1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors
Andreas Savvides, Athanassios Boulis
and Mani B. Srivastava
Networked and Embedded Systems Lab
University of California, Los Angeles
Presented by Yong Chen
Department of Computer Science
University of Virginia

2. Contribution Overview
+ A good idea to compute the location according to beacon location.
+ Algorithm to decide the nodes to participate the collaborative multilateration
- No distributed implementation details for iterative & collaborative multilateration
- Algorithms and solutions are not robust

3. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

4. Introduction What is Localization
A mechanism for discovering spatial relationships among objects

5. Introduction here, It is Location discovery for nodes
Given a network of sensor nodes where a few nodes know their location how do we calculate the location of the nodes?
Known Location
Unknown Location

6. Introduction Why need this kind of localization? Motivation
Support Location Aware Applications
Track Objects
Report event origins
Evaluate network coverage
Assist with routing, GF
Support for upper level protocols.
GPS is not practical
Not work Indoors or if blocked from the GPS satellites
Spends the battery life of the node
Issue of the production cost factor of GPS
Increase the size of sensor nodes

7. Introduction Two phases
Location discovery approaches consist of two phases : Ranging phase, Estimation phase
Ranging phase (distance estimation)
Each node estimate its distance from its neighbors
Estimation phase (distance combining)
Nodes use ranging information and beacon node locations to estimate their positions

8. Introduction phases 1: Ranging phase
Distance measuring methods
Signal Strength
Uses RSSI readings
Time based methods
ToA, TDoA
Used with radio, acoustic, ultrasound
Angle of Arrival (AoA)
Measured with directive antennas or arrays

9. Introduction phases 2: Estimation phase
Hyperbolic Trilateration
Triangulation
Multi-lateration
Considers all available beacons A B C a b c
Sines Rule
Cosines Rule

10. Introduction Related work
Outdoor
Automatic Vehicle Location (AVL)
Determine the position of police cars
Use ToA, Multi-lateration
Global Positioning System (GPS) & LORAN
GPS:24 NAVSTAR satellites
LORAN: ground based beacons instead of satellites
Time-of-flight, trilateration
Mobile phone position
Cellular base station transmits beacons
Use TDoA, Multi-lateration

11. Introduction Related work
Indoor
RADAR system
Track the location of users within a building
RF strength measurements from three fixed base stations
Build a set of signal strength maps
Mathing the online readings from the maps
Cricket location support system
Use Ultrasound from fixed beacons
Multi-lateration
The Bat system
Node carries an ultrasound transmitter

12. Introduction Ranging characterization
Received Signal Strength
RF signal attenuation is a function of distance
Inconsistent Model because of environment fading and shadowing effects and the altitude of the radio antenna
A Model is derived by obtaining a least square fit for each power level

13. Introduction Ranging ToA using RF and Ultrasound
The time difference between RF and ultrasound
To estimate the speed to sound, perform a best line fit

14. Introduction Discussion
Does ToA suffer from the environment changes?
Obstacles, interference to ToA?
Extra work to identify the pairs of Radio Signal and Ultrasound pulse.
Constraints: Ultrasound range on the Medusa nodes used is about 3 meters (11-12 feet), the ultra-range of second generation of Medusa is about meters, far less than the communication radius (30-100m)
Any other comments?

15. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

16. AHLoS Ad-Hoc Localization Systeme
Ranging phase (distance estimation)
ToA
Estimation phase (distance combining)
Multilateration

17. AHLoS Overview Some percentage of nodes knows their positions
Beacon nodes
Nodes with known positions
Broadcast their locations to their neighbors
Unknown nodes
Nodes with unknown positions
Use ranging information and beacon node locations to estimate their positions
Once knows its location, becomes a beacon node
Atomic, Iterative, and Collaborative Multilateration

18. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

19. AHLoS Atomic Multilateration
Requirement
Atomic multilateration can take place if the unknown node is within one hop distance from at least three beacon nodes. The node may also estimate the ultrasound propagation speed if four or more beacons are available
Topology for atomic multilateration 1
d1x X X’
d2x 2
d3x 3
Three lined beacons,
location X is not unique 1
d1x X X’
d2x 2
Two beacons,
location X is not unique 1 X
d1x
d2x 2 3
d3x
Three beacons,
location X is unique 1
d1x X X’
One beacon,
location X is not unique

