Transcript Localization

Node Localization in Wireless
Sensor Networks
Radu Stoleru
Computer Science
University of Virginia
Motivation
Sensor
Radio link
Sensor
Sensor
Sensor
Credits: OTB News
2
Motivation
Credits: Matt Welsh (Harvard)
3
Motivation
Credits: www.visitingdc.com
4
Enabling Technology?
Processor
Mica Dot
Mica2
RAM
ETag
ATmega128, 8-bit,
Telos
16MHz
4KB
Program Memory
128KB
External Flash Memory
512KB
Radio Transceiver
(60kbps, 250kbps)
Lifetime (2AA, no dutycycle)
Mica Mote
Operating Environment
Untethered, Harsh
Pluto
~6 days
EmberNet
DSYS25
BT Node
XYZ
5
Existing WSN Applications
Military (VigilNet, Virginia)
Volcano Monitoring (Harvard)
Structural Monitoring (Berkeley)
Credits: OTB News, www.visitingdc.com. Matt Welsh (Harward)
6
How to Localize Nodes?
► Why
node localization is a Hard problem?
Outdoor GPS
Expensive,
Precise
Range-Based
absolute distance/angle estimates
WiFi Hotspots
Cheap,
Inaccurate
Range-Free
use only connectivity and proximity
7
Let’s Compare Solutions
Low
Cost
AdHoc
Long
Range
Low
Power
High
Accuracy
Fast
Ideal
+
+
+
+
+
+
Range Free
+
+
+/-
+
-
+
Ultrasound
-
+
-
-
+
+
WiFi
-
-
+/-
-
-
+
GPS
-
+
+
-
+
+
8
Contributions
Outline
► Node
Localization in WSN
 Event-Based Localization
► Asymmetric
Architecture
 Image-Based Passive Localization
► Asymmetric
Architecture
 Implementations, Real Evaluations
 Robust Localization Framework
 Proximity to Deployment Area,
Deployment Knowledge
► Integrated
WSN Systems
► Programming Abstractions
Spotlight
(SenSys ’05)
StarDust
(SenSys ’06)
They work too!
EmNetS ‘07
SECON ’04,
EmNetS ‘04
Mobisys ‘04, RTAS ‘05,
INFOCOM ‘06,
TOSN ‘06
ICDCS ‘04
9
Spotlight
10
Concept of Operation
(X1, Y1, R1) at T1
(X2, Y2, R2) at T2
(X2, Y2, R2)
(X1, Y1, R1)
11
Questions
► How
to efficiently distribute events in the
network? (trade-off: energy spent for event
creation vs. accuracy)
► How to deal with sensing channel noise?
► How to deal with unrecoverable errors in the
sensing channel? Can we do better than loosing
everything?
► What is the range of the system?
► How long does it take to localize a network?
12
System Architecture
Spotlight Device
Sensor Node i
e(t)
Event Distribution
Function E(t)
Event Detection
Function D(e)
Generates Events
Timestamp Events
Report Timestamps
Compute Location
Ti = { ti1 ti2 ….}
pi(x,y,z)
Localization
Function L(T)
Disseminate Location
Asymmetric Architecture
Event Distribution Function
(EDF): the core of Spotlight
We propose 3
Functions
13
Point Scan EDF
Light Spot
0
Sensor
s
L
E(t )  p | p [0, L]  p  t * s
LTi  ti1  E(ti1 )  ti1 * s
14
Line Scan EDF
E(t )  Ex (t ) Ey (t )
LTi  {ti1, ti 2}  E(ti1 ) E(ti 2 )
15
Area Cover EDF
Event
Detection
Errors?
0000
0001
0010
0011
0100
0101
0110
0111
1000
1001
1010
1011
1100
1101
1110
1111
E(t )  p | p  Sk  BIT(k , t )  1
LTi   p | p  COG(Sk )  ( BIT(k , t )  trueif t Ti ) 
( BIT(k , t )  false if t T  Ti )
16
Area Cover EDF
Event
Detection
Errors?
Use ECC, e.g.
Hamming(7,4)
dddpdpp
Does the code placement matter? What if we can not even
detect an error?
17
Area Cover EDF


min  d E (i, j ) 
 d H ( i , j ) K

Code placement strategies
d E (i, j )  Euclidian distancebetween i and j
d H (i, j )  Hammingdistancebetween i and j
Code placement, No ECC, 1-bit error
Code placement, with ECC, 3-bit error
18
Design Analysis
►
Execution Cost assuming:
 All nodes in a square area, with length D
 N events / unit time generated by the Spotlight device
 r is tolerable localization error
Criterion
Point
Scan
Line Scan
Area Cover
Localization Time
(D2/r2)/N
(2D/r)/N
logrD/N
# Detections
1
2
logrD
# Time Stamps
1
2
logrD
Event Overhead
D2
2D2
D2logrD/2
r2 N
DrN
Spotlight Power
D2 N / 2
19
Implementation
►
►
mSpotlight System
Spotlight Device:
 Projector
 Laptop
►
Mica2 motes
►
Short range (10-20m)
►
Versatile, it generates:
 Point Scan
 Line Scan
 Area Cover
20
Implementation
►
Spotlight System
►
Spotlight Device:
 Telescope Mount
 Diode Laser
 Laptop
►
XSM motes
►
Long range (>1000m)
►
It generates:
 Point Scan
 Line Scan
21
mSpotlight - Point Scan EDF
14
87cm/sec
Location error [cm]
12
58cm/sec
10
43cm/sec
8
35cm/sec
6
4
2
0
7.0
10.5
14.0
17.5
21.0
24.5
Event Size [cm]
Localization Duration [sec]
120
87cm/sec
100
58cm/sec
43cm/sec
80
35cm/sec
60
40
20
0
7.0
10.5
14.0
17.5
21.0
24.5
Event Size [cm]
22
mSpotlight - Area Cover EDF
3.5
Location error [cm]
3.0
20ms/event
40ms/event
80ms/event
100ms/event
60ms/event
2.5
2.0
1.5
1.0
0.5
0.0
6
8
10
12
Localization Duration [sec]
Number of Bits
10
9
8
7
6
5
4
3
2
1
0
20ms/event
40ms/event
80ms/event
100ms/event
6
60ms/event
8
10
12
Number of Bits
24
Spotlight - Point Scan EDF
Localization Error [cm]
25
0.7m/sec
20
1.4m/sec
3m/sec
15
10
5
0
6
12
18
Event Size [cm]
Localization
of an area
100x40 m2
Localization Time [min]
40
3m/sec
35
6m/sec
30
9m/sec
25
20
15
10
5
0
0
25
50
75
100
125
150
Event Size [cm]
25
Comparison
Low
Cost
AdHoc
Long
Range
Low
Power
High
Accuracy
Fast
Ideal
+
+
+
+
+
+
Spotlight
+
+
+
+
+
+/-
Range Free
+
+
+/-
+
-
+
Ultrasound
-
+
-
-
+
+
WiFi
-
-
+/-
-
-
+
GPS
-
+
+
-
+
+
26
StarDust
27
Corner-Cube Retroreflector
►Problem:
Sensor Nodes have limited power
supply. Hence, impossible to shine like a star.
28
Concept of Operation
ID ?
ID ?
ID ?
ID ?
ID ?
ID ?
ID ?
We need to match node IDs to locations!
29
System Architecture
Central Device
Node ID Matching
R
V’
V’
Sensor Node i
Image
Processing
Φ(Ψ(λ))
Ψ(λ)
Transfer Function
Φi(λ)
Light Emitter
Radio Model
G(Λ,E)
Asymmetric Architecture
1.
2.
3.
Take two pictures, one without light and one with light
Image Processing to identify the positions of the light-spots
Label relaxation algorithm to associate the positions with node
30
Image Processing
A football stadium where we deploy 6 sensor nodes
in a 3x2 grid. The distance between the lighting
device and the sensor nodes is approximately 500ft.
►
Deployment area
Without illumination
With illumination
31
Image Processing
Difference
Edge Detection
► Each pixel P is described by an RGB Value
The light reflected back by CCR has highest intensity.
► Edge Detection is applied to identify the location of
light spot
►
32
System Architecture
Central Device
Node ID Matching
V’
Sensor Node i
Image
Processing
Φ(Ψ(λ))
Transfer Function
Φi(λ)
R matching
ID
turnsΨ(λ)out to be a
V’
very difficult problem!
Light Emitter
Radio Model
V’
G’(V’,E’)
G(Λ,E)
Isomorphism
G(L,E)
33
Mapping: Positions ↔ Nodes
►Idea
I: Use constraints
 Color constraints
 Connectivity constraints
 Spatial Deployment constraints
 Temporal constraints
►Idea
II: Combine different constraints
►Idea
III: Label relaxation algorithm
34
Color Constraints
► Use color to map positions to nodes.
►
Two extreme Cases
 Each node has different CCR color (unique
mapping).
 Only one color available (no constraints)
35
Connectivity Constraints
► A pair of nodes must be located near each
?
other if they can communicate with each other.
36
Connectivity Constraints
Radio Model: N nodes in square of size L; k = #
unidirectional radio connections; R = radio range
L
R(k ) 
N
2


 k 
 k 
  0.66
  0.6
 0.051
 4N 
 4N 


60
Connected
N = 26
K = 180
Not Connected
40
R = 25ft
Count
L = 60ft
50
30
20
10
0
0
10
20
30
40
50
60
70
80
90
Distance [feet]
37
Spatial Deployment Constraints
► Drop sensor nodes at different
locations
 Wind affects the size of landing area
 x-y displacement follows Rayleigh
distribution
A
A
B
B
Landing
area
Unique Mapping
Probabilistic Mapping
38
Time Constraints
►Drop
sensor nodes at different times
►Two extreme cases
 Drop one by one. Take a picture after each drop.
 Drop all at the same time. No constraints.
Picture N
Picture N+1
Picture N+2
39
Mapping: Positions ↔ Nodes
►Idea
I: Use constraints
 Color constraints
 Connectivity constraints
 Spatial Deployment constraints
 Temporal constraints
►Idea
II: Combine different constraints
►Idea
III: Label relaxation algorithm
40
Hybrid constraints?
►
Color & Connectivity: Introduction of the
label relaxation algorithm
Pni (k )
0.25
ni
11
ni
nj
12
12
0.32
0.2
11
9
8
0
0.2
4
0.25
1
11
10
12
0.25
8
9
0.25
7
0.25
5
0.25
3
0.0
10
10
0.25
12
8
8
0.25
nj
7
0.2
0.68
4
5
0.2
0
1
3
0.2
Qnsi (k )
Fact: ni is connected with nj
Support
11
10
ni can not be 8 or 1
nj can not be 3
41
Label relaxation algorithm
Pni (k ) denotes the probability node n has label λ
i
k
Qns (k ) denotes the support for label λk of node ni
i
The label relaxation algorithm is iterative
1 s
s
P (k ) 
Pni (k )Qni (k )
Kni
s 1
ni
N
K ni   Pnsi (k )Qnsi (k )
k 1
K ni
S
s 1
P
is a normalization factor to ensure ni (k )  1
is the number of iterations
42
Example of Supports
Connectivity constraint supports according
to the link quality ( g  number of packets
k m
n
n
received)
►
i
j
M
Q (k )   gk m P (m )
s
ni
m 1
s
nj
►Time
constraints supports
►Color constraint supports
Q (k )  1
s
ni
0.25
11
12
12
0.2
9
0.2
7
0.2
5
0.2
3
0.2
8
0.25
8
10
0.25
4
0.25
1
11
10
Space constraints supports according to
displacement distance
Qnsi (k )  PDF (Dnik )
►
43
Experiments
►Outdoor
deployment of 26 nodes in a 120X60 ft2
area
►Investigate hybrid solution: connectivity and
color constraints
Credits: Shan Lin
44
Coloring Space Size
Colors randomly assigned to nodes.
Larger color space
means dramatically
improved performance
45
Color Uniqueness
Colors randomly assigned to nodes
It is better to assign one
color to multiple nodes.
46
Localization Time
Improved time (w.r.t. Color)
Improved accuracy (w.r.t.
Connectivity)
Assume 50 colors available.
47
Error Tolerance
Tolerate 10% false
negatives, for double of
localization error)
False Negatives: influence of not identifying nodes in the picture
48
Connectivity
49
StarDust Summary
►Accuracy
 5 feet error under 12 colors with 91% correctness in
mapping
►Flexible
framework
 Allows for novel constraints => higher accuracy
►Range
 1500 ~4500 ft (clearness of the atmosphere)
►Time
 52 seconds (our case) for color and connectivity.
 Milliseconds with unique colors.
►Cost
 less than $0.1 per node
50
Comparison
Low
Cost
AdHoc
Long
Range
Low
Power
High
Accuracy
Fast
Ideal
+
+
+
+
+
+
StarDust
+
+
+
+
+/-
+
Spotlight
+
+
+
+
+
+/-
Range Free
+
+
+/-
+
-
+
Ultrasound
-
+
-
-
+
+
WiFi
-
-
+/-
-
-
+
GPS
-
+
+
-
+
+
51
Related Work
►
►
Range-free:
Centroid [Bulusu:02], Gradient(Nagpal:03), DVHop[Niculescu:03], APIT(He:03), Prob Grid(Stoleru:04),
ELA(Vicaire:05), SerLoc (Laszos:05)
Range-based:
Cricket(Pryiantha:00), AhLoS(Savvides:00),
RADAR(Bahl:00), SpotON(Hightower:00),
Calamari(Whitehouse:02), DV-Distance[Niculescu:03],
Robust Quads(Moore:04), Acoustic Range(Simon:04),
MoteTrack(Lorincz:05), RIPS (Maroti:05), Local
Angle(Bruck:05), LaSLAT (Taylor:06)
52
Conclusions
►Node
localization has multi-dimensional requirements
Challenge: How to meet requirements in realistic
settings!
Answer: Asymmetric Architecture
►High
accuracy in multi-dimensional space: Spotlight
 Long effective range, reusable system (amortized cost)
►Fast
localization in multi-dimensional space: StarDust
 Extensible framework, good accuracy
►Systems
work too! Spotlight and StarDust
 System implementation and outdoor evaluation
53
Acknowledgments:
Prof. John A. Stankovic (UVa) - advisor
Tian He (U. Minnesota)
Pascal A. Vicaire (UVa)
Many more professors and colleagues at UVa and UIUC
For more info:
www.cs.virginia.edu/radu
54
Node Localization in Wireless
Sensor Networks
Radu Stoleru
Computer Science
University of Virginia