. Information Overload Department ofElectrical Engineering
  9. 9. Problem Statements (1) Reduce information overload on the operator Solutions: • Semantic abstractions of sensor data • Semantic Sensor Web based automatic event comprehension (2) Identify the location of the event Solution: • Indoor localization in GPS denied environments Department ofElectrical Engineering
  10. 10. Semantic Sensor Web Department ofElectrical Engineering
  11. 11. Semantic Web “The Semantic Web is a major research initiative of the World Wide Web Consortium (W3C) to create a metadata- rich Web of resources that can describe themselves not only by how they should be displayed (HTML) or syntactically (XML), but also by the meaning of the metadata.” From W3C Semantic Web Activity Page “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001 Department ofElectrical Engineering
  12. 12. Semantic Sensor Web • An approach of annotating raw sensor data with semantic, spatial and temporal metadata to increase interoperability. • Provide abstraction to low level sensor data for more intuitive representation. • Provide Comprehension abstractions for event identification. Department ofElectrical Engineering
  13. 13. Cyber-Physical Systems for Situation Awareness Semantic Sensor Web Department ofElectrical Engineering
  14. 14. System Architecture of Semantic Web Implementation Department ofElectrical Engineering
  15. 15. Abstraction Bipartite Graph Sensor Perception and Event comprehension abstractions for the Example Scenario Department ofElectrical Engineering
  16. 16. Detecting a Fire • Temperature (local, on robot): = 100 C • Temperature (IR): = 150 C • IR light (looking straight): = 800 • IR light (looking down): = 800 • Carbon Dioxide: = 2000 ppm Department ofElectrical Engineering
  17. 17. Simulation Results Fire Robot Department ofElectrical Engineering
  18. 18. Future Work Unknown Event • Temperature (local, on robot): = 100 C • Temperature (IR): = 22 C • IR light (looking straight): =0 • IR light (looking down): = 800 • Carbon Dioxide: = 2000 ppm Department ofElectrical Engineering
  19. 19. Future Work Fuzzy Abstraction Set • Fuzzy abstraction sets can be useful in handling the problem of uncertain perception conditions. • An uncertain sensor perception can infer more than one situation in some cases. Department ofElectrical Engineering
  20. 20. Indoor Localization Department ofElectrical Engineering
  21. 21. GPS denied Environments Department ofElectrical Engineering
  22. 22. Traditional Indoor Localization Techniques • Active Badge and Active Bat system. • RADAR: An In-building RF-based user location and tracking system. RF • RFID radar Camera • Object tracking with multiple cameras • Computer vision based localization • Wireless Sensor Network TDoA Department ofElectrical Engineering
  23. 23. The Cricket Motes • High performance MICA2 wireless location system. • Ultrasound transmitter and receiver for time of flight ranging. • Decentralized and scalable operation. • Applications:- • Indoor location system • Ubiquitous computing • Asset/person tracking Department ofElectrical Engineering
  24. 24. Beacon & Listener Ceiling Listeners • Dynamic • Receivers Beacons • Static • Pseudo-Satellites floor Department ofElectrical Engineering
  25. 25. TDoA in The Cricket System Department ofElectrical Engineering
  26. 26. Localization: Trilateration Number of nodes = 3. Outlier Rejection and Multilateration are used to improve location results. Department ofElectrical Engineering
  27. 27. TDoA Based Positioning System Pros Cons • Distance estimation accuracy • Always need Line-of-Sight is less than 2 cm for 10 (LOS) for distance estimation. meters. • Do not work in presence • Appropriate for localizing of ultrasonic noise. small vehicles such as Mobile • Slow update rate for dynamic robots or a person. application. • Least expensive. Department ofElectrical Engineering
  28. 28. The Proposed Algorithm • Utilizes fusion of RSSI and TDoA data for accurate distance estimation. • The algorithm stages, • RSSI data training • Distance estimation • Localization • Uses TDoA as a primary distance estimation technique. • RSSI data is trained and converted into appropriate distance measurements. • The proposed algorithm can be used in absence of one or many TDoA links. Department ofElectrical Engineering
  29. 29. Initial Conditions • Distances between all beacons are known and fixed Department ofElectrical Engineering
  30. 30. Beacon B1 Transmit Data RSSI Link TDoA Link 0 ? ? ? 0 ? ? ? ? 0 ? ? R12 0 ? ? ? ? 0 ? ? ? 0 ? ? ? ? 0 B1 ? ? ? 0 B2 0 ? ? ? ? 0 ? ? ? ? 0 ? B4 R14 ? ? 0 B3 L 0 ? ? ? R1L T1L 0 ? ? ? ? 0 ? ? ? ? ? 0 ? ? ? ? 0 ? ? ? R13 ? 0 ? ? ? ? 0 ? ? ? ? ? 0 Department ofElectrical Engineering
  31. 31. Beacon B4 Transmit Data RSSI Link TDoA Link 0 R21 R31 R41 0 ? ? ? R12 0 ? ? R12 0 R32 R42 R13 R23 0 ? R13 R23 0 ? R14 R24 R34 0 B1 R14 R24 R34 0 B2 0 ? ? ? R12 0 ? ? R13 R23 0 ? B4 R14 R24 R34 0 B3 L 0 ? ? ? R1L T1L 0 ? ? ? R12 0 ? ? R2L T2L R12 0 ? ? R13 R23 0 ? R3L T3L R13 R23 0 R43 R14 R24 R34 0 R4L T4L R14 R24 R34 0 Department ofElectrical Engineering
  32. 32. Distance Estimation (i) • In traditional RSSI based estimation schemes, distance is estimated with following equation, 2 Pr GtGr (d ) 2 2 Pt (4 ) d L 2 ˆ 2 GtGr Pt 1 d 2 (4 ) L R SSI ˆ A d R SSI Where, A is an environment loss factor Department ofElectrical Engineering
  33. 33. Distance Estimation (ii) Where, „n‟ is number of data collected ˆ A ˆ d trained RSSI Department ofElectrical Engineering
  34. 34. IPS Results – Proposed Algorithm RMS error in position estimation (using proposed algorithm). Department ofElectrical Engineering
  35. 35. Summary • An approach of solving future situation awareness problems using Cyber-Physical Systems. • Introduced Semantic Web based sensor data abstraction to reduced the operator information overload. • A novel indoor localization algorithm to pinpoint the location of event being monitored. Department ofElectrical Engineering
  36. 36. Acknowledgement • Dr. Kuldip Rattan (Advisor) • Dr. Amit Sheth ( Co-advisor / Director, Kno.e.sis) Department ofElectrical Engineering
  37. 37. Questions ? Department ofElectrical Engineering
  38. 38. Demo [If time permits] SECURE : Semantic Empowered resCUe enviRonmEnt Department ofElectrical Engineering

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