Routing in Intermittently Connected Mobile Networks

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Routing in Intermittently Connected Mobile
Networks
Thrasyvoulos Spyropoulos, Kostantinos Psounis,
and Cauligi S. Raghavendra EE Department,
USC spyropou, kpsounis, raghu_at_usc.edu
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Intermittently Connected Mobile Networks
S
D

• A wireless network that is very sparse and
  partitioned
• disconnected clusters of nodes
• Nodes are (highly) mobile making the clusters
  change often over time
• No contemporaneous end-to-end path!

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Networks following ICMN paradigm

• Sensor networks for habitat monitoring and
  wildlife tracking
• ZebraNet sensor nodes attached on zebras,
  collecting information about movement patterns,
  speed, herd size, etc.
• Boatnet
• Ad hoc networks for low cost Internet provision
  to remote areas/communities
• Africa, Saami, etc.
• Inter-planetary networks
• extend the idea of Internet to space
• Ad-hoc military networks

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Conventional Routing Protocols Fail

• Reactive Protocols (e.g. DSR D. Johnson et al.
  01, AODV C. Perkins et al. 02)
• route request cannot reach destination!
• path breaks right after or even while being
  discovered
• Proactive Protocols (e.g. DSDV C. Perkins et al.
  94, DREAM S. Basagni et al. 98)
• will fail to converge!
• deluge of topology-update packets

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Can anything be done then?
A different routing paradigm

• Exploit node mobility to deliver messages
• (Tse et al. exploit mobility to increase
  capacity)
• A snapshot of connectivity graph is always
  disconnected.
• Idea If we overlap many snapshots over time, an
  end-to-end path will be formed eventually!
• Store-and-forward model of routing
• a node stores a message until an appropriate
  communication opportunity arises
• a series of independent forwarding decisions
  time next hop that will eventually bring the
  packet to its destination

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Example of store and forward routing
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Main Issue What is an appropriate next hop?
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Choosing A Next Hop

• A local and intuitive criterion A forwarding
  step is efficient if it reduces the expected
  distance from destination
• usually reduction of expected distance gt
  reduction of expected hitting time

Destination
B
A
C
Efficient Routing Ensure that each forwarding
step on the average reduces distance or hitting
time with destination
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Problem Formulation

• M nodes move independently on an grid of size N
• mobility models random walk, random waypoint
• Transmission range K
• small enough to have partial connectivity
• transmission is faster than movement
• Proximity measure between positions A and B
• Manhattan distance dAB xA xB yA yB
• Performance evaluation metrics
• expected hitting time from A to B EATB
• in a symmetric graph EATB ET(dAB)
• average delivery delay
• number of transmissions (per message delivered)

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Problem Formulation (contd)

• Each node maintains a timer for each other node
• TX(Y) time since node X last encountered node
  Y
• encounter come within transmission range
• only information available to a node X regarding
  the network (no location, speed, direction, etc.)
• Timer maintenance
• Initially TX(Y) ?
• When X encounters Y TX(Y) 0
• At every time step (unless case b applies) TX(Y)
  TX(Y) 1

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Single-Copy vs. Multiple-Copy Routing Strategies

• Single-Copy only a single copy of each message
  exists in the network at any time
• Multiple-Copy multiple copies of a message may
  exist concurrently in the network

Single Copy
Multiple Copy
lower number of transmission lower contention
for shared resources
lower delivery delay higher robustness
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Outline

• Single-copy strategies
• design space
• Seek and Focus
• performance analysis
• simulations
• Multiple-copy schemes
• comparison to single-copy
• existing flooding and utility-based schemes
• Spray and Wait
• performance analysis
• simulations

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Direct Transmission

• Forward message only to its destination
• simplest strategy
• Its expected delay is an upper bound for every
  other protocol.

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Randomized Routing

• Node A forwards message to node B with
  probability p
• P(B closer to destination D than A) P(A closer
  to D than B)
• yet, because transmission speed is faster than
  the speed of movement it can be shown that

Result The randomized policy results in a
reduction of the expected hitting time to
destination at every step
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Utility-based Routing

• Destinations location (relative to another
  nodes location) gets indirectly logged in timer
  during encounter
• Location info gets diffused through mobility
  process
• Define an appropriate utility function UX(Y)
  based on timer value TX(Y)
• e.g. UX(Y) - expected hitting time given timer
  value
• Utility-based routing
• Node A forwards a message for node D to
  node B iff UA(D) lt UB(D)

• Now, if TB(D) lt TA(D),
• PBA P(B closer to D than A) gt P(A closer
  to D than B)

