routing and performance evaluation of disruption tolerant networks

1

Routing and Performance Routing and Performance Evaluation of Disruption Tolerant Evaluation of Disruption Tolerant

NetworksNetworks

Mouhamad IBRAHIMPh.D. defense

Advisor: Philippe Nain

INRIA Sophia AntipolisINRIA Sophia Antipolis14 November, 200814 November, 2008

2

Thesis outline

Part I: Design and performance evaluation of routing

protocols for disruption tolerant networks

Part II: Design and performance evaluation of medium

access control protocol for IEEE 802.11 standard

3

Routing in mobile ad hoc networks

Mobile Ad Hoc Networks (MANETs) No fixed infrastructure Nodes communicate in a peer to peer mode

with other nodes Nodes work as routers: Store-Forward

Routing in MANETs: Main assumption Existence of end-to-end paths between

Source-Destination pairs

4

Routing challenges in MANETs Instability of wireless paths: node mobility, low

node density, interferences,… Does not help to establish and maintain routes

Appearance of Disruption/Delay Tolerant Networks (DTNs): disconnected mobile networks Often there is no end-to-end path among

Source-Destination pairs

take advantage of node mobility to perform routing

Store-Carry-Forward

5

Store-Carry-Forward: how does it work?

S

V1

V3D

V2

R

6

Routing approaches for DTNs

Classification based on the degree of knowledge that nodes have about their future contact opportunities

Four classes of routing techniques: Scheduled-contact based routing Controlled-contact based routing Predicted-contact based routing Opportunistic-contact based routing

7

Opportunistic-contact based routing

Flooding mechanism Epidemic routing protocol

Limit the number of hops Multicopy Two-hop Relay protocol

Limit the number of copies Spray-and-Wait protocol

Question: To what extent we can push the performance if we

increase number of contact opportunities: Throwboxes

8

Throwboxes (1)

Throwboxes are fixed relays with better storage and energy capabilities

Battery powered for short term use or solar panel for long term use

Photos are taken from http://prisms.cs.umass.edu/dome/

9

Throwbox (2)

Operate in Store-Forward paradigm

Promising approach to route messages in DTNs Adding one throwbox on UMass DieselNet

improves packet delivery by 37% and reduces message delivery delay by 10%[1]

Research still in its early stage!!Part I: Evaluate and design routing techniques for

opportunistic DTNs augmented by throwboxes

[1] N. Banerjee et al. An energy-efficient architecture for DTN throwboxes. Infocom 2007.

10

Opportunistic DTNs: Inter-meeting times

Characteristic of inter-meeting times among nodes

Random mobility: Inter-meeting times mobile/mobile have

shown to follow an exponential distribution [Groenevelt et al.: The message delay in mobile ad hoc networks. Performance Evaluation, 2005]

Human mobility: Inter-meeting times mobile/mobile have

shown to follow power law distribution [Chaintreau et al.: Impact of human mobility on the design of opportunistic forwarding algorithms. Infocom, 2006]

11

Opportunistic-contact: Random mobility

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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Mobile/box inter-meeting timesCCDF on a linear-log scale: log(Pr(τ > x)) = log(e - μ x )= - μ x )CCDF on a linear-log scale: log(Prτ((x > log(e =μ x -μ x - =(

Simulation N = 1Exponential –μx Simulation N = 5Exponential –5μxSimulation N = 10Exponential –10μx

Simulation N = 1Exponential –μx Simulation N = 5Exponential –5μxSimulation N = 10Exponential –10μx

13

E[c].μ]E[

E[c]πc

Parameter μ (1) Stationary probability to find the mobile within

neighborhood of a box

f(.,.) stationary spatial pdf of the mobility model

Using Renewal theory, we have

y),f(x,πrdudvv)f(u,πκ

2c )( Lr

Contact time

C1 C2 C3

Time

τ1 τ2 τ3

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Parameter μ (2) Unconditioning on throwbox location within the network area

LxL

Case of Random Direction model: mobile nodes are uniformly distributed[1]

and hence

independent of throwboxes pdf distribution!!

LxL1

cdxdyy)g(x,y)f(x,

]E[V

r2μ

pdf of throwboxes distribution

pdf of throwboxes distributionStationary pdf of location

for mobility model

Stationary pdf of location for mobility model

2L

1y)f(x,

]E[VL

r2μ

1c

2

[1] P. Nain et al. Properties of random direction models. Infocom 2005.

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Parameter μ (3) Case of Random Waypoint model: mobile

nodes are distributed around the center[3]

μ depends on throwboxes spatial distributionThrowboxes uniformly distributed

Throwboxes generally distributed, e.g.

2

1),(

Lyxg

),(),( yxfyxg ]E[VL

1.36r2μ

1c

22

]E[VL

r2μ

1c

21

[3] J.-Y. Le Boudec and M. Vojnovic. Perfect simulation and stationarity of a class of mobility models, Infocom 2005.

16

Performance evaluation of relaying protocols in DTNs with throwboxes

Epidemic routing protocol (ER)

Multicopy two-hop relay protocol (MTR)

17

S

V1

V3D

V2

R

B1

B2

Epidemic Routing flooding protocolEpidemic Routing flooding protocol

Epidemic routing protocol

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S

V1

V3D

V2

R

B1

B2

Copies make at MAX two hops between Source/Destination

Copies make at MAX two hops between Source/Destination

Multicopy two-hop protocol (MTR)

19

S

V1

V3D

V2

R

B1

B2

Network model

Source node

Source node

Destination node

Destination node

N-1 mobile relay nodes

N-1 mobile relay nodes

M throwboxes

M throwboxes

Mobile/mobile: Exponential with λ[4]

Mobile/mobile: Exponential with λ[4]

Mobile/box: Exponential with μ

Mobile/box: Exponential with μ

[4] R. Groenevelt, P. Nain, and G. Koole. The message delay in

mobile ad hoc networks. Performance Evaluation, 2005.

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Metrics of interest

Distribution and mean value of

Delivery delay T user side

Total number of generated copies G when one packet is to be send from source to destination network operator side

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Markov analysis Two-dimensional continuous time absorbing

Markov chain I(t) = (R(t),B(t)) as follows:

For t < T:

R(t) {1,2,…,N} number of mobile nodes holding a copy of the packet (source included)

B(t) {0,1,2,…,M} number of throwboxes holding a copy of the packet (assumed fully disconnected)

For t > T, I(t)= {a} absorbing state, i.e. when destination receives the packet

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MTR protocol: Delivery delay (1) Approach to solve: Stochastic analysis

Delivery delay TMTR is the minimum of N + M mutually independent R.V.s

TMTR = (DSD, Dr1, Dr

2,…, DrN-1, DB

1,…, DBM)

Hence distribution of TMTR reads asMNtMN

MTR ttetTr )1()1()( 1)(

source destination: exponential with rate λsource relay destination: sum of two

exponentials with rate λ source throwbox destination: sum of two exponentials with rate μ

23

MTR protocol: Delivery delay (2) and mean of TMTR reads as

Using fluid model, we obtained also asymptotic expression for E[TMTR] when N or M go large

mn1N

0n

M

0mMTR )

MμNλ

μ()

MμNλ

λ(m)!(n

m

M

n

1N

MμNλ

1]E[T

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MTR protocol: # of generated copies

Define Pra(n,m) as probability that last visited state before absorption is state (n,m)

Pra(n,m) is sum of probabilities of different paths joining state (1, 0) to state (n,m) These probabilities are all equal. Their total number is

The probability distribution of GMTR reads as

1m

0j

1n

1ia MμNλ

μj)(M

MμNλ

λi)(N

MμNλ

mμnλ

1n

1mnm)(n,Ρr

1n

1mn

MNknknkGNk

MknaMTR

,...2,1,),(Pr)(Pr),min(

),1max(

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Epidemic protocol: Delivery delay Approach to solve: Theory of absorbing Markov

chain

Delivery delay TER represents time to absorption

Q = infinitesimal generator of Markov chain

M = transition matrix among non-absorbing states

00

RMQ

,j)(1,m]E[T1)(MN

1jER

m*(i,j) is the (i,j)th entry of M-1

[1,1,...1],[1,0,...0]bε,be1x)Pr(T MxER

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Epidemic: # of generated copies

Define Pra(n,m) as probability that last visited state before absorption is state (n,m)

Case of epidemic protocol: transition rates are state dependent approach reported by [Gaver et al.: Finite Birth-And-Death Models in Randomly Changing Environments, 1984]

The probability distribution of GER follows then

a)m),q((n,mN)n(1,mm)(n,Ρra

MN1,2,...k,n)k(n,Prk)(GPrN)min(k,

M)kmax(1,naER

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Case of connected Throwboxes

Underlying assumption: Pass a copy to one throwbox to let all the others infected

Same expressions hold by substitutingM 1μ M μ

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Model validation: Delivery delay

Epidemic protocol

RWP model

Epidemic protocol

RWP model

Throwboxes disconnected and uniformly distributedThrowboxes disconnected and RWP stationary distributedThrowboxes connected and uniformly distributedThrowboxes connected and RWP stationary distributed


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MTR protocolRWP model

MTR protocolRWP model



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Performance evaluation framework for throwboxes-augmented DTNs

Objective: Framework to evaluate and analyze performance of various routing strategies for DTNs extended with throwboxes

31

Proposed five routing strategies (1)

Main idea: define possible message forwarding interactions among the Source, Mobile relays, Throwboxes and the Destination

Ultimate goal: exploit throwboxes presence to minimize copies generations at mobile nodes

32

Proposed five routing strategies (2) Common forwarding interactions:

Source Relay

Relay Destination

Relay Destination

Source Relay

Relay Throwbox

Relay Throwbox

Relay Throwbox

Relay Destination

Relay Destination

Strategy VStrategy IVStrategy IIIStrategy IIStrategy I

Infected throwbox Mobile relay

Destination

Particular interactions for each strategyParticular interactions for each strategy

Infected mobile relay Mobile relay

Source ThrowboxDestination

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Metrics of interestUnder a given routing strategy s:

1- Mean delivery delay between a Source/Destination E[Ts]

Mean number of valuable transmissions E[Gs], i.e. those made only by mobile nodes plus the source

2- Mean number of mobile relays infected by the source, Is

3- Mean number of infected throwboxes, Ks

4- Proba. Source delivers message to destination, PrSs

5- Proba. Mobile relay delivers message to destination, PrRs

34

Modeling framework (1) Three-dimensional continuous time absorbing

Markov chain As(t) = (Is(t), Js(t), Ks(t)) as follows:

For t < Ts, As(t) = (Is(t), Js(t), Ks(t)):

Is(t) Number of mobile nodes infected by the source

Js(t) Number of mobile nodes infected by the

throwboxes

Ks(t) Number of infected throwboxes

For t > Ts: As(t) = {a} absorbing state

35

Modeling framework (2)

)1kj,(i,Fk)j,S(i,

k)j,γ(i,k),1j(i,F

k)j,S(i,

k)j,β(i,

k)j,,1(iFk)j,S(i,

k)j,α(i,k)j,(i,uk)j,(i,F

ss

sss

i,j,k i,j+1,k

i+1,j,k

i,j,k+1

a

α(i,j,k)

β(i,j,k)

θ(i,j,k)

γ(i,j,k)

S(i,j,k)

Fs(i,j,k) is mean value of metric Fs till absorption starting from (i,j,k)

Fs(i,j,k) is mean value of metric Fs till absorption starting from (i,j,k)

Mean value of metric Fs at (i,j,k)

Mean value of metric Fs at (i,j,k)

k)j,S(i,

1k)j,(i,u s 1- Mean sojourn time Ts

2- Mean number of mobile relays Is

k)j,S(i,

k)j,θ(i,ik)j,(i,u s 3- Mean number of throwboxes

Ks k)j,S(i,

k)j,θ(i,k)j,(i,us k4- Proba. delivery by source

PrSs k)j,S(i,

1k)j,(i,us 5- Proba. delivery by relay PrRs

k)j,S(i,

)1(k)j,(i,us

ji

36

Modeling framework (3) Values of Fs are known at last states only

one possible transition to state {a}, e.g.

Iterating recursive equation till initial state (1,0,0): (1,0,0)T]E[T

ss

M)(N,0,uM)(N,0,F ss N,0,M

a

θ(N,0,M)

(1,0,0)PrR

(1,0,0)PrS(1,0,0)K1)(1,0,0)(I]E[G

s

ssss

Known!

37

Modeling framework (4)

To compute E[Ts] and G[Ts] under a given strategy Define corresponding state space Es and infinitesimal generator Qs(t)

38

Framework validation

N = M Metric Analytical Simulation Rel. error %

10

T(1,0,0) 8.15 103 7.95 103 2.54

I(1,0,0) 3.21 3.27 1.75

K(1,0,0) 1.48 1.37 7.83

PrS(1,0,0) 0.27 0.28 4.21

PrR(1,0,0) 0.55 0.56 1.15

100

T(1,0,0) 2.30 103 2.6 103 2.75

I(1,0,0) 8.35 8.46 1.29

K(1,0,0) 4.34 4.61 5.81

PrS(1,0,0) 0.07 0.08 6.48

PrR(1,0,0) 0.79 0.78 3.0

Strategy II: Analytical versus simulation results Strategy II: Analytical versus simulation results

39

Comparing E[T] and E[G] with respect to Epidemic protocol

Strategy II Strategy IV Strategy V

Strategy II Strategy IV Strategy V

40

Diameter of epidemic protocol

Context: Opportunistic DTNs running epidemic protocol WITHOUT throwboxes

Objective: Examine the mean length of forwarding

path

41

Diameter of epidemic protocol Instance of epidemic tree:

XS,D denote number of intermediate hops between S and D

Aim is to compute E[XS,D]: diameter of epidemic protocol

R5

S

R2 R1

D R3

R4

42

Diameter computation (1)

Approach to solve: Theory of recursive tree

Recursive tree is like any tree on a graph, however, nodes are labeled with their joining instants to the tree

Example: recursive tree of order 4 1

2

3 4

2

4

3

1

2

3

4

1

2 3 4

1

2

3

4

1

3

2

4

1

E[Xi,j] is known for random tree

E[Xi,j] is known for random tree

43

Diameter computation (2)

Conditioning on possible labels of the destination among the N nodes

Look to the impact of limiting number of forwarding hops on relaying performance

Using the framework, we analyze different dissemination algorithm with limited number of hops

1

11 1

1]log[][

1][

N

i

N

nnSD i

i

NNH

NXE

44

Epidemic protocol: Limiting # of hops

Max. hop = 2Max. hop = 3 Max. hop = 4Max. hop = 5

Max. hop = 2Max. hop = 3 Max. hop = 4Max. hop = 5

45

Part II

46

Adaptive Backoff Algorithm for IEEE 802.11

Motivation: IEEE 802.11 performs poorly in congested network Following a successful transmission, source

station chooses backoff duration randomly in {0,…,CW0}

Objectives: Adaptive algorithm aware of active stations

Maximize system throughput and minimize end-to-end delay

Inadequate for large networks

Inadequate for large networks

47

σ2T

1τc

*

N

How to transmit at optimal transmission probability τ*

Bianchi model[5]: Transmission probability

Our idea:

)p)(2(1pCW1)p)(CW2(1

p)22(1τm

00

m= log(CWmax/CW0)

m= log(CWmax/CW0)

)p)p(2p(1τ

p)2)(1τ-(2CWm*

*

min

[5] G. Bianchi. Performance analysis of the IEEE 802.11 distributed coordination function. JSAC 2000.

48

Estimating # of active stations Active stations are decoding all transmitted

packets on the channel identify emitting stations

Stations counts signs of life coming from others stations signs of life: error free data and RTS

packets Measured during virtual transmission

times

Samples used as input to a corrected WMA filter

β2

0iCW

2

0iNCW

αN

ik

ikik

Ňk: sample at kth periodCWk: window at kth periodα, β : correcting factors

49

Algorithm performanceAdaptiveStandard

AdaptiveStandard

Group entrance

Group departure

Group entrance

Group departure

50

Conclusions (1)

Accurate approximation for meeting rate between a mobile/throwbox: For two common mobility model For general throwboxes spatial distribution

Explicit expressions for the distribution and the mean of delivery delay and number of generated copies Under epidemic and MTR protocols Asymptotic expressions for these means under

MTR

51

Conclusions (2)

Proposed various routing strategies for DTNs augmented with throwboxes

Markovian framework to evaluate performance of various routing strategies Can be extended to evaluate other

performance metrics and routing techniques

Explicit expression for the diameter of forwarding path under epidemic protocol

52

Conclusions (3)

Proposed an efficient MAC protocol for IEEE 802.11 Adapt starting value of contention window

to network size Original mechanism to estimate number of

active stations

53

Future research direction

Analyze correlation and heterogeneous movement patterns in real mobility traces

Elaborate corresponding mobility models and evaluate proposed routing strategies over them e.g. markovian model for community based

mobility, bus mobility

Analyze impact of different buffer management techniques on routing under heterogeneous mobility model

54

The end …

Thank you!

55

Publications M. Ibrahim, A. Al Hanbali, P. Nain, "Delay and Resource

Analysis in MANETs in Presence of Throwboxes", Performance Evaluation, Vol. 64, Issues 9-12, P. 933-947, October 2007.

Al Hanbali, M. Ibrahim, V. Simon, E. Varga, I. Carreras "A Survey of Message Diffusion Protocols in Mobile Ad Hoc Networks", Inter-Perf 2008, Athens, Greece, Octobre 2008.

M. Ibrahim, S. Alouf, "Design and Analysis of an Adaptive Backoff Algorithm for IEEE 802.11 DCF mechanism", Networking 2006, Coimbra, Portugal, Mai 2006.

Under submission: M. Ibrahim, P. Nain, I. Carreras. "On routing trade-offs in throwbox-embedded DTN networks".

57

ZebraNet: mobility based routing

Objective track zebras in wildlife Collars attached to zebras Base stations move sporadically to collect data

58




MTRMTR EpidemicEpidemic

59

Virtual transmission time

Virtual transmission time = time separating two successful random transmissions

virtual transmission time $j$

Idle IdleACK

SIFS DIFS

ACK

SIFS DIFS DIFS

slots slotsSuccessful Successful transmission transmission

virtual transmisiontime $j-1$

$T_c$ $T_s$

Collision

$\sigma$

s

s

Tewasted_timΕ

TthroughputSystem

60

Mean wasted time E[wasted_time] = E[collision_time] +

E[idle_time] E[collision_time] = f(τ,N) with τ for

fixed N E[idle_time] = g(τ,N) with τ for fixed NN = 50

N = 20N = 10

N = 50N = 20N = 10

Bianchi

model

Bianchi

model

σ2

Tc

1τ*

N

routing and performance evaluation of disruption tolerant networks

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