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A High-Accuracy, Low-Cost Localization System for Wireless Sensor Networks

A High-Accuracy, Low-Cost Localization System for Wireless Sensor Networks. Radu Stoleru, Tian He, John A. Stankovic, David Luebke University of Virginia Department of Computer Science. Overview. UNIVERSITY of VIRGINIA. ACM SenSys November 2-4, 2005. Problem Statement State of Art

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A High-Accuracy, Low-Cost Localization System for Wireless Sensor Networks

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  1. A High-Accuracy, Low-Cost Localization System for Wireless Sensor Networks Radu Stoleru, Tian He, John A. Stankovic, David Luebke University of Virginia Department of Computer Science

  2. Overview UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Problem Statement • State of Art • Spotlight: THE System • System Implementation • Performance Evaluation • Conclusions

  3. Problem Statement UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Localization: • Find the position of sensor nodes • Research Challenge: • Current limitations: • Sophisticated Hardware: timesynch with satellites (GPS) • Short Range, Highly Directional (ultrasound) • Reduced Cost Effectiveness: hefty price for a 1-time event • Large Form Factor (any solution that requires additional hardware) High Location Accuracy ≡ High Cost How to solve this, practically?

  4. State of Art UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Many algorithmic solutions, few evaluated in realistic environments • Solutions that use ranging: • MIT Cricket (Priyantha et at., 2000) • UCLA AHLoS (Savvides et al., 2000) • Microsoft RADAR (Bahl et al., 2000) • UWashington SpotON (Hightower et al., 2000) • GPS • Range-free solutions: • USC Centroid (Bulusu et al., 2000) • MIT Amorphous Computing (Nagpal et al., 2003) • Virginia APIT (He et al., 2003) • Rutgers DV-Hop (Niculescu et al., 2003)

  5. System Design UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Main Idea: • Generate well-controlled (spatial/temporal) events in the sensor field • A node generates a timestamp when it detects an event • Using the above, obtain spatial information (i.e., location) for a node • Scenario: • Nodes deployed from UAV (Spotlight device) • Nodes self-organize, time-synchronize • The UAV generates light events • Sensor nodes detect the events and report the timestamps • The Spotlight device computes the location of the sensor nodes

  6. System Design UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Assumptions: • Line of Sight • Position and orientation for the Spotlight device • GPS/INS (Inertial Navigation System) – cm positioning, arcsec attitude • Map of deployment area: • LIDAR (Light Detection and Ranging) 10s centimeter accuracy • SAR (Synthetic Aperture Radar) 10s centimeter accuracy. • Powerful Spotlight device • >1000meters

  7. System Design UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. Generates Events Timestamp Events Report Timestamps Compute Location Report Location Spotlight System Architecture Event Distribution Function: the core of Spotlight We propose 3 Functions

  8. System Design 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111 UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Line Scan • Event Distribution Functions: • Point Scan • Area Cover

  9. System Design Criterion Point Scan Line Scan Area Cover Localization Time # Detections 1 2 logrD # Time Stamps 1 logrD 2 Event Overhead D2 2D2 D2logrD/2 UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Execution Cost assuming: • All nodes in a square area, with length D • N events / unit time generated by the Spotlight device • r is tolerable localization error • Event Overhead / Unit Time  Spotlight Device Power

  10. System Implementation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • mSpotlight System • Spotlight Device: • Projector • Laptop • Mica2 motes • Short range (10-20m) • Versatile, it generates: • Point Scan • Line Scan • Area Cover • Purpose: Investigate Capabilities

  11. System Implementation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Spotlight System • Spotlight Device: • Telescope Mount • Diode Laser • Laptop • XSM motes • Long range (>1000m) • It generates: • Point Scan • Line Scan (recently) • Demo: later today

  12. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Parameters Investigated • EDF (Point & Line Scan, Area Cover) • Event Size • Scanning Speed • Laser Power • Distance: Spotlight Device, Nodes • Performance metric: • Localization Error • Localization Time • Localization Range • Bias in Location Estimation

  13. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • mSpotlight - Point Scan EDF Localization Error vs. Event Size Localization Duration vs. Event Size

  14. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • mSpotlight - Line Scan EDF Localization Error vs. Event Size Localization Duration vs. Event Size

  15. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • mSpotlight - Line Scan EDF Position Estimation Bias Position Estimation w/o Bias (ideal)

  16. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • mSpotlight - Area Scan EDF Localization Error vs. Event Size Localization Duration vs. Event Size

  17. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Spotlight - Line Scan EDF Localization Error vs. Event Size Localization Duration vs. Event Size

  18. Performance Evaluation UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Spotlight - Line Scan EDF Localization Range Detectable Event Sizes

  19. Future Work Distributed Spotlight System Localization Overhead Reduction Dynamic Event Distribution Function Stealthiness Deployment in Unknown Terrain Self-Calibration Event Distribution Detection and Reporting UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005.

  20. Conclusions UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. • Contributions: designed, implemented, evaluated the Spotlight Localization System • Main ideas: • Well-controlled (spatio-temporal) events in the network. Infer spatial information from temporal knowledge • Long Effective Range (1000s meters) • High Accuracy (10s centimeter) • No hardware changes to existing motes • Reusable localization system (amortized cost) Complexity

  21. UNIVERSITY of VIRGINIA ACM SenSys November 2-4, 2005. Thank you!

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