Routing and Performance Evaluation of Disruption Tolerant Networks

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Routing and Performance Evaluation of Disruption
Tolerant Networks

• Mouhamad IBRAHIM
• Ph.D. defense
• Advisor Philippe Nain
• INRIA Sophia Antipolis
• 14 November, 2008

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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

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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

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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

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Store-Carry-Forward how does it work?
R
V2
D
V3
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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

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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

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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/d
ome/
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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 101
• 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.
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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

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Opportunistic-contact Random mobility
Random Waypoint model (RWP)
Random Direction model (RD)
X2
V2
T2, V2
X1
a2
V1
T1, V1
R
a1
R

• Directions (ai) are uniformly distributed (0, 2p)
  
• Speeds (Vi) are uniformly distributed (Vmin,Vmax)
  
• Travel times (Ti) are exponentially /generally
  distributed

• Next positions (Xi)s are uniformly distributed
• Speeds (Vi)s are uniformly distributed
  (Vmin,Vmax)

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Mobile/box inter-meeting times
CCDF on a linear-log scale log(Pr(t gt x))
log(e - µ x ) - µ x
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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

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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 distributed1 ?
• and hence
• independent of throwboxes
• pdf distribution!!

pdf of throwboxes distribution
Stationary pdf of location for mobility model
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 center3
• µ depends on throwboxes spatial distribution
• Throwboxes uniformly distributed
• Throwboxes generally distributed, e.g.

3 J.-Y. Le Boudec and M. Vojnovic. Perfect
simulation and stationarity of a class of
mobility models, Infocom 2005.
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Performance evaluation of relaying protocols in
DTNs with throwboxes

• Epidemic routing protocol (ER)
• Multicopy two-hop relay protocol (MTR)

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Epidemic routing protocol
Epidemic Routing ? flooding protocol
R
V2
D
V3
V1
S
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Multicopy two-hop protocol (MTR)
Copies make at MAX two hops between
Source/Destination
R
V2
D
V3
V1
S
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Network model
M throwboxes
N-1 mobile relay nodes
Mobile/box Exponential with µ
R
V2
Mobile/mobile Exponential with ?4
D
V3
Destination node
Source node
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 lt 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 gt 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, Dr2,, DrN-1, DB1,, DBM)
• Hence distribution of TMTR reads as

source ? relay ? destination sum of two
exponentials with rate ?
source ? throwbox ? destination sum of two
exponentials with rate µ
source ? destination exponential with rate ?
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MTR protocol Delivery delay (2)

• and mean of TMTR reads as
• Using fluid model, we obtained also asymptotic
  expression for ETMTR when N or M go large

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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

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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

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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

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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 substituting
• M ? 1
• µ ? M µ

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Model validation Delivery delay

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

Epidemic protocol RWP model
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Model validation Delivery delay

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

MTR protocol RWP 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

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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

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Proposed five routing strategies (2)

• Common forwarding interactions

Particular interactions for each strategy
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Metrics of interest

• Under a given routing strategy s
• 1- Mean delivery delay between a
  Source/Destination ETs
• Mean number of valuable transmissions EGs, 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

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Modeling framework (1)

• Three-dimensional continuous time absorbing
  Markov chain As(t) (Is(t), Js(t), Ks(t)) as
  follows
• For t lt 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 gt Ts As(t) a ? absorbing state

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Modeling framework (2)
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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)

Known!
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Modeling framework (4)

• To compute ETs and GTs under a given strategy
  ? Define corresponding state space Es and
  infinitesimal generator Qs(t)

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Framework validation
Strategy II Analytical versus simulation results
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Comparing ET and EG with respect to Epidemic
protocol
Strategy II Strategy IV Strategy V
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Diameter of epidemic protocol

• Context Opportunistic DTNs running epidemic
• protocol WITHOUT throwboxes
• Objective Examine the mean length of forwarding
• path

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Diameter of epidemic protocol

• Instance of epidemic tree
• XS,D denote number of intermediate hops between S
  and D
• ? Aim is to compute EXS,D diameter of
  epidemic protocol

S
R2
R1
R5
R3
D
R4
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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

EXi,j is known for random tree
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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

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Epidemic protocol Limiting of hops
Max. hop 2 Max. hop 3 Max. hop 4 Max. hop
5
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• Part II

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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
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How to transmit at optimal transmission
probability t

• Bianchi model5
• Transmission probability
• Our idea

m log(CWmax/CW0)
5 G. Bianchi. Performance analysis of the IEEE
802.11 distributed coordination function. JSAC
2000.
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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

Nk sample at kth period CWk window at kth
period a, ß correcting factors
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Algorithm performance
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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

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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

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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

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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

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The end

• Thank you!

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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".

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ZebraNet mobility based routing

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

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Model validation Delivery delay

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

MTR
Epidemic
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Virtual transmission time

• Virtual transmission time time separating two
  successful random transmissions

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Mean wasted time

• Ewasted_time Ecollision_time Eidle_time
• Ecollision_time f(t,N) with t for fixed N
• Eidle_time g(t,N) with t for fixed N

N 50 N 20 N 10
Bianchi model