Using Manhattan Mobility Model for the Counter-Base Broadcasting protocol in MANETs

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Using Manhattan Mobility Model for the
Counter-Base Broadcasting protocol in MANETs
ENDs Talk

• Sara Omar al-Humoud

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Outline

• Introduction
• Cbase
• Mobility Models
• RWP
• MMM
• Results
• Future Direction

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Research Outline
ACBase2
ACBase1
Contribution
Related work
Probabilistic
Deterministic
Flooding
Broadcasting
Introduction
Routing
Wireless MANET
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Counter Base Broadcast
Scheme

• When receiving a message
• counter c is set to keep track of number of
  duplicate messages received.
• Random Assessment Delay (RAD) timer is set.
• When the RAD timer expires the counter is tested
  against a fixed threshold value C, broadcast is
  inhibited if c gt C.

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Adjusted Counter-based1
Comparison Flow charts between Counter-base
d
Flooding
Get the Broadcast ID Get degree n of node X c 1
Get the Broadcast ID c 1 Set RAD 0..Tmax
Get the Broadcast ID
n lt avg
C c1 Tmax Tmax1
C c2 Tmax Tmax2
Set RAD 0..Tmax
While (RAD)
While (RAD)
same packet heard
same packet heard
same packet heard
c c 1
c c 1
End while
End while
C lt c
C lt c
drop packet
Trans packet
drop packet
Trans packet
drop packet
Trans packet
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Adjusted Counter-Based Broadcast
ACBase1 Scheme

• Adjusted Counter-Based Broadcast
• Based on the original counter-based scheme
• Add the ability to decide the counter and the RAD
  according to neighbourhood density
• Neighbourhood density is divided according to the
  Average number of neighbours into
• Density1 Sparse
• Density2 Dense

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Outline

• Introduction
• Cbase
• Mobility Models
• RWP
• MMM
• Results
• Future Direction

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Mobility Models

• Traces
• Synthetic Model
• Entity
• Group

Dartmouth
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Random Way Point Mobility Model
RWP

• How it works
• at every instant, a node randomly chooses a
  destination and moves towards it with a velocity
  chosen randomly from 0, Vmax, where Vmax is the
  maximum allowable velocity for every mobile node.

25 nodes
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Manhattan Mobility Model
MMM

• How it works
• A node is allowed to move along the grid of
  horizontal and vertical streets on the map.
• At an intersection the node can turn left, right
  or go straight.
• P of same street 0.5
• P of turning left 0.25
• P of turning right 0.25

MANHATTAN HOR_STREET_NUM 3 VER_STREET_NUM
3 LANE_NUM 12
25 nodes (3x3 street)
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Outline

• Introduction
• Cbase
• Mobility Models
• RWP
• MMM
• Results
• Future Direction

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Prameters
Simulation parameters
Simulation parameter Value
Simulator ns-2 version (2.33)
Network Area 1000 x 1000 meter
Transmission range 250 meter
Data Packet Size 256 bytes
Node Max. IFQ Length 50
Simulation Time 900 sec
Pause Times 0 sec
Number of Trials 30
MAC layer protocol IEEE 802.11
Mobility model Random waypoint model, Manhattan Mobility Model
Channel Bandwidth 2Mb/sec
Confidence Interval 95
Packet Rate 2 packets per sec
Node Speed Max 30 km_per_hour Min 5 km_per_hour
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Performance metrics

• Saved Rebroadcast (SRB)
• (r - t)/r
• r number of hosts receiving the broadcast
  message
• t number of hosts that actually transmitted the
  message.
• Reachability
• r/e
• r number of hosts receiving the broadcast
  packet
• e number of mobile hosts that are reachable,
  directly or indirectly, from the source host .
• Average latency
• the interval from the time the broadcast was
  initiated to the time the last host finished its
  rebroadcasting.

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Results
SRB
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Results
Reachability
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Results
Average Latency
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Future Directions

• MMM
• Limiting the number of nodes (cars) in a lane
• Building a bigger map (Glasgow cc)
• Scripting a mobility map generator
• Develop the ACBase2 that calculates the threshold
  value according to a function of the number of
  neighbours

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Questions
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Introduction
Broadcasting Applications

• Discovering neighbours
• Collecting global information
• Addressing
• Helping in multicasting and Unicast
• Route discovery, route reply
• in on-demand routing protocols like DSR, AODV to
  broadcast control messages.
• Conventionally broadcast is done through flooding

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Introduction
Broadcasting Applications

• Flooding may lead to
• Redundancy
• x Consume limited bandwidth
• Contention
• x Increase in delay
• Collision
• x High packet loss rate
• Broadcast storm problem!

f(n) n2 2n 1
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Related work
Probabilistic Broadcasting Methods

• Probability-based
• Rebroadcast with probability P
• Counter-based
• Rebroadcast if the node received less than Cth
  copies of the msg
• Location-based
• Rebroadcast if the area within the nodes range
  that is yet to be covered by the broadcast gt Ath
• Distance-based
• Rebroadcast if the node did not receive the msg
  from another node at a distance less than Dth

Receiver rebroadcast decision
Simple Implementation RD based on instantaneous
information from broadcast msgs
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Related work
Deterministic Broadcasting Methods

• Reliable Broadcast
• Self-pruning
• Scalable broadcasting
• Dominant Pruning
• Cluster-based

Sender rebroadcast decision
Elaborate Implementation Rebroadcast decision
based on neighbourhood study
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Related work
Counter-Based related Broadcasting Methods

• Counter-based broadcast
• Adaptive Counter-based broadcast Tseng2003
• Adjusted Counter-Based Aminu2007
• Color-based broadcast Haddad 2006
• Distance-aware counter-based broadcast Chen 2005

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Related work
Counter-Based related Broadcasting Methods

• 2- Color-based broadcast
• Scheme
• each broadcast node selects a color from a set of
  ? colors which it writes to a color-field present
  in the broadcast message.
• all nodes which hear the message rebroadcast it
  unless they have heard all ? colors by the time a