APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks IEEE INFOCOM 2008, Phoenix, AZ, USA Jaehoon Jeong, Shuo Guo, Tian He.

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Presentation transcript:

APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks IEEE INFOCOM 2008, Phoenix, AZ, USA Jaehoon Jeong, Shuo Guo, Tian He and David Du Computer Science and Engineering, University of Minnesota April 16th,

Problem Definition Wireless Sensor Deployment Detection Timestamp Target Detecting Sensors 2

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

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

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

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

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

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

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

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

Traffic Analysis for Road Segment Length Estimation  We want to estimate the length of the road segment between two neighboring sensors S 1 and S 2.  There are three sensors S 1, S 2, and S 3 as below: Neighboring Sensors S 1, S 2, and S 3 Vehicle Detection Sequence at Sensors S 1, S 2, and S 3 11 Correlation among Timestamps! vehicles

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

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

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

Outdoor Test for TDOD 15  Road Segment Estimation 43 sec 45 sec 56 sec54 sec 44.7 sec 50.3 sec TDOD gives good estimates for road segments.

Path Estimate vs. Road Segment Estimate sec 37 sec52 sec TDOD cannot give good estimates for paths. Path estimate has a large deviation per traffic measure.

Procedure of Prefiltering for Virtual Graph (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 17 Isomorphic

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 Topology of Wireless Sensors (c) Virtual Graph for Sensor Network 18

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

Graph Matching Example Virtual GraphVirtual Subgraph Road NetworkReal Graph Reduction Abstraction Matching 20

Node Location Identification  Localization of Intersection Nodes  We have localized Intersection Nodes with Permutation Matrix P.  Localization of Non-intersection Nodes  Let G v = (V v, E v ) be the virtual graph.  Beginning from an intersection node u in E v, we create a path from u to another intersection node v such as Virtual Graph 21 Real Graph Node Localization Done! uv

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 22

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. 23

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

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 system in real road networks with Motes such as XSM. 25

Q & A 26