massimo franceschetti university of california at berkeley
DESCRIPTION
Wireless sensor networks with noisy links. MASSIMO FRANCESCHETTI University of California at Berkeley. Uniform random distribution of points of density λ. One disc per point. Studies the formation of an unbounded connected component. Continuum percolation theory. - PowerPoint PPT PresentationTRANSCRIPT
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MASSIMO FRANCESCHETTIUniversity of California at Berkeley
Wireless sensor networkswith noisy links
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Continuum percolation theoryMeester and Roy, Cambridge University Press (1996)
Uniform random distribution of points of density λ
One disc per pointStudies the formation of an unbounded connected component
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Model of wireless networks
Uniform random distribution of points of density λ
One disc per pointStudies the formation of an unbounded connected component
A
B
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0.3 0.4
Example
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[Quintanilla, Torquato, Ziff, J. Physics A, 2000]
c(r) 4 r2 = 4.5 = ENC
2r
Threshold known (only) experimentally
ENC is independent of r
0.35910c(1)
r2 r2==
c(r)
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Maybe the first paper on Wireless Ad Hoc Networks !
Theory
To model wireless multi-hop networks
Ed Gilbert (1961)(following Erdös and Rényi)
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Ed Gilbert (1961)
λc λ2
1
0
λ
P
λ1
P = Prob(exists unbounded connected component)
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A nice story
Gilbert (1961)
Mathematics Physics
Started the fields ofRandom Coverage Processesand Continuum Percolation
Engineering (only recently)Gupta and Kumar (1998)
Phase TransitionImpurity Conduction
FerromagnetismUniversality (…Ken Wilson)
Hall (1985)Meester and Roy (1996)
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Engineering
“What have we learned from this theory? That adding more transmittershelps reaching connectivity…
…so what?”
(Jan Rabaey)
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Welcome to the real world
“Don’t think a wireless network is like a bunch of discs on the plane” (David Culler)
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•168 nodes on a 12x14 grid• grid spacing 2 feet• open space• one node transmits “I’m Alive”• surrounding nodes try to receive message
Experiment
http://localization.millennium.berkeley.edu
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Prob(correct reception)
Connectivity with noisy links
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Unreliable connectivity
1
Connectionprobability
d
Continuum percolationContinuum percolation
2r
Random connection modelRandom connection model
d
1
Connectionprobability
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Rotationally asymmetric ranges
How do percolation theory results change?
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Random connection model
Connectionprobability
||x1-x2||
)( 21 xxg
2
)())((0x
xgxgENC
]1,0[:)( 221 xxgdefine
Let 221, xx
such that
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Squishing and Squashing
Connectionprobability
||x1-x2||
))(()( 2121 xxpgpxxgs
)( 21 xxg
2
)())((x
xgxgENC
))(())(( xgsENCxgENC
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Connectionprobability
1
||x||
Example
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2
)(0x
xg
Theorem
))(())(( xgsxg cc
For all
“longer links are trading off for the unreliability of the connection”
“it is easier to reach connectivity in an unreliable network”
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Shifting and Squeezing
Connectionprobability
||x||
)(
0
1
)()(
))(()(yhs
s
y
dxxxgxdxxgss
xhgxgss
)(xg
2
)())((x
xgxgENC
))(())(( xgssENCxgENC
)(xgss
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Example
Connectionprobability
||x||
1
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Mixture of short and long edges
Edges are made all longer
Do long edges help percolation?
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2
51.44)(
...359.0
2
2
rdxxgCNP
r
cc
c
CNP
Squishing and squashing Shifting and squeezing
for the standard connection model (disc)
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Prob(Correct reception)
Rotationally asymmetric ranges
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CNP
Is the disc the hardest shape to percolate overall?
Non-circular shapes
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CNP
To the engineer: as long as ENC>4.51 we are fine!To the theoretician: can we prove more theorems?
Connectivity
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The network is connected, buthow do I get packets to destination?
Two extreme cases:
• Re-transmissions are independent (channel is highly variant)
• Re-transmissions have same outcome (channel is not variant)
Flip a coin at every transmission
Flip a coin only once to determine network connectivity
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Compare three cases
1
Connectionprobability
d d
1
Connectionprobability
Reliable network Unreliable network• independent retransmissions• dependent retransmissions
ENCunrel= ENCrel
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Is shortest path always good?
0.9
0.9
0.2
SourceA
B
Sink
Path Hop
Count
Exp. Num. Trans.
A Sink 1 5
A B Sink 2 2.22
Not for independent transmissions!
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Max chance of delivery without retransmission
Shortest path
Min expected number of transmissions
Unreliable-dependent
Reliable
Unreliable-independent
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Bottom lineLong links are helpful if you can consistently exploit them
Connectionprobability
1
||x||
p
p
RR
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Bottom lineLong links are helpful if you can consistently exploit them
Connectionprobability
1
||x||
p
p
RR
N hops vs. N hops (no retransmission)
N hops vs. hops (with indep. retransmission)
p
p
N
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Acknowledgments
Connectivity: L. Booth, J. Bruck, M. Cook.
Routing: T. Roosta, A. Woo, D. Culler, S. Sastry