APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks

1. Jaehoon Jeong, Shuo Guo, Tian He and David DuComputer Science and Engineering, University of Minnesota{jjeong,sguo,tianhe,du}@cs.umn.edu APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks April 16th, 2008 IEEE INFOCOM 2008, Phoenix, AZ, USA

2. Problem Definition Detection Timestamp Wireless SensorDeployment Target Detecting Sensors

3. APL Localization Sequence Timestamp Collection Timestamp Analysis Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

4. APL Localization Sequence Timestamp Collection Timestamp Analysis Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

5. APL Localization Sequence Timestamp Collection Timestamp Analysis Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

6. APL Localization Sequence Timestamp Collection Timestamp Analysis Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

7. APL Localization Sequence SensorNetwork Timestamp Collection Timestamp Analysis Prefilter Path Estimates Matching Road Network Reduce SensorNet Graph Perform Graph Matching

8. APL Localization Sequence Timestamp Collection Timestamp Analysis Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

9. APL Localization Sequence Timestamp Collection Timestamp Analysis Isomorphic Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

10. APL Localization Sequence Timestamp Collection Timestamp Analysis Matching Prefilter Path Estimates Reduce SensorNet Graph Perform Graph Matching

11. Traffic Analysis for Road SegmentLength Estimation • We want to estimate the length of the road segment between two neighboring sensors S1 andS2. • There are three sensors S1, S2, and S3 as below: Correlation among Timestamps! vehicles vehicles Vehicle Detection Sequence at Sensors S1, S2, and S3 Neighboring Sensors S1, S2, and S3

12. Time Difference on Detection (TDOD)Operation • Estimation of Movement Time through TDOD Operation • Time Difference On Detection(TDOD) for Sensors S1 and S2 Road Segment Length?

13. Comparison between Non-aggregation Method and Aggregation Method 26.8 sec WrongEstimate Speed LimitV =50 km/h Road LengthL =130 m Aggregation Window AccurateEstimate For Noise-Resilient Estimate,we compute Moving Average with Time Difference Window of 10 seconds. 9.3 sec Movement Time T = L/V=9.36 sec For Noise-Resilient Estimate,we compute Moving Sum with Aggregation Window of 5 seconds. Estimated Road LengthL’ =129.2 m

14. Outdoor Test for TDOD • Test Road Network Speed LimitV =64.4 km/h Speed LimitV =64.4 km/h Road (A, B)L =800 m Road (B, C)L =900 m Movement Time T = L/V= 44.7 sec Movement Time T = L/V= 50.3 sec

15. Outdoor Test for TDOD 44.7 sec • Road Segment Estimation 45 sec 43 sec 50.3 sec TDOD gives good estimates for road segments. 56 sec 54 sec

16. Path Estimate vs. Road Segment Estimate 95 sec 95 sec TDOD cannot give good estimates for paths. 37 sec 52 sec Path estimate has a large deviation per traffic measure.

17. Procedure of Prefiltering for Virtual Graph Isomorphic (a) Road Sensor Network (b) Virtual Topology for Sensors (c) Virtual Graph after Prefiltering based on Relative Deviation Error (d) Virtual Graph after Prefiltering based on Minimum Spanning Tree

18. Graph Matching • Now, we have a virtual graph whose subgraph is isomorphic to the real graph corresponding to the road network. (a) Road Sensor Network (b) Virtual Topologyof Wireless Sensors (c) Virtual Graphfor Sensor Network

19. Graph Matching Procedure • First, we find the Subgraph isomorphic to the real graph. • To find out Intersection Nodes from the Virtual Graph. Reduced Virtual Subgraph Reduction Reduction • Second, we perform the Isomorphic Graph Matching. • To find out the Optimal Permutation Matrix for matching. Matching Permutation

20. Graph Matching Example Virtual Graph Virtual Subgraph Reduction Matching Road Network Real Graph Abstraction

21. Node Location Identification • Localization of Intersection Nodes • We have localized Intersection Nodes with Permutation Matrix P. • Localization of Non-intersection Nodes • Let Gv = (Vv, Ev) be the virtual graph. • Beginning from an intersection node uin Ev, we create a path from u to another intersection node v such as Virtual Graph Real Graph u v Node Localization Done!

22. Performance Evaluation • We investigate the effect of the following three parameters on our localization: • Maximum Time Synchronization Error • Vehicle Speed Standard Deviation • Vehicle Interarrival Time • Simulation Setting • 18 sensors are deployed. • 10-hour road traffic measurement • Vehicle Speed: 50 km/h • Default Time Synch Error: 0.01 sec • Default Interarrival Time: 120 sec Road Network

23. Performance Comparison between Road Segment Estimation Methods • Maximum Time Synchronization Error • Up to 0.3-second time-synch error, 0% localization error rate can be achieved. • Vehicle Speed Standard Deviation • Up to 10 km/h vehicle-speed deviation,0% localization error rate can be achieved. • Vehicle Interarrival Time • For the interarrival time greater than 1 second,0% localization error rate can be achieved.

24. APL Operational Region • What range of (i) time synchronization and (ii) vehicle speed deviation does APL work well? Operational Region Localization Error Ratio Time Synch Error [sec] Vehicle Speed Deviation [km/h]

25. Conclusion • In sparse sensor networks over road networks, sensors cannot effectively obtain (i) pair-wise ranging distance or (ii) connectivity information. • Our Autonomous Passive Localization (APL) works well under realistic scenarios • With Vehicle-detection timestamps and • With Road map of target area. • As next step, we will perform the test of APL systemin real road networks with Motes such as XSM.

26. Q & A Thanks!