20. AHLoS Atomic Multilateration
What we know:
1. The location of Three or more beacons N1,N2,N3, … …
2. Ti0, the time from beacon Ni to unknown node 0 for ultrasound propagation
What we want to get:
The location of the unknown node 0
How to get the location:
Make the difference between the measured distance and estimated Euclidean distance to be as small as possible.
Method used:
The minimum mean square estimate (MMSE), let F to be as small as possible
(Equation 3)
(Equation 4)

21. AHLoS Incorrectness 1 in Atomic Multilateration
The goal is let F(X0,Y0,S) in equation 4 to be as small as possible
(Equation 4)
We should have
(Equation 40)
Here, equation 5 is generated by setting = 0
So it has
(Equation 5)
If equations 5 have solutions, they are solutions to equation 4. BUT equations 5 may not have solutions, because Ti0 is a measured value, equations 5 can not be guaranteed to have solutions on the measured values Ti0.

22. AHLoS Incorrectness 2 in Atomic Multilateration
Look at the solution of the system of equations
(Equation A)
(Equation B)
How to get it?
In the process, one important assumption is If ,
doesn’t exist.
We can not use the method

23. AHLoS Incorrectness 3 in Atomic Multilateration
3 beacons are not enough to get a unique solution with unknown speed s.
In the left figure, d1x, d2x, d3x are distance
But in the equations, distance is unknown,
Another variable is introduced, the ultrasound
Propagation speed s.
There are only 3 equations with x, y square factors and unknown s.
3 beacons are not enough to get a unique location solution with unknown speed s. 1
d1x
d3x X 3
d2x 2

24. AHLoS Atomic Multilateration Example 1
Conditions:
Three beacons N1(0,1),N2(0,-1),N3(2,0)
One unknown node N0
The time of the ultrasound propagation:
From N1 to N0, it is sqrt(2) s
From N2 to N0, it is sqrt(2) s
From N3 to N0, it is 1 s
Test:
Using the algorithm on the paper to see if we can get the coordinates of N0 or some other interesting results.
N1(0,1) 1
N3(2,0)
N0(1,0)
N2(0,-1)

25. AHLoS Atomic Multilateration Example 1
From equation above, we have
N1(0,1)
Equation N1
Equation N2 1
N3(2,0)
Equation N3
N0(1,0)
N1 – N3 and N2 – N3 , we have
N2(0,-1)

26. AHLoS Atomic Multilateration Example 1
We can not directly use the solution provided by the paper.

27. AHLoS Atomic Multilateration Example 1
From equations above, we have
Equation e1
N1(0,1)
Equation e2
Equation e3 1
N3(2,0)
Eliminating
Equation e4
N0(1,0)
Equation e5
Equation e6
From Equation e4,e5, we have
N2(0,-1)
From Equation e5,e6, we have
Equation e7
From Equation e3,e5,e6 we have
Equation e8

28. AHLoS Atomic Multilateration Example 1
Equation e6
N1(0,1)
Equation e7
Equation e8 1
N3(2,0)
From Equation e6,e7,e8, we have
2 sets of results
N0(1,0)
N2(0,-1) OR

29. AHLoS Atomic Multilateration Example 1
Taking the algorithm on the paper
3 beacons are not enough to get a unique solution with unknown speed s.
N1(0,1) 1
N3(2,0)
N0’(7,0)
N0(1,0)
N2(0,-1) OR

30. AHLoS Atomic Multilateration Example 2
Conditions:
Three beacons N1(0,1),N2(0,-1),N3(2,0)
One unknown node N0
The time of the ultrasound propagation:
From N1 to N0, it is sqrt(2) ms
From N2 to N0, it is sqrt(2) ms
From N3 to N0, it is 1 ms
Test:
Using the standard MMSE method to see if we can get the coordinates of N0 or some other interesting results.
N1(0,1) 1
N3(2,0)
N0(1,0)
N2(0,-1)

31. AHLoS Atomic Multilateration Example 21
N3(2,0)
N0(1,0)
N2(0,-1)
Taking the algorithm on MMSE
3 beacons are not enough to get a unique solution with unknown speed s.
select

32. AHLoS Conclusion in Atomic Multilateration
With the unknown speed of ultrasound pulse or other efficient constraints, generally, it is impossible to get a unique location of one unknown node only depending 3 un-lined beacons
Other constraints, such as a roughly scope of ultrasound speed, angle, etc, must be added to make the solution determined. Or 4 un-lines beacons determine one unknown node’s location
The computation process on the paper is not robust.
In the algorithms later, we assume the speed of ultrasound is known

33. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

34. AHLoS Iterative Multilateration
A central version:
Each node send its neighboring / ranging information with
The neighbors to one central node.

35. AHLoS Iterative Multilateration
A distributed version:
For unknown node, when it receives one beacon packet,the event will be triggered.
Once three unlined beacons are available, begin to compute location.
sate = UNKNOWN;
numberOfBeaconPacketReceived=0;
event result_t onBeaconPacketReceive(TOS_MsgPtr msg) {
if ( state == BEACON ) return TRUE;
numberOfBeaconPacketReceived++;
processPacket(msg);
if (numberOfBeaconPacketReceived >= 3 & unlinedbeacons())
{ computeLocation();
state = BEACON;
call broadcastBeaconPacket(); }

36. AHLoS Iterative Multilateration
It shows node positions are within 20 cm from the actual positions.
What is the behind:
1.How many steps are there for accumulated error?
2.How beacons are deployed?
3.Small scale

37. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

38. AHLoS Collaborative Multilateration
One node estimates its position by considering use of location information over multiple hops
How it works
For one node, to decide which nodes should be in its participating node set S
For node , i is connected to u, and node u is an unknown node, the goal function is the same as that of the atomic multi-lateration, to minimize the

39. AHLoS Collaborative Multilateration
Comments
Definition 1
A node is a participating node if it is either a beacon or if it is an unknown node with at least three participating neighbors
Definition 2
A participating node pair is a beacon-unknown or unknown-unknown pair of connected nodes where all unknowns are participating

40. AHLoS Collaborative Multilateration
1).For the left graph, how does node 2 know node 4 is a participating node? And vice versa.
2).For the right graph, node 2 and node 4 can decide if they should attend the col-multilateration? If so, can they decide the locations uniquely? 1 4 2 3
Node 2,4 are symmetric along line 1-3

41. AHLoS Collaborative Multilateration
Collaborative multilateration eligibility
A central controller will execute the function on the upper right corner ( the algorithm in figure 10 of the paper): 5 1 1 2 4 2 4 3 3 6

42. AHLoS Collaborative Multilateration
A central node:
Call isCollaborative(2,-1,true)
A central node:
Call isCollaborative(2,-1,true) 5 1 1 2 4 2 4 3 3 6

43. AHLoS Collaborative Multilateration
A central node:
Call isCollaborative(2,-1,true)
limit = 3
count = beaconCount(2) = 2
A central node:
Call isCollaborative(2,-1,true)
limit = 3
count = beaconCount(2) = 2 5 1 1 2 4 2 4 3 3 6

44. AHLoS Collaborative Multilateration
A central node:
Call isCollaborative(2,-1,true)
limit = 3
count = beaconCount(2) = 2
For unknown node 4
call isColl(4,2,false)
A central node:
Call isCollaborative(2,-1,true)
limit = 3
count = beaconCount(2) = 2
For unknown node 4
call isColl(4,2,false) 5 1 1 2 4 2 4 3 3 6

45. AHLoS Collaborative Multilateration
A central node:
Call isCollaborative(2,-1,true){
limit = 3
count = beaconCount(2) = 2
For unknown node 4
call isColl(4,2,false){
limit = 2
return true }
A central node:
Call isCollaborative(2,-1,true)
limit = 3
count = beaconCount(2) = 2
For unknown node 4
call isColl(4,2,false) ){
limit = 2
return true } 5 1 1 2 4 2 4 3 3 6

46. AHLoS Collaborative Multilateration
A central node:
Call isCollaborative(2,-1,true){
limit = 3
count = beaconCount(2) = 2
For unknown node 4
call isColl(4,2,false){
limit = 2
return true }
count++ // 3
count == limit, return true}
A central node:
Call isCollaborative(2,-1,true)
limit = 3
count = beaconCount(2) = 2
For unknown node 4
call isColl(4,2,false) ){
limit = 2
return true }
count++ // 3
count == limit, return true} 5 1 1 2 4 2 4 3 3 6

47. AHLoS Collaborative Multilateration
A central node:
isCollaborative(2,-1,true) == true
We have five participating node pairs{1,2},{2,3},{2,4},{4,5},{4,6}
A central node:
isCollaborative(2,-1,true) == true
We have five participating node pairs {1,2},{2,3},{2,4},{4,1},{4,3}
Collaborative multilateration eligibility for node 2 is finished.
NEXT: Begin to compute the locations of the unknown nodes 5 1 1 2 4 2 4 3 3 6

48. AHLoS Collaborative Multilateration
Assuming: ultrasound speed s == 1
For five participating node pairs {i,u}:{1,2},{3,2},{2,4},{5,4},{6,4}
For five participating node pairs {i,u}:{1,2},{3,2},{2,4},{1,4},{3,4}
1(0,2)
5(4,2)
1(3,2)
2(2,0)
4(4,0)
2(2,0)
4(4,0)
3(1,-1)
6(5,-1)
3(3,-1)

49. AHLoS Collaborative Multilateration
For five participating node pairs {i,u}:{1,2},{3,2},{2,4},{5,4},{6,4}
For five participating node pairs {i,u}:{1,2},{3,2},{2,4},{1,4},{3,4}
Locations of Node 2,4 can’t be determined
1(0,2)
5(4,2)
1(3,2)
2(2,0)
4(4,0)
2(2,0)
4(4,0)
3(1,-1)
6(5,-1)
3(3,-1)

50. AHLoS Collaborative Multilateration
An efficient distributed version is hard to be achieved.
Large packet exchange or RPC-like procedure call is unavoidable.
Large computation cost: matrix computation.
Request: Node 4, execute isCollaborative(4,2,false)
Or node 4, send me your neighbor information and distances 1 5 2 4 6 3
Answer: Node 2, isCollaborative(4,2,false) return true.
OR, node 2, here is my neighbor and distance LIST.

51. AHLoS Collaborative Multilateration
A efficient distributed version is hard to be achieved.
Who will trigger the call firstly? Synchronization is needed.
Request
Request
Request 1 C 2 A 6 8 10 12 4 3 B 7 9 11 13 5

52. AHLoS Conclusion of Collaborative Multilateration
The central version is not robust
Efficient distributed version is hard to get in the current frame.
High communication
High computation
synchronization
How the distributed version is implemented by the paper?
Comments?

53. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

54. Performance evaluation
What kind of performance evaluation do we need for the localization? What do we care most about the localization?
Accuracy
Scalability
Cost ……

55. Performance evaluation Accuracy
Only Iterative Multilateration is included as we talked earlier.
What is the behind:
1.How many steps are there for accumulated error?
2.How beacons are deployed?
3.Small scale

56. Performance evaluation Scalability
300 nodes
200 nodes
A sensor field of 100 by 100, sensor range of 10
Distributed algorithm?
How beacons are deployed?

57. Performance evaluation cost
117 nodes/10,000m2 Uniformly distributed, Range = 10

58. Performance evaluation cost

59. Performance evaluation cost
Is it necessary to spend three pages to compare the distributed algorithm and central algorithm for a sensor network localization problem?
Simulation tool?

60. Outline Introduction AHLoS Performance evaluation Conclusion
Ad-Hoc Localization Systeme and overview
Atomic Multilateration
Iterative Multilateration
Collaborative Multilateration
Performance evaluation
Conclusion

61. Conclusion
A good idea to compute the location according to beacon location.
Errors in Atomic Multilateration
Errors in Collaborative Multilateration
Insufficient Performance evaluation
No implementation details to the difficulties on distributed Collaborative Multilateration
Other little misses
Equation 6

62. References
Papers: 1.”Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors” 2.”Distributed Fine-Grained Localization in Ad-Hoc networks” 3.”Localization in Ad-Hoc Sensor Networks” Slides: 1.”Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors” presented by Kisuk Kweon 2.”LOCALIZATION” presented by Lewis Girod 3.”Survey of Estimation of Location in Sensor Networks” Presented by Wei-Peng Chen 4.”Dynamic Location Discovery in Ad-Hoc Networks” presented byAndreas Savvides, Boulis and Mani B. Srivastava 5.”Distributed localization in wireless ad-hoc sensor network” presented by Vaidyanathan Ramadurai

63. Comments & Questions… Thanks