Range-Free Localization Schemes in Large Scale Sensor Networks

1. Range-Free Localization Schemes in Large Scale Sensor Networks Tian He Chengdu Huang Brian. M. Blum John A. Stankovic Tarek F. Abdelzaher Department of Computer Science, University of Virginia APIT @ MobiCom'03 University of Virginia

2. Outline • Problem Statement • State of the Art • Motivation & Contribution • A.P.I.T. Algorithm Details • Evaluation • Conclusion APIT @ MobiCom'03 University of Virginia

3. Problem Statement • Localization Problem: • How nodes discover their geographic positions in 2D or 3D space? • Target Systems: • Static large scale sensor networks or one with a low mobility • Goal: • An affordable solution suitable for large-scale deployment with a precision sufficient for many sensor applications. APIT @ MobiCom'03 University of Virginia

4. State of the Art (1) • Range-based Fine-grained localizations • TOA (Time of Arrival ): GPS • TDOA (Time Difference of Arrival): MIT Cricket & UCLA AHLos • AOA (Angle of Arrive ): Aviation System and Rutgers APS • RSSI (Receive Signal Strength Indicator) : Microsoft RADAR and UW SpotOn Required Expensive hardware Limited working range ( Dense anchor requirement) Log-normal model doesn’t hold well in practice [D. Ganesan] APIT @ MobiCom'03 University of Virginia

5. State of the Art (2) • Range-Free Coarse-grained localization • USC/ISI Centroidlocalization • Rutgers DV-Hop Localization • MIT AmorphousLocalization • AT&T Active Badge Simple hardware/ Less accuracy APIT @ MobiCom'03 University of Virginia

6. Motivation • High precision in sensor network localization is overkill for a lot of applications. • Large scale deployment require cost-effective solutions. Routing Delivery Ratio Entity Tracking Time Under different localization Error ( % Radio Range) APIT @ MobiCom'03 University of Virginia

7. Contributions • A novel range-free algorithm with enhanced performance under irregular radio patterns and random node placement with a much smaller overhead than flooding based solutions • The first to provide a realistic and detailed quantitative comparison of existing range-free algorithms. • First investigation into the effect of localization accuracy on application performance APIT @ MobiCom'03 University of Virginia

8. Overview of APIT Algorithm • APIT employs a novel area-based approach. Anchors divide terrain into triangular regions • A node’s presence inside or outside of these triangular regions allows a node to narrow the area in which it can potentially reside. • The method to do so is called Approximate Point In Triangle Test (APIT). IN IN Out APIT @ MobiCom'03 University of Virginia

9. Pseudo Code: Receive beacons (Xi,Yi) from N anchors N anchors form triangles. For ( each triangle TiЄ ){ InsideSet  Point-In-Triangle-Test (Ti) } Position = COG ( ∩Ti  InsideSet); For each node Anchor Beaconing Individual APIT Test Triangle Aggregation Center of Gravity Estim. APIT Main Algorithm APIT @ MobiCom'03 University of Virginia

10. Point-In-Triangle-Test • For three anchors with known positions: A(ax,ay), B(bx,by), C(cx,cy), determine whether a point M with an unknown position is inside triangle ∆ABC or not. A(ax,ay) M B(bx,by) C(cx,cy), APIT @ MobiCom'03 University of Virginia

11. Perfect P.I.T Theory • If there exists a direction in which M is departure from points A, B, and C simultaneously, then M is outside of ∆ABC. Otherwise, M is inside ∆ABC. • Require approximation for practical use • Nodes can’t move, how to recognize direction of departure • Exhaustive test on all directions is impractical APIT @ MobiCom'03 University of Virginia

12. Departure Test Recognize directions of departure via neighbor exchange • Receiving Power Comparison ( the solution we adopt) • Smoothed Hop Distance Comparison ( Nagpal 1999 MIT) Experimental Result from Berkeley Experiment Result from UVA APIT @ MobiCom'03 University of Virginia

