1. Wireless Sensor Network Localization Overview

2. Motivation “ Location is everything” Military Surveillance (meters)
Location-aware services
Military Surveillance (meters)
Location-aware Services (10 meters)
Interactive Gaming– WII (center meters)

3. Localization Technical related to localization Node Localization
Target (source) Localization
Location Directory Services
person
event
Base Station
vehicle

4. The need for Localization1.
Location of Data 2.
Geo-Routing 3.
Object Tracking
Sensing Coverage 4.
GHT-Base Storage 5.
L-based Query 6.
Location of Data Source
Geo-Routing/Location-Scoped Routing
Object Tracking
Sensing Coverage
GHT:A Geographic Hash Table
Location-based query
Location-based services (e.g. Virtual touch screen, point&use)
Location-based
Services (point & use) 7.

5. Outline Introduction Localization Taxonomy
Range-based localization
Range-free localization
Event-driven
Overview of 5 different schemes
Range Free localization

6. Existing Solutions (Outdoor)
Outdoor GPS
Positioning with Wifi Hotspots
Range-Based
use absolute distance estimates
or angle estimates
Expensive, Precise
Range-Free
use only connectivity and proximity
Cheap, Inaccurate

7. Existing Academia Solutions
Range-Based
MIT Cricket (Priyantha et at., 2000)
UCLA AHLoS (Savvides et al., 2000)
UWashington SpotON (Hightower et al., 2000)
Range-Free
Virginia APIT (He et al., 2003)
MIT Amorphous Computing (Nagpal, 2003)
Rutgers DV-Hop (Niculescu et al., 2003)
USC Centroid (Bulusu et al., 2000)
Event-Driven
Spotlight, Lighthouse, MSP

8. Range based localization (1)
Fine-grained localizations
Uses absolute point to point distance/angle estimates
Requires expensive hardware
Common examples
Time of Arrival (TOA)
Time Difference of Arrival (TDOA)
Angle of Arrive (AOA)
Receive Signal Strength Indicator (RSSI)

9. Basic idea of Range-based Solutions
Relative locations are unique if the graph is rigid
To avoid translation, rotation and flipping, d+1 anchors are needed to obtain global positions in d dimension space.
Non-rigid
rigid
rigid

10. Well-known Range-based solutions
GPS
Cricket (Priyantha ‘00)
AHLoS (Savvides ‘00)
Calamari (Whitehouse ’02)
Robust Quadrilaterals (Moore ‘04)
RIPS (Maroti ’05)
TOA
TDOA
RSSI
Noise issue
Interferometric
Range-Based solutions
Robust Ranging + Rigid Theory + Geometry

11. Range free localization (1)
Coarse-grained localization
Localization that does not rely on distance measurement
Simple hardware/Low cost solution

12. Anchor based localization
Centroid Algorithm (Bulusu ‘00)
APIT Centroid (He’03)
SeRLoc (Lazos 04)
Gradient algorithm (Nagpal ‘03)
APS – AdHoc Positioning System (Niculescu ‘03)
Five papers!!!
Check them out for more details

13. Anchor based localization
Centroid Algorithm
APIT
SeRLoc
Gradient algorithm
APS – AdHoc Positioning System
Five papers !!!

14. Centroid Algorithm (1) Anchor nodes (Reference nodes)
With known locations
Overlapping regions of coverage

15. Centroid Algorithm (2) Connectivity based approach
Anchors broadcast their position periodically
Packets with sequence numbers
The node Nk selects
the closest anchors Nk

16. Centroid Algorithm (2) Choose the anchors with CM > 90%
The node Nk localizes itself to the centroid Nk

17. Anchor based localization
Centroid Algorithm
APIT
SeRLoc
Gradient algorithm
APS – AdHoc Positioning System
Five papers !!!

18. APIT Localization An area-based approach: APIT Localization Scheme
Approximated Point-In-Triangle Test.
Anchor nodes divide sensor field into triangular regions.
Given whether a node N locates inside or outside particular triangle
Formalize the problem

19. 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

20. Departure Test Recognize directions of departure via neighbor exchange
Receiving Signal Strength Comparison (Monotonic properties)
Experimental Result from Berkeley Experiment Result from UVA

21. 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

22. 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

23. Anchor based localization
Centroid Algorithm
APIT
SeRLoc
Gradient algorithm
APS – AdHoc Positioning System
Five papers !!!

24. SeRLoc (1) Secure Range Independent Localization
Anchor nodes are equipped with directional sectored antenna
Normal nodes have Omni-directional antennas
Anchors transmit beacons within a sector
Each beacon contains anchor location and angles of sectors

25. SeRLoc (2) Region of Intersection Anchor Sensor θ3,2 A4 θ3,1 A3 A1 s
Coordinates
Slopes A1
(X1, Y1)
[θ1,1, θ1,2] A2
(X2, Y2)
[θ2,1, θ2,2] A3
(X3, Y3)
[θ3,1, θ3,2] A4
(X4, Y4)
[θ4,1, θ4,2] s A3
(0, 0)

26. SeRLoc (3)
A node collects beacons and determines a search area (rectangular)
Computes the overlapping region using a majority vote.
Finds center of gravity

27. Sensor places a grid of equally spaced points into the search area.
SeRLoc (4)
Anchors
Sensor
Search Area
(Xmin+R, Ymax-R)
(Xmin+R, Ymin+R)
(Xmax-R, Ymin+R)
(Xmax-R, Ymax-R)
2R+Xmin - Xmax
Sensor places a grid of equally spaced points into the search area. A4
Anchors heard
by the sensor
(X4, Y4)
(X1, Y1)
(X2, Y2)
(X3, Y3) R R
Define:
Xmin = min { Xi i  AHs }
Ymin = min { Yi i  AHs }
Xmax = max { Yi i  AHs }
Ymax = max { Yi i  AHs }
XA1
yA2 A3 A1
XA3 s
yA4 A2

28. SeRLoc (5) ROI Sensor Search Area 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 …
ROI

29. Anchor based localization
Centroid Algorithm
APIT
SeRLoc
Gradient algorithm
APS – AdHoc Positioning System
Five papers !!!

30. Gradient Algorithm (1) Step 1: Distance Estimation
The anchor nodes broadcast their position
first counts the hops to anchor
then estimate its distance from the anchor
Step 2: Multilateration
With known distances from three anchors, a node can use multilateration to estimate its own coordinates

31. Step 1: Distance Measurement (1)
Each anchor broadcasts a packet (gradient) with location and sequence number.
(x1, y1)
Anchors
(x1, y1) 2
Sensor
(x1, y1) 1

32. Step 1: Distance Measurement (2)
A distance estimate is obtained from hop count and average per hop distance
= estimated Euclidian distance (Kleinrock-Silvester formula)

33. Step 2: Multilateration (1)
How to find self position knowing the distances from three anchors?

34. Step 2: Multilateration (2)
Ideally the desired coordinates are located at the point of intersection of three circles
How to solve especially with noisy measurements?
Gradient Descent Iterative Solution
True location
Initial estimation

35. Multilateration (3) Gradient descent iterative solution
Start with initial guess
Compute the distances based on
Define a measure of error
Update based on error gradient

36. Gradient Algorithm (3)
With distances from three anchors a node can find its own location by iteratively solving the equations (multilateration with gradient descent)

37. Anchor based localization
Centroid Algorithm
APIT
SeRLoc
Gradient algorithm
APS – Ad-Hoc Positioning System

38. APS Algorithm (1) Ad-Hoc Positioning System
Similar to Gradient algorithm
Difference lies in estimating one-hop Euclidean distance
In APS, an anchor node Ai uses correction factor to estimate one-hop Euclidean distance

39. APS Algorithm (2) Example of correction factor 30 + 60 = 12.9m 3 + 4

40. APS Algorithm (3)
With correction factor, each node estimates the distance
Use least square to solve the multilateration equations

41. If you decide to finish a project on localization
What are the key issues you need to consider before hand?

42. Discussion: Issues to consider
Cost (extra HW)
Degree of accuracy needed
Indoors/outdoors
Line of sight or not
2D-3D
Efficiency (Energy budget) (Number of messages)
Clock synchronization accuracy
Error Assumptions
Security attacks

43. Summary Taxonomy: Range-based and Range-free
Range-free solutions localize sensor nodes without expensive localization hardware.
Proximity-based Solution: Centroid
Area-based Solutions: APIT and SerLoc
Hop-count-based solutions: Gradient and APS

44. MDS Algorithm (1) Multidimensional Scaling (MDS)
Conventional data analysis technique
Idea: To model data in 2-D or 3-D based on similarity or dissimilarity (scaling)
For localization
distance matrix coordinate matrix 1 2 3 1 1 2 2 3 3

45. MDS Algorithm (2)
How to get C from D ?
Example

46. MDS Algorithm (2) Double centering (subtracting column and row mean)
Compute the singular value decomposition (SVD) of
Keeping the largest singular values