Range-Free Localization Schemes for Large Scale Sensor Networks

1. Range-Free Localization Schemes for Large Scale Sensor Networks Tian He, Chengdu Huang, Brian M. Blum, John A. Stankovic, Tarek Abdelzaher

2. Agenda • Introduction • Previous work • Range-based localization scheme • Range-free localization scheme • APIT • Simulation and Performance Comparison

3. Localization • What is “localization”? • Determining where a given node is physically located in a network. • Why do we need to localize a node? • Identify the location at which sensor reading originate. • In novel communication protocols that route to geographic areas instead of ID.

4. Range-Based Localization Schemes • Definition: • Use absolute point-to-point distance or angle estimates. • “Fine-grained” • Require complex and expensive hardware.

5. Some geometry • Triangulation • Determine the location by measuring angles from known points. • Trilateration • Determine the location by measuring distance between reference points. • Multilateration • Determine the location by measuring time difference of signal from reference points.

6. TOA (Time of Arrival) • GPS • Expensive in hardware and energy-consuming • TDOA (Time Difference of Arrival) • Extra hardware. Expensive and energy-consuming, too.

7. AOA (Angle of Arrival) • Estimate relative angles between neighbors. • Require additional hardware and is expensive to deploy In large sensor networks. • RSSI (Received Signal Strength Indicator) • Signal strength is reverse-proportional to the square of distance • Multi-path fading, background interference, irregular signal propagation make estimates inaccurate.

8. Range-Free Localization Schemes • Assume absolute point-to-point distance or angle estimates or are not available. • A coarse estimate is sufficient for most applications. • Inexpensive hardware.

9. Range-Free Localization • Centroid Algorithm • Simple to implement • Assume perfect spherical radio propagation. • Assume Identical transmission range for all radios. • Every anchor beacons location information (Xi,Yi) • A node approximates its location by averaging all location beacons received.

10. Range-Free Localization • DV-HOP • Similar to classical distance vector routing. • An anchor broadcasts a beacon to be flooded in the area.

11. HopSizeL1=

12. Range-Free Localization • Amorphous Positioning • First step is similar to DV-Hop. • Refine the computation with information from neighbors. • Assume the density of the network, nlocal is known.

13. nlocal

14. Range-Free Localization - APIT • Approximate Point In Triangulation • It isolates the environment into triangular region between beaconing nodes. • A node’s presence inside of outside of these triangular regions allows a node to narrow down the area in which it can potentially reside.

15. APIT – Main Algorithm

16. Perfect PIT Test

18. Perfect PIT Test - flaws • Sounds good, but what if the nodes can’t move…? • In addition, the cost to exhaustively search in all directions is high.

19. Approximation of the Perfect PIT Test • Assume in a narrow angle, the receive signal strength is monotonically decreasing. • The above assumption is experimentally valid.

20. Approximate PIT Test • Use neighbor information, exchanged via beaconing, to emulate the node movement in the Perfect PIT test.

21. Error case • InToOutError • The node is inside the triangle, but concludes based on the APIT test that it is outside the triangle • OutToInError • The node is outside the triangle, but concludes based on the APIT test that it is inside the triangle OutToInError InToOutError

22. Error Measurement • Now, the problem is whether the error rate is high or not? • The worst case error percentage is 14% • While node density increases, OutToIn error decreases,and InToOut error slightly increases.

23. APIT Aggregation • Aggregate all individual APIT test result through a grid scan algorithm, where a grid array is used to represent the maximum area in which a node will likely reside. • The aggregation masks individual errors.

24. Observations • APIT exploits the redundancy of sensor networks, so the aggregated decisions provide accurate estimates. • We can use a single moving anchor that sends out beacons at different locations to localize all nodes.

25. Simuation and Comparsion • Radio Model • The real life is never perfect • DOI: the irregularity of the radio pattern • Placement Model • Random placement • Uniform placement

26. Simulation Parameters • ND (Node Density) • AH (Anchors Heard) • ANR (Anchor to Node Range Ratio) • The distance of anchor beacon divided by the distance of node radio. • AP (Anchor Percentage) • DOI (Degree of Irregularity) • Placement • Default values in simulation • AH=16, ND=8 and ANR=10

27. Localization Error for Varying AH • Estimation error decreases as the number of AH increases. • APT outperform other techniques when AH > 8.

28. Localization Error for Varying ND • Centroid algorithm remain almost constant while varying ND. • When the node density is small, Amorphous has larger error due to offline-estimation.

29. Localization for Varying ANR • Larger ANR decrease the need for more anchors. • Estimation error increases as ANR increases. • Larger beacon propagation distance result in larger accumulated error.

30. Localization Error for Varying DOI • Both Centroid and APIT are more robust to the irregular radio pattern. • DV-Hop exchange online information between peers, resulting in better performance than Amorphous algorithm.

31. Performance and Requirement Summary • No single algorithm works best under all scenarios. • Centroid requires the smallest communication overhead but nevertheless simple. • APIT outperforms other algorithm when an irregular radio pattern and random node placement are considered.

32. Questions?