Dynamic FineGrained Localization in AdHoc Networks of Sensors

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
• 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?

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

10
Introduction Related work

• Outdoor
• Automatic Vehicle Location (AVL)
• Determine the position of police cars
• Use ToA, Multi-lateration
• Global Positioning System (GPS) LORAN
• GPS24 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
• Multi-lateration

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 10-15 meters, far less than the
  communication radius (30-100m)
• Any other comments?

15
Outline

• Introduction
• AHLoS
• 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
• 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

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
doesnt 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
EXAMPLE
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)
N3(2,0)
1
N0(1,0)
N2(0,-1)
25
AHLoS Atomic Multilateration Example 1
EXAMPLE
From equation above, we have
N1(0,1)
Equation N1
Equation N2
N3(2,0)
1
Equation N3
N0(1,0)
N1 N3 and N2 N3 , we have
N2(0,-1)
26
AHLoS Atomic Multilateration Example 1
EXAMPLE
N1(0,1)
N3(2,0)
1
N0(1,0)
N2(0,-1)
We can not directly use the solution provided by
the paper.
27
AHLoS Atomic Multilateration Example 1
EXAMPLE
From equations above, we have
Equation e1
N1(0,1)
Equation e2
Equation e3
N3(2,0)
1
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
EXAMPLE
Equation e6
N1(0,1)
Equation e7
Equation e8
N3(2,0)
1
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)
N3(2,0)
1
N0(7,0)
N0(1,0)
N2(0,-1)
OR
30
AHLoS Atomic Multilateration Example 2
EXAMPLE
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)
N3(2,0)
1
N0(1,0)
N2(0,-1)
31
AHLoS Atomic Multilateration Example 2
EXAMPLE
N1(0,1)
N3(2,0)
1
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 nodes 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
• 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 numberOfBeaconPacketReceived0
event result_t onBeaconPacketReceive(TOS_MsgPtr
msg) if ( state BEACON ) return
TRUE numberOfBeaconPacketReceived processP
acket(msg) if (numberOfBeaconPacketReceived gt
3 unlinedbeacons()) computeLocation() st
ate 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
• 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 count beaconCount(2) 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 count beaconCount(2) 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 count beaconCount(2) 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 count beaconCount(2) 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
pairs1,2,2,3,2,4,4,5,4,6
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,u1,2,3,2,2,4,5,4,6,4
For five participating node pairs
i,u1,2,3,2,2,4,1,4,3,4
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,u1,2,3,2,2,4,5,4,6,4
For five participating node pairs
i,u1,2,3,2,2,4,1,4,3,4
Locations of Node 2,4 cant 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
• 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
• 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
Papers1.Dynamic Fine-Grained Localization in
Ad-Hoc Networks of Sensors2.Distributed
Fine-Grained Localization in Ad-Hoc
networks3.Localization in Ad-Hoc Sensor
Networks Slides1.Dynamic Fine-Grained
Localization in Ad-Hoc Networks of
Sensors presented by Kisuk Kweon
2.LOCALIZATION presented by Lewis
Girod3.Survey of Estimation of Location in
Sensor Networks Presented by Wei-Peng
Chen4.Dynamic Location Discovery in Ad-Hoc
Networks presented byAndreas Savvides, Boulis
and Mani B. Srivastava5.Distributed
localization in wireless ad-hoc sensor
network presented by Vaidyanathan Ramadurai
References
63
Comments QuestionsThanks