13. A.P.I.T. Test Approximation: Test only directions towards neighbors • Error in individual test exists , however is relatively small and can be masked by APIT aggregation. APIT(A,B,C,M) = IN APIT(A,B,C,M) = OUT APIT @ MobiCom'03 University of Virginia

14. APIT Aggregation • Aggregation provides a good accuracy, even results by individual tests are coarse and error prone. High Possibility area Grid-Based Aggregation With a density 10 nodes/circle, Average 92% A.P.I.T Test is correct Average 8% A.P.I.T Test is wrong Low possibility area Localization Simulation example APIT @ MobiCom'03 University of Virginia

15. Evaluation (1) • Comparison with state-of-the art solutions • USC/ISI Centroidlocalization by N.Bulusu and J. Heidemann 2000 • Rutgers DV-Hop Localization by D.Niculescu and B. Nath 2003 • MIT AmorphousLocalization by R. Nagpal 2003 Centroid DV-Hop (online)/ Amorphous (offline) APIT @ MobiCom'03 University of Virginia

16. Evaluation (2) • Radio Model: Continuous Radio Variation Model. • Degree of Irregularity(DOI ) is defined as maximum radio range variation per unit degree change in the direction of radio propagation α DOI =0 DOI = 0.05 DOI = 0.2 APIT @ MobiCom'03 University of Virginia

17. Simulation Setup • Setup • 1000 by 1000m area • 2000 ~ 4000 nodes ( random or uniform placement ) • 10 to 30 anchors ( random or uniform placement ) • Node density: 6 ~ 20 node/ radio range • Anchor percentage 0.5~2% • 90% confidence intervals are within in 5~10% of the mean • Metrics • Localization Estimation Error ( normalized to units of radio range) • Communication Overhead in terms of #message APIT @ MobiCom'03 University of Virginia

18. Error Reduction by Increasing #Anchors AH=10~28,ND = 8, ANR = 10, DOI = 0 Placement = Uniform Placement = Random APIT @ MobiCom'03 University of Virginia

19. Error Reduction by Increasing Node Density AH=16, Uniform, AP = 0.6%~2%, ANR = 10 DOI=0.1 DOI=0.2 APIT @ MobiCom'03 University of Virginia

20. Error Under Varying DOI ND = 8, AH=16, AP = 2%, ANR = 10 Placement = Uniform Placement = Random APIT @ MobiCom'03 University of Virginia

21. Communication Overhead • Centroid and APIT • Long beacons • DV-Hop and Amorphous • Short beacons • Assume: 1 long beacon = Range2 short beacons = 100 short beacons • APIT > Centroid • Neighborhood information exchange • DV-Hop > Amorphous • Online HopSize estimation ANR=10, AH = 16, DOI = 0.1, Uniform APIT @ MobiCom'03 University of Virginia

22. Performance Summary APIT @ MobiCom'03 University of Virginia

23. Hermes Project @ UVA NEST Demo EnviroTrack Real-Time Routing QoS Scheduling Data Aggregation Lazy Binding MAC Sensing Coverage APIT Localization Mote Test Bed APIT @ MobiCom'03 University of Virginia

24. Conclusions • Range-free schemes are cost-effective solutions for large scale sensor networks. • Through a robust aggregation, APIT performs best with irregular radio patterns and random node placements • APIT performs well with a low communication overhead( e.g. 2500 instead of 25,000 msgs) APIT @ MobiCom'03 University of Virginia

25. Questions? Thanks APIT @ MobiCom'03 University of Virginia

26. Error Case Since the number of neighbors is limited, an exhaustive test on every direction is impossible. • InToOut Error can happen when M is near the edge of the triangle • OutToIn Error can happen with irregular placement of neighbors PIT = IN while APIT = OUT PIT = OUT while APIT = IN APIT @ MobiCom'03 University of Virginia

27. Empirical Study on APIT Approximation • Percentage of error due to APIT approximation is relatively small (e.g. 14% in the worst case, 8% when density is 10) • More important, Errors can be masked by APIT aggregation. APIT Error under Varying Node Densities APIT @ MobiCom'03 University of Virginia