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Utility-based Routing (contd)
ETD
EATD ET(d)
d
A
B
B
Result 1 Utility-based routing has a larger
expected delay reduction than the simple
randomized policy
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Randomized vs. Utility-based Routing

• Randomized strategy
• transmissions are faster than movement
• - many transmissions for marginal gain (forwards
  message blindly)
• Utility-based strategy
• takes advantages of indirect location info to
  make better forwarding decisions
• - slow start In a large network, source and
  destination are far gt all nodes around source
  have very low utility gt takes a long time until
  a good next hop is found initially

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Seek and FocusA Hybrid Routing Strategy
IDEA Avoid the slow start phase of
utility-based schemes, while still taking
advantage of the higher efficiency of
utility-based forwarding

• Seek phase If utility around node is low,
  perform randomized forwarding to quickly search
  nearby nodes
• Focus phase When a high utility node (i.e. above
  a threshold) is discovered, switch to
  utility-based forwarding
• look for a good lead to the destination and
  follow it

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Oracle-based Optimal Algorithm

• Assume all nodes trajectories (future movements)
  are known
• Then, the algorithm picks the sequence of
  forwarding decisions that minimizes delay
• Note that flooding (multi-copy strategy) has the
  same delay as this algorithm when there is no
  contention

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Performance analysis

• Compute expected delivery delay (ED)
• Assumptions
• mobility model random walk on grid (torus)
• there is no contention in the wireless channel
• Notation
• EXTY expected hitting time from X to Y
• ET expected hitting time from stationary
  distribution
• (indep. of specific position for symmetric graph)

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Direct Transmission K 0

• ED ET
• Hitting time distribution approximately
  exponential
• Results from D. Aldous and J. Fill Reversible
  Markov chains and random walks on graphs

- - ET ?(NlogN)
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Direct Transmission K gt 0

• 1) EDdt EXTA

• 2) EXTA EXTY - EATY
• EXTY cNLogN

K 3
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Oracle-based Optimal Algorithm

• M nodes
• Tx Range K

D
S
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Randomized Algorithm
Probability q Tx jump
q
p
P(at least one node within range)
f(K) average transmission distance
Probability 1-q Random walk
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Randomized Algorithm (contd)

• Approximate actual message movement with a random
  walk performing D independent 1-step moves at
  each time slot
• Note This walk is slower than the actual walk
• would reach destination later, on the average
• Define an appropriate martingale to show that

Destination movement
Message movement
Note D 1 2 ? randomized is faster than
direct transmission!
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Simulation vs. Analysis
upper bound
lower bound

• Simulation and theoretical results are closely
  matched
• Randomized algorithm is efficient for large K

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Simulations with contention

• Simulated schemes
• Randomized with probability p 0.5
• Randomized with probability p 1.0
• Utility-based routing
• Seek and Focus (with probability p 0.5 in seek
  phase)
• Seek and Focus (with probability p 1.0 in seek
  phase)
• Direct transmission
• Used a simple collision avoidance MAC protocol to
  handle contention

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Scenario 1 (random walk, small network)

• 50x50 grid, 20 nodes, transmission range 5
• Only 1 message is routed between two randomly
  chosen nodes

Randomized (p 0.5)
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Seek and Focus (p 0.5)
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2
Randomized (p 1.0)
5
Seek and Focus (p 1.0)
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Utility-based
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Direct
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Scenario 2 (random walk, large network)

• 500x500 grid, 50 nodes, transmission range 60
• 50 messages are routed between randomly chosen
  nodes

Randomized (p 0.5)
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Seek and Focus (p 0.5)
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2
Randomized (p 1.0)
5
Seek and Focus (p 1.0)
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Utility-based
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Scenario 3 (random waypoint)

• 500x500 grid, 50 nodes, transmission range 20
• 50 messages are routed between randomly chosen
  nodes

Randomized (p 0.5)
4
Seek and Focus (p 0.5)
1
2
Randomized (p 1.0)
5
Seek and Focus (p 1.0)
3
Utility-based
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Outline

• Single-copy strategies
• design space
• Seek and Focus
• performance analysis
• simulations
• Multiple-copy schemes
• comparison to single-copy
• existing flooding and utility-based schemes
• Spray and Wait
• performance analysis
• simulations

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Multiple-copy vs. single-copy Routing

• Higher robustness
• Low delivery delay
• - Higher number of transmissions
• - Contention for shared resources

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Flooding-based and Utility-based Schemes

