1. Distributed localization in wireless sensor networks
The embedded Ubicom system
30 may 2002
Distributed localization in wireless sensor networks
Koen Langendoen
Niels Reijers
Delft University of Technology
The Netherlands

2. The embedded Ubicom system
30 may 2002
Technology trend
Small integrated devices
Smaller, cheaper, more powerful
PDAs, mobile phones
Many opportunities, and research areas
Power management
Distributed algorithms

3. Wireless sensor networks
The embedded Ubicom system
30 may 2002
Wireless sensor networks
Wireless sensor node
power supply
sensors
embedded processor
wireless link
Many, cheap sensors
wireless  easy to install
intelligent  collaboration
low-power  long lifetime

4. Possible applications
The embedded Ubicom system
30 may 2002
Possible applications
Fire rescue
breadcrumbs
exit path
hazard detection
Environmental monitoring
detecting forest fires
Monitoring bulk goods (potatoes)
mix sensors with goods
temperature, humidity

5. Required technologies
The embedded Ubicom system
30 may 2002
Required technologies
Efficient data routing
ad-hoc network
one or more ‘datasinks’
In-network data processing
large amounts of raw data
limited power and bandwidth
Node localization

6. The embedded Ubicom system
30 may 2002
Ad-hoc localization
Many nodes (> 100)
NO infrastructure
NO central processing
Sparse anchor nodes
known position
Other nodes determine position using this data
Distance measurement

7. The embedded Ubicom system
30 may 2002
Ad-hoc localization
2D, static node positions
Several different algorithms
have been proposed
3 will be compared
Simulations on
DAS2 supercomputer

8. The embedded Ubicom system
30 may 2002
Main result
no ‘one size fits all’
Best algorithm depends on:
error in range measurement (range variance)
connectivity (number of neighbours)
network topology
node capabilities
application requirements

9. The embedded Ubicom system
30 may 2002
Three-phase approach
Determine distance to anchor nodes
(communication)
Establish position estimates
(computation)
Iteratively refine positions using additional range measurements
(both)

10. Phase 1: Distance to anchor
The embedded Ubicom system
30 may 2002
Phase 1: Distance to anchor
Three algorithms
Sum-dist [Savvides et al.]
DV-Hop [Niculescu et al., Savarese et al.]
Euclidean [Niculescu et al.]
anchors flood network
with their known position

11. The embedded Ubicom system
30 may 2002
Phase 1: Sum-dist
Anchors
flood network with known position
Nodes
add hop distances
require range measurement B
B: 6+4 = 10 6 4
C: = 15 6 5 C
A: 5 5 A

12. The embedded Ubicom system
30 may 2002
Phase 1: DV-hop
Anchors
flood network with known position
flood network with avg hop distance
Nodes
count #hops to anchors
multiply with avg hop distance
A-B: 12
3 hops B
avg hop: 4 3 2 4 1 C A

13. The embedded Ubicom system
30 may 2002
Phase 1: Euclidean
Anchors
flood network with known position
Nodes
determine distance by
range measurement
geometric calculation
require range measurement B C A

14. The embedded Ubicom system
30 may 2002
Phase 1: Euclidean (2)
Wanted:
Distance A-G D G E
Using AEGF:
A-G = 8
...or 3 F
Using AEGD:
A-G = 8
...or 0.5 A
A-G = 8

15. The embedded Ubicom system
30 may 2002
Phase 1: Euclidean (3)
Needs high connectivity
Error prone (selecting wrong distance)
Perfect accuracy possible B D G E C F A

16. The embedded Ubicom system
30 may 2002
Phase 1: Comparison
Range
measurement
Very accurate: Euclidean
Reasonable: Sum-dist
None / very bad: DV-hop

17. Phase 2: Determining position
The embedded Ubicom system
30 may 2002
Phase 2: Determining position
Two algorithms:
Lateration
very common
local triangulation
solve [Ax=b]
Min-max [Savvides et al.] B C A

18. The embedded Ubicom system
30 may 2002
Phase 2: Min-max
Using range to anchors to determine a bounding box
Use center of box as
position estimate B C A

19. Comparison: distance error
The embedded Ubicom system
30 may 2002
Comparison: distance error

20. Comparison: distance bias
The embedded Ubicom system
30 may 2002
Comparison: distance bias

21. The embedded Ubicom system
30 may 2002
A problem with Min-max
Very sensitive to anchor placement

22. The embedded Ubicom system
30 may 2002
Phase combined

23. The embedded Ubicom system
30 may 2002
Phase combined
Euclidean: very sensitive to both range variance and connectivity

24. The embedded Ubicom system
30 may 2002
Error and coverage
There is a tradeoff between coverage and error

25. The embedded Ubicom system
30 may 2002
Matrix
Radio range (connectivity)
16 (15.3)
14 (12.0)
12 (8.8)
10 (6.4)
8 (4.1)
Range variance
Eucl
Lat
Sum-d MM
DV-hop
0.025
0.05
0.1
0.25
0.5
Radio range (connectivity)
16 (15.3)
14 (12.0)
12 (8.8)
10 (6.4)
8 (4.1)
Range variance
Eucl
Lat
Sum-d MM
DV-hop
0.025
0.05
0.1
0.25
0.5
Radio range (connectivity)
16 (15.3)
14 (12.0)
12 (8.8)
10 (6.4)
8 (4.1)
Range variance
Eucl
Lat
Sum-d MM
DV-hop
0.025
0.05
0.1
0.25
0.5
Radio range (connectivity)
16 (15.3)
14 (12.0)
12 (8.8)
10 (6.4)
8 (4.1)
Range variance
Eucl
Lat
Sum-d MM
DV-hop
0.025
0.05
0.1
0.25
0.5
Radio range (connectivity)
16 (15.3)
14 (12.0)
12 (8.8)
10 (6.4)
8 (4.1)
Range variance
Eucl
Lat
Sum-d MM
DV-hop
0.025
0.05
0.1
0.25
0.5
Radio range (connectivity)
16 (15.3)
14 (12.0)
12 (8.8)
10 (6.4)
8 (4.1)
Range variance
Eucl
Lat
Sum-d MM
DV-hop
0.025
0.05
0.1
0.25
0.5

26. The embedded Ubicom system
30 may 2002
Phases 1 and 2
Position error usually 30% of the radio range or higher
Range measurements between nodes only used to determine anchor distance
Can we do better?

27. Phase 3: Iterative refinement
The embedded Ubicom system
30 may 2002
Phase 3: Iterative refinement
obtain initial position (phases 1 and 2)
broadcast my position
iteratively refine position using:
ranges to direct neighbours
their initial positions

28. Phase 3: Iterative refinement
The embedded Ubicom system
30 may 2002
Phase 3: Iterative refinement
Initial estimate
Receive neighbour positions
Broadcast new position
Local lateration A

29. The embedded Ubicom system
30 may 2002
Phase 3: Position error

30. The embedded Ubicom system
30 may 2002
Phase 3: Coverage

31. The embedded Ubicom system
30 may 2002
Conclusion
No ‘one size fits all’
Refinement needs better coverage to be useful
Lots of room for improvement in all phases
Details in Tech Report PDS (

32. The embedded Ubicom system
30 may 2002
What is wrong?
Bad topology
identical hop-TERRAIN positions
twins
Error propagation
rapid infection of complete network
– hop – triangulate – hop – triangulate –

33. The embedded Ubicom system
30 may 2002
Confidence weights
Weight input for triangulation (wAx = wb)
Initialization
anchors 1.0
twins, identical hops 0
others 0.1
Triangulation
large residue 0
small residue avg of input confidences