![Page 1: Models for Epidemic Routing Giovanni Neglia INRIA – EPI Maestro 20 January 2010 Some slides are based on presentations from R. Groenevelt (Accenture Technology](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfad1a28abf838c9c1a5/html5/thumbnails/1.jpg)
Models for Epidemic RoutingModels for Epidemic Routing
Giovanni Neglia
INRIA – EPI Maestro
20 January 2010
Some slides are based on presentations from
R. Groenevelt (Accenture Technology Labs)
and M. Ibrahim (previous Maestro PhD student)
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Outline
Introduction on Intermittently Connected Networks (or Delay/Disruption Tolerant Networks)
Markovian models Message Delay in Mobile Ad Hoc Networks, R.
Groenevelt, G. Koole, and P. Nain, Performance, Juan-les-Pins, October 2005
Impact of Mobility on the Performance of Relaying in Ad Hoc Networks, A. Al-Hanbali, A.A. Kherani, R. Groenevelt, P. Nain, and E. Altman, IEEE Infocom 2006, Barcelona, April 2006
Fluid models Performance Modeling of Epidemic Routing, X.
Zhang, G. Neglia, J. Kurose, D. Towsley, Elsevier Computer Networks, Volume 51, Issue 10, July 2007, Pages 2867-2891
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Intermittently Connected Networks
mobile wireless networks no path at a given time instant between two nodes
because of power contraint, fast mobility dynamics maintain capacity, when number of nodes (N) diverges
• Fixed wireless networks: C = Θ(sqrt(1/N)) [Gupta99]• Mobile wireless networks: C = Θ(1), [Grossglauser01]
a really challenging network scenario No traditional protocol works
A
V2 V3V1
B C
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Some examples
Inter-planetary backbone
DakNet, Internet to rural area
DieselNet, bus network
ZebraNet, Mobile sensor networks
Network for disaster relief team Military battle-field network …
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Terminology and Focus Why delay tolerant?
support delay insensitive app: email, web surfing, etc.
Why disruption tolerant? almost no infrastructure, difficult to attack post 9/11 syndrome
In this lecture we only consider case where there is no information about future meetings how to route?
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Epidemic Routing
A
V2 V3V1
B C
Message as a disease, carried around and transmitted
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Epidemic Routing
A
V2 V3V1
B C
Message as a disease, carried around and transmittedo Store, Carry and Forward paradigm
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Epidemic Routing
Message as a disease, carried around and transmitted
How many copies? 1 copy ⇨ max throughput, very high delay More ⇨ reduce delay, increase resource consumption
(energy, buffer, bandwidth) Many heuristics proposed to constrain the infection
K-copies, probabilistic…
A
V2
V3
V1
BC
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An example:Epidemic routing in ZebraNet
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An example:Epidemic routing in ZebraNet
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Standard Epidemic Routing
S
22
33
44
Time
55
DD
t1
TD
t2
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Epidemic Style Routing
Epidemic Routing [Vahdat&Becker00] Propagation of a pkt -> Disease Spread achieve min. delay, at the cost of transm.
power, storage trade-off delay for resources
K-hop forwarding, probabilistic forwarding, limited-time forwarding, spray and wait…
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2-Hop Forwarding
S
22
33
4
Time
55
DD
t0
TD
At most 2 hop
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Limited Time Forwarding
S
22
33
Time
55
D
t0
44
3
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Epidemic Style Routing
Epidemic Routing [Vahdat&Becker00] Propagation of a pkt -> Disease Spread achieve min. delay, at the cost of transm.
power, storage trade-off delay for resources
K-hop forwarding, probabilistic forwarding, limited-time forwarding, spray and wait…
Recovery: deletion of obsolete copies after delivery to dest., e.g., TIMERS: when time expires all the copies are
erased IMMUNE: dest. cures infected nodes VACCINE: on pkt delivery, dest propagates
anti-pkt through network
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IMMUNE RecoveryS
22
33
44
Time
55
DD
t0
TD
44788
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VACCINE RecoveryS
22
33
44
Time
55
DD
t0
TD
44788 7
22
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Outline
Introduction on Intermittently Connected Networks (or Delay/Disruption Tolerant Networks)
Markovian models the key to Markov model Markovian analysis of epidemic routing
Fluid models
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The setting we consider N+1 nodes
moving independently in an finite area A
with a fixed transmission range r and no interference
1 source, 1 destination Performance metrics:
Delivery delay Td
Avg. num. of copies at delivery C Avg. total num. of copies made G Avg. buffer occupancy
S
2
3
D
N
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Standard random mobility models
X1
X2
V1
V2
Directions (αi) are uniformly distributed (0, 2π)
Speeds (Vi) are uniformly distributed (Vmin,Vmax)
Travel times (Ti) are exponentially /generally distributed
Directions (αi) are uniformly distributed (0, 2π)
Speeds (Vi) are uniformly distributed (Vmin,Vmax)
Travel times (Ti) are exponentially /generally distributed
R
T1, V1
T2, V2
R
Next positions (Xi)s are uniformly distributed
Speeds (Vi)s are uniformly distributed (Vmin,Vmax)
Next positions (Xi)s are uniformly distributed
Speeds (Vi)s are uniformly distributed (Vmin,Vmax)
α1
α2
Random Waypoint model (RWP)
Random Waypoint model (RWP)
Random Direction model (RD)
Random Direction model (RD)
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The key to Markov model
[Groenevelt05] if nodes move according to standard random
mobility model (random waypoint, random direction) with average relative speed E[V*],
and if Nr2 is small in comparison to A pairwise meeting processes are almost
independent Poisson processes with rate:
A
wrV *2 w: mobility specific constant
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Exponential distribution finds its roots in theindependence assumptions of each mobility
model:• Nodes move independently of each other• Random waypoint: future locations of a
node are independent of past locations of that node.
• Random direction: future speeds and directions of a node are independent of past speeds and directions of that node.
There is some probability q that two nodes will meet before the next change of direction. At the next change of direction the process repeats itself, almost independently.
Intuitive explanation
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Why “almost”?
pairwise meeting processes are almost independent Poisson processes with rate:
1. inter-meeting times are not exponential if N1 and N2 have met in the near past they are
more likely to meet (they are close to each other)• the more the bigger it is r2 in comparison to A
2. meeting processes are not independent if in [t,t+τ] N1 meets N2 and N2 meets N3, it is
more likely that N1 meets N3 in the same interval• the more the bigger it is r2 in comparison to A
moreover if Nr2 is comparable with A (dense network) a lot of meeting happen at the same time.
A
wrV *2 w: mobility specific constant
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Nodes move on a square of size 4x4 km2 (L=4 km)
Different transmission radii (R=50,100,250 m)
Random waypoint and random direction:no pause time[vmin,vmax]=[4,10] km/hour
Random direction: travel time ~ exp(4)
Examples
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Pairwise Inter-meeting time
ttTP
etTP t
)(log
)(
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Pairwise Inter-meeting time
ttTP
etTP t
)(log
)(
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Pairwise Inter-meeting time
ttTP
etTP t
)(log
)(
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Assume a node in position (x1,y1) moves in a straight line with speed V1.
Position of the other node comes from steady-state distribution with pdf π(x,y).
Look at the area A covered in Δt time:
The derivation of λ
V*
V*Δt
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The derivation of λ
Probability that nodes meet given by
A
yx dxdyyxp .),(1,1
For small r the points in π(x,y) in A can be approximated by π(x1,y1) to give
€
px1 ,y1≈ 2r ⋅V1
* ⋅Δ t ⋅π (x1,y1).
Unconditioning on (x1,y1) gives
11111,100
),( ydxyxpp yx
LL
€
≈2r ⋅V1* ⋅Δ t ⋅
0
L
∫0
L
∫ π 2(x1,y1)dx1y1
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Proposition: Let r<<L. The inter-meeting time for the random direction and the random waypoint mobility models is approximately exponentially distributed with parameter
Here E[V*] is the average relative speed between two nodes and is the pdf in the point (x,y).
The derivation of λ
€
≈2 r ⋅E[V*]⋅0
L
∫ π 2(x,y)dxdy0
L
∫ ,
),( yx 9
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The derivation of λ
Proposition: Let r<<L. The inter-meeting time for the random direction and the random waypoint mobility models is approximately exponentially distributed with parameter
Here E[V*] is the average relative speed between two nodes and ω ≈ 1.3683 is the Waypoint constant.€
RD ≈2rE[V*]
L2,
€
RW ≈2rωE[V*]
L2.
,
82L
rvRD
.
82L
rvRW
If speeds of the nodes are constant and equal to v,
then
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Summary up to now
First steps of this research a good intuition some simulations validating the intuition for
a reasonable range of parameters What could have been done more
prove that the results is asymptotically (r->0) true “in some sense”
What can be built on top of this? Markovian models for routing in DTNs
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2-hop routing
Model the number of occurrences of the message as an absorbing Markov chain:
• State i{1,…,N} represents the number of occurrences of the message in the network.
• State A represents the destination node receiving (a copy of) the message.
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Model the number of occurrences of the message as an absorbing Markov chain:
• State i{1,…,N} represents the number of occurrences of the message in the network.
• State A represents the destination node receiving (a copy of) the message.
Epidemic routing
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Proposition: The Laplace transform of the message delay under the two-hop multicopy protocol is:
Message delay
, )!(
)!1()(
1
*2
iN
i NiN
NiT
NiiN
N
N
iiNP
i,,1 ,
)!(
)!1()( 2
and
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Proposition: The Laplace transform of the message delay under epidemic routing is:
Message delay
€
TE*(θ) =
1
N
jλ (N +1− j)
jλ (N +1− j) +θj=1
i
∏i=1
N
∑ ,
€
P(NE = i) =1
N, i =1,K ,N
and
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Corollary: The expected message delay under the
two-hop multicopy protocol is
Expected message delay
N
iiNiN
Ni
NTE
1
2
2)!(
)!1(1 ][
and under the epidemic routing is
€
E[TE ] = 1
λN
1
ii=1
N
∑
Where γ ≈ 0.57721 is Euler’s constant.
,1
2
1
N
ON
,1
)log(1
N
ONN
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Relative performance
and
€
E[TE ]
E[T2]≈
log(N)
N
2
π
€
E[NE ]
E[N2]≈
N
2π
The relative performance of the two relay protocols:
Note that these are independent of λ!
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• These expressions hold for any mobility model which has exponential meeting times.
• Two mobility models which give the same λ also have the same message delay for both relay protocols! (mobility pattern is “hidden” in λ)
• Mean message delay scales with mean first-meeting times.
• λ depends on:- mobility pattern- surface area- transmission radius
Some remarks
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Example: two-hop multicopy
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Example: two-hop multicopy
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Example: two-hop multicopy
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Example: two-hop multicopy
Distribution of the number of copies (R=50,100,250m):
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Example: two-hop multicopy
Distribution of the number of copies (R=50,100,250m):
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Example: two-hop multicopy
Distribution of the number of copies (R=50,100,250m):
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Example: unrestricted multicopy
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Outline
Introduction on Intermittently Connected Networks (or Delay/Disruption Tolerant Networks)
Markovian models the key to Markov model Markovian analysis of epidemic routing
Fluid models
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Why a fluid approach?
[Groenevelt05] Markov models can be developed States: nI =1,…, N: num. of infected nodes, different
from destination; D: packet delivered to the destination
Transient analysis to derive delay, copies made by delivery; hard to obtain closed form, specially for more complex schemes
Infection rate:
Delivery rate:
)()( IIN nNnIr
In
)1( N )2(2 N )3(3 N
1 2 3 N-2 NN-12)2( N3)3( N )1( N
D
2 3 )2( N )1( N N
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Modeling Works: Small and Haas Mobicom 2003 [small03]
ODE introduced in a naive way for simple epidemic scheme
• N is the total number of nodes,• I the total number of infected nodes• λ is the average pairwise meeting rate
Average pair-wise meeting rate obtained from simulations
TON 2006 [haas06] consider a Markov Chain with N-1 different
meeting rates depending on the number of infected nodes (obtained from simulations)
Numerical solution complexity increases with N
))()(()(' tINtItI
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Our contribution (1/3)
A unified ODE framework... limiting process of Markov processes as N
increases [Kurtz 1970]
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[Kurtz1970]
{XN(t), N natural} a family of Markov process in Zm
with rates rN(k,k+h), k,h in Zm
It is called density dependent if it exists a continuous function f() in Rm such that rN(k,k+h) = N f(1/N k, h), h<>0
Define F(x)=Σh h f(x,h)
Kurtz’s theorem determines when {XN(t)} are close to the solution of the differential equation:
€
∂x(s)
∂s= F(x(s)),
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[Kurtz1970]
Theorem. Suppose there is an open set E in Rm and a constant M such that |F(x)-F(y)|<M|x-y|, x,y in E supx in EΣh|h| f(x,h) <∞,
limd->∞supx in EΣ|h|>d|h| f(x,h) =0
Consider the set of processes in {XN(t)} such that limN->∞ 1/N XN(0) = x0 in Eand a solution of the differential equation
such that x(s) is in E for 0<=s<=t, then for each δ>0
€
∂x(s)
∂s= F(x(s)),
€
x(0) = x0
€
limN→∞
Pr sup0≤s≤ t
1
NXN (s) − X(s) > δ
⎧ ⎨ ⎩
⎫ ⎬ ⎭= 0
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Application to epidemic routing rN(nI)=λ nI (N-nI) = N (λN) (nI/N) (1-nI/N) assuming β = λ N keeps constant (e.g.
node density is constant) f(x,h)=f(x)=x(1-x), F(x)=f(x) as N→∞, nI/N → i(t), s.t.
with initial condition
multiplying by N
))(1)(()(' tititi
Nni IN
/)0(lim)0(
))()(()(' tINtItI
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What can we do with the fluid model? Derive an estimation of the number of
infected nodes at time t e.g. if I(0)=1 -> I(t)=N/(1+(N-1)e-Nλt)
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Delivery delay Td: time from pkt generation at the src until the dst receives the pkt CDF of Td, P(t) := Pr(Td<t) given by:
Average delay
Avg. num. of copies sent at delivery
infected nodes-dst meeting rate
What can we dowith the fluid model?
))(1)(()(' tPtItP
0
))(1(][ dttPTE d
1)()(][0
'
dttPtICE
delivery rate at time t
prob. that pkt is not delivered yet
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What can we dowith the fluid model? Consider recovery process, eg IMMUNE
(dest. node cures infected node):
Total num. of copies made:
Total buffer usage
ItR
IRINItI
)('
)()('
)(lim tRt
0
)( dttI
R(t): num. of recovered nodesNum. of susceptible
nodes
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More flexible than Markov models to model all the different variants,
e.g. limited-time forwarding
or probabilistic forwarding, K-hop forwarding...
under different recovery schemes (VACCINE, IMMUNE,...)
)()('),()1)()()(1)(()('
tItTtItTtINtItI
r
rrrr
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Our contribution
Closed formulas for average delay, number of copies and CDF in many cases
Asymptotic results Numerical evaluation always possible
without scaling problems Study of delay vs buffer occupancy or
delay vs power consumption for different forwarding schemes
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Epidemic Routing Average delay
ODE provides good prediction on average delay
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Delay distribution CDF of delay under epidemic routing,
N=160
Modeling error mainly due to approx. of ODE
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Some results Extensible to other schemes
teNNtI )1()(
NteN
NtP
1
1)(
NteN
NtI
)1(1)(
teNNtNetP )1(11)(
NtpeN
NtI
)1(1)(
pNtpeN
NtP /1)
1(1)(
)1(
ln
N
N
1
1
2
1
N
)1(
ln][
)1(
ln
Np
NTE
N
Nd
)(1,2
1IMNG
NC
)_(2
523 2
TXIMNNN
G
2
1,
2
N
GNC
p
NpC
1
)1(
Epidemicrouting
2-hopforwarding
Prob.forwarding
)(),( tPtI ][ dTE GC,
Matching results from Markov chain model, obtained easier
~
~ ~
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An application: Tradeoffs evaluation
Delay vs Power Delay vs Buffer
2-hop3-hop
2-hop3-hop
src/dest transmission
epidemicrouting
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Other issues
Not considered in this presentation Effect of different buffer management
techniques when the buffer is limited ODEs by moment closure technique
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References
Papers discussed Markovian models
• Message Delay in Mobile Ad Hoc Networks, R. Groenevelt, G. Koole, and P. Nain, Performance, Juan-les-Pins, October 2005
• Impact of Mobility on the Performance of Relaying in Ad Hoc Networks, A. Al-Hanbali, A.A. Kherani, R. Groenevelt, P. Nain, and E. Altman, IEEE Infocom 2006, Barcelona, April 2006
Fluid models• Performance Modeling of Epidemic Routing, X. Zhang, G. Neglia, J.
Kurose, D. Towsley, Elsevier Computer Networks, Volume 51, Issue 10, July 2007, Pages 2867-2891
Other [Grossglauser01] Mobility Increases the Capacity of Ad Hoc
Wireless Networks, M. Grossglauser and D. Tse, IEEE Infocom 2001
[Gupta99] The capacity of Wireless Networks, P. Gupta, P.R. Kumar, IEEE Conference on Decision and Control 1999
[Kurtz70] Solution of ordinary differential equations as limits of pure jump markov processes, T. G. Kurtz, Journal of Applied Probabilities, pages 49-58, 1970