Transcript Dynamic Fine-Grained Localization in Ad-Hoc - NeSL

Dynamic Fine-Grained
Localization in Ad-Hoc
Networks of Sensors
A. Savvides, C. C. Han, M. B. Srivastava
Networked and Embedded Systems Lab
University of California, Los Angeles
{asavvide, simonhan, mbs}@ee.ucla.edu
Localization in Sensor
Networks
• Context awareness in applications
• Network coverage analysis
• Report origins of events
– Temperature at a specific part of the room
– Locate/track objects, people, robots
– Assist with routing
• Why not GPS?
– Costly, power hungry, requires line-of-sight,
large form factor, accuracy
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Problem Statement
• Estimate node locations in an adhoc network of nodes
Iterative Multilateration
– Uniformly deployed nodes on a flat
plane
• Ad-Hoc Localization
System(AHLoS)
– Every node contributes to process
– Small fraction of nodes (beacons)
Collaborative Multilateration
are initially aware of their locations
– Distributed
• Robust to surrounding environment
changes and node failures
• Energy Efficient
• Scalable
– Inter-node ranging uses(RSSI,
ultrasound)
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Ranging
• Localization relies on the ability of nodes
to measure distances
Measurement 1
Measurement 2
Multilateration
or other
Position
Estimate
Measurement n
• Physical layer effects may bias ranging =>
empirical study
– RF Received Signal Strength Indicator (RSSI)
– RF + Ultrasound Time-of-Arrival(ToA)
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Target Platforms
Rockwell WINS Node
(RSSI)
• 200MHz StrongARM
• DECT Radio from Connexant
Medusa Experimental Node
(ToA)
• Atmel AVR 8535 MCU
• RFM Radio
• 40KHz Ultrasound 5
Platform Characterization
Ultrasound ToA
Max range 3m, accuracy 2cm
RSSI in football field
Max range 20m, accuracy 7m
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Localization Algorithms
• Atomic Multilateration (base case)
– Solution similar to GPS
– Formulated as a least squares problem
– Requires 3 beacons (if more than 3 beacons are
available, the ultrasound propagation speed is
also estimated)
– May not work if beacons
are badly aligned
Beacon
Unknown
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Atomic Multilateration
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Minimize over all
1
f ( xi , x0 , s)  sti 0  ( xi  x0 ) 2  ( yi  y0 ) 2
This can be linearized to the form
where
  x12  y12  xk2  yk2 

2
2
2
2 

x

y

x

y
2
2
k
k 
y



 2
2
2
2
 xk 1  yk 1  xk  yk 
MMSE Solution:
y  Xb
0
i  1,2k  1
4
3
 2( xk  x1 )
2( yk  y1 )
t k20  t102 


2
2
2
(
x

x
)
2
(
y

y
)
t

t
k
2
k
2
k0
20 
b
X 






2
2
2
(
x

x
)
2
(
y

y
)
t

t
 k
k 1
k
k 1
k0
( k 1) 0 

b  ( X T X ) 1 X T y
 x0 
y 
 0
 s 2 
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Iterative Multilateration
• Each node that calculates its location it becomes a
beacon that can help other nodes to calculate
their locations
• Allows Distributed Operation
• Problem:
– Error accumulation
– Reasonable results can be achieved for
small networks since ultrasonic distance
measurement is accurate
• Error accumulation can be limited using weights
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Iterative Multilateration
• Each node that calculates its location it becomes a
beacon that can help other nodes to calculate
their locations
• Allows Distributed Operation
• Problem:
– Error accumulation
– Reasonable results can be achieved for
small networks since ultrasonic distance
measurement is accurate
• Error accumulation can be limited using weights
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Iterative Multilateration
• Each node that calculates its location it becomes a
beacon that can help other nodes to calculate
their locations
• Allows Distributed Operation
• Problem:
– Error accumulation
– Reasonable results can be achieved for
small networks since ultrasonic distance
measurement is accurate
• Error accumulation can be limited using weights
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Iterative Multilateration
Accuracy
Ranging + Beacon Error
49
47
44
42
40
38
36
34
31
28
26
24
21
20
18
16
13
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
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Error Distance (m)
Ranging Error
Node Id
50 Nodes, 20x20 room, range=3m, 10% beacons 20mm white gaussian ranging error
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Collaborative Multilateration
• Considers location information over multiple hops
• More than one unknown node positions are estimated
simultaneously
• Set of nodes considered MUST have a unique solution
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Collaborative Multilateration
Results
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Node and Beacon Placement
• Nodes are assumed to have a uniform distribution
• The success of the iterative multilateration
process depends on node connectivity and beacon
availability
Node range = 10m
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Node vs. Initial Beacon
Densities
% Resolved Nodes
Total Nodes
% Initial Beacons
Uniformly distributed deployment in a field 100x100. Node range = 10
Results include only iterative multilateration
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Experimental Setup
• Initially Simulated in
ns-2 on top of DSDV
• Testbed Implementation
• Ultrasound transmitted
simultaneously with RF
• Distributed Computation
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Centralized or Distributed?
• Where should the computation for
location estimation take place?
– At a central node?
– Inside the network?
• How does this decision facilitate
– Scalability
– Robustness
– Energy efficiency
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Centralized vs. Distributed
Localization
Distributed Pros
•
•
•
More robust to node
failure
Less traffic => less power
Better handling of local
environment variations
–
–
•
•
Speed of ultrasound
Radio path loss
Rapid updates upon
topology changes
No time synch. required
Centralized Cons
• A route to a central point
• Time synchronization
• High latencies for location
updates
• Central node requires
preplanning
• More traffic => higher
power consumption
Centralized Pros
• Can solve more accurately
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Energy Characterization
• Ultrasound penalty is the same for both
cases so we did not characterize it
• Measured AVR MCU and RFM radio
AVR
Mode
Current
Power
Active
2.9mA
8.7mW
Sleep
1.9mA
5.9mW
Power
Down
1μA
3μW
• Total Power - 20mW
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Localization Energy Cost
Distributed
Centralized
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Energy per node (J)
7
6
5
4
3
2
1
0
100
200
300
400
500
600
700
Netw ork Size
Node range 10m, 20% beacons
Central node at the center of the network
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Related Work
• Centralized
– RADAR [Bahl et. al]
– Active BAT [Harter et. al]
• Proximity
– Cricket System [Priyantha et. al]
• Ad-Hoc Distributed Proximity
– GPS Less Localization [Bulusu et. al]
• Ad-Hoc Centralized
– Convex Optimization Methods [Doherty et. al]
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Conclusions and Future Work
• Initial results are encouraging (20 cm accuracy)
• A distributed implementation is desirable
• This is only the beginning!
– Medusa II Node under development
• 20 meter ultrasound range
• More computation power
– New 3D test bed
• Collaborative Multilateration is promising and
should be further explored
• Many new applications are emerging!
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AHLoS Website
http://nesl.ee.ucla.edu/projects/ahlos
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