• Epidemic Routing (flooding) handover a copy to
  everyone
• minimum delay under no contention
• Randomized Flooding (Y. Tseng et al. 02)
  handover a copy with probability p
• Utility-based Flooding (A. Lindgren et al. 03)
  handover a copy to a node with a utility at least
  Uth higher than current
• Constrained Utility-based Flooding like
  previous, but may only forward a bounded number
  of copies of the same message

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Shortcomings

• Flooding
• too many transmissions (energy-efficiency
  concerns)
• unbounded number of copies per message
  (scalability issues)
• under high traffic, high contention for buffer
  space and bandwidth results in poor performance
• Utility-based
• high Uth significant delay increase source
  takes a very long time until it finds a good next
  hop (slow start)
• low Uth degenerates to flooding

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Spray and Wait

• Performance goals
• significantly reduce transmissions by bounding
  the total number of copies/transmissions per
  message
• under low traffic minimal penalty on delay
  (close to optimal)
• under high traffic reduce the delay of existing
  flooding- and utility-based schemes thanks to
  less contention
• 2 phases
• Spray phase spread L message copies to L
  distinct relays
• Wait phase wait until one of the L relays
  finds the destination (i.e. use direct
  transmission)

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Spray and Wait Variations

• Source Spray and Wait
• Source starts with L copies
• whenever it encounters a new node, it hands one
  of the L copies
• this is the slowest among all (opportunistic)
  spraying schemes
• Optimal Spray and Wait
• source starts with L copies
• whenever a node with n gt 1 copies finds a new
  node, it hands half of the copies that it carries
• optimal spreads the L copies faster than any
  other spraying scheme

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Performance analysis

• Compute ED, the expected delivery delay
• Assumptions
• mobility model random walk on grid
• there is no contention in the wireless channel
• Recall that EDdt denotes the expected delivery
  delay of direct transmission

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Source Spray and Wait

• Let ED(i) denote the expected remaining delay
  after i copies are spread
• Clearly EDsrc ED(1)
• ED(1) can be calculated through a system of
  recursive equations

If destination, stop

• A similar recursion procedure gives the delay of
  Optimal Spray and Wait

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Upper bound

• Exact delay not in closed form
• Derive a bound in closed form
• This is an upper bound for any Spray and Wait
  algorithm

Probability a wait phase is needed
Wait Phase
Spray Phase
Bound is tight for LltltM
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Simulation vs. Analysis
(analysis)

• Good match between theory and simulations
• Spray and Wait achieves a delay only 1.5-2 times
  that of the optimal

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Simulation vs. Analysis (contd)
(analysis)
Efficient spraying becomes more important for
large L
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Simulations (with contention, waypoint model)

• Simulated schemes
• Epidemic routing
• Randomized flooding (p 0.03)
• Utility-based flooding (Uth 0.02)
• Constrained utility-based flooding
• Source Spray and Wait (L 10)
• Optimal Spray and Wait (L 10)
• Seek and Focus
• Oracle-based optimal algorithm
• Same collision avoidance MAC protocol and utility
  function as before

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Scenario A (low traffic)
500x500, M 50 nodes, K 20

• Spray and Wait
• performs 60-97 less transmissions (even less
  than seek and focus)
• achieves a lower delay than utility-based schemes
  that is about twice that of the optimal

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Scenario B (high traffic)
500x500, M 50 nodes, K 20 (6 coverage), 40
(25 coverage)

• Spray and Wait achieves up to an order of
  magnitude reduction in number of transmission
  compared to flooding and utility-based schemes
• and a delivery delay lower than all other schemes

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Conclusions

• Seek and Focus
• yields the best tradeoff between delay and number
  of transmissions among single-copy schemes
• Spray and Wait
• is as energy efficient as single-copy schemes
• yields lower delay than existing flooding- and
  utility-based schemes, and
• this delay is within a factor of 2 from that of
  optimal

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Future Work

• Analysis of utility-based schemes
• Analysis under contention
• Explore hybrid schemes where
• L copies are spread initially
• Each copy is routed using some efficient
  single-copy scheme (e.g. utility-based
  single-copy routing)
• Performance of all protocols under more realistic
  mobility models that exhibit correlation in space
  and/or time
• Capacity Analysis

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References

• A. Spyropoulos, K.Psounis, and C. Raghavendra.
  Single-copy routing in intermittently connected
  mobile networks. CENG-2004-11 Technical Report,
  University of Southern California, June 2004. in
  IEEE SECON 04.
• A. Spyropoulos, K.Psounis, and C. Raghavendra.
  Multi-copy routing in intermittently connected
  mobile networks. CENG-2004-12 Technical Report,
  University of Southern California, June 2004.