Multifloor tracking algorithms in Wireless Sensor Networks

1. Multifloor tracking algorithms in Wireless Sensor Networks Devjani Sinha Masters Project University of Colorado at Colorado Springs Location Tracking

2. Why Location Tracking is Useful?Adapted from Motetrack presentation • Assist Firefighters in Search/Rescue inside building • Often cannot see because of heavy smoke + are unfamiliar with building • Use wireless sensors (badge/beacon); GPS does not work in buildings • Can greatly benefit from a heads-up display to track their location and monitor safe exit routes • Chicago City Council … all buildings more than 80 feet tall must submit electronic floor plans [Forefront, Fall 2003] • Incident commander can better coordinate rescuers from command post Location Tracking

3. Related work • Motetrack (Harvard Lorincz and Matt Welsh) • TinyOS/Mote based. • 3D location tracking using radio signal information • Distributed/reference signature based. Thus more reliable. • No Multi floor implementation • Spot-on (Washington, Jeffrey Hightower and Gaetano Borriello/XeroxParc, Roy Want) • RFID, 3D location Tracking • Requires customized special software • centralized • No Simulation yet for Multi Floor • FRSN Location Tracking • TinyOS/Mote based. • Multi-Floor Simulation • 3D location tracking using radio signal information Location Tracking

4. Research Goals • Single Floor Location Tracking • Use Jeff Rupp's Obstructed Radio Model (2D) • 2D Hill climbing algorithm • Multi Floor Location Tracking (3D) • Extend Obstructed Radio Model to 3D • Extend Hill climbing algorithm to 3D • Analyze the performance and impact factors such as scaling, height, initial sensor sets • Develop tool to visualize the results. Location Tracking

5. Why Motes/TinyOS seems to be the right platform • MOTES are small in size • Easy to embed in environment and equipment • MOTES can operate off of battery + it is low power • Resilient to infrastructure failure • TinyOS is a well established platform • Used by over 150 research groups worldwide • Easy to integrate new sensors/actuators Mica2 mote Location Tracking

6. Modeling and Simulation • TinyOS – mote operating system • TOSSIM - Simulate TinyOS mote network • TinyViz – visual TOSSIM • Standard Java application • Uses a ‘plug-in’ architecture to allow for expansion • Wide array of existing plugins • Easy to expand Location Tracking

7. Obstructed Radio Model Plugin • Authored by Jeff Rupp, UCCS • Plug-in is based in the Radio Model done by Nelson Lee at Berkeley • Assumes 60dB equates to a maximum bit error rate • Radio signals are obstructed by varying amounts by different materials • Loss in free space over distance • walls presented low attenuation, about 3-12dB Location Tracking

8. Multi Floor model assumptions • For sake of simplicity, the following assumptions were made: • The layout of each floor is identical. • Every floor is setup with equal number of Beacon nodes 10ft above the floor. The mote layout is identical for each floor. • The floor height is set at 10 ft. • The attenuation of the floor/ceiling is assumed to be 20dB. • Cubicle attenuation is assumed to be 15dB • Outer Wall attenuation is assumed to be 35dB Location Tracking

9. Multi floor Setup in the GUI symbol is beacon sensor node. The label is sensor ID. Here small rooms has one sensor, large room has two. The hallway has 6 sensors. The top one is the sink node which collecting the sensor data. Location Tracking

10. Hill Climbing Algorithm Legend:Red square is actual target location. 4 purple/grey dots are sensors with strongest signals. Location Tracking

11. Hill Climbing Algorithm Based on the initial sensor set, an estimated location, x, is computed. Through perturbation, four neighboring locations from x is calculated and the one with closest estimated signal strengths will be chosen for next round. x Location Tracking

12. Responder Position in GUI Here the red squares are randomly generated firefighter locations. The overlay green squares are estimated locations. Location Tracking

13. Identical results for SF=1 and SF=2 SF=2 results in error and variance in tracking Performance: Effect of Scaling Factors Single Floor Multi Floor Location Tracking

14. Varying Z value for responder • Marginal Differences Location Tracking

15. Top4 vs. Top3 motes • Top3 results in error and variance in tracking • Top3 results in zero convergence issues Location Tracking

16. Conclusions • This concept can be developed using small, inexpensive and low-power devices • Using radio signal information alone, it is possible to determine the location of a roaming node at close to meter-level accuracy. • First Responder Sensor Network software provides an attractive solution to the critical problem of indoor location tracking. • The multi floor model is quite robust to variations in z co-ordinate of responder. • Using top 4 beacon motes in the algorithm gives more accurate results Location Tracking

17. Future Work • Incorporate Java 3D API in TinyViz • 2D Mote Network conversion to 3D • Multi Floor display of Responder Positions • Implementation of Multi Floor FRSN Location Tracking

18. Key References • Konrad Lorincz and Li Li, “MoteTrack: A Robust, Decentralized Approach to RF-Based Location Tracking,” Proceedings of the International Workshop on Location and Context-Awareness (LoCA 2005) at Pervasive 2005, May 2005. • “MoteTrack: An Indoor Location Detection System for Sensor Networks”, Konrad Lorincz and Li Li, Harvard University. (http://www.eecs.harvard.edu/~konrad/projects/motetrack/) • “Radio Signal Obstruction Plug-in for TinyViz” by Jeff Rupp, CS526 from UCCS CO 80933-7150, Fall 2003. • “TOSSIM: A Simulator for TinyOS Networks” by Philip Levis and Nelson Lee, (Version 1.0 - June 26, 2003), September 17, 2003. Location Tracking

19. Questions? Location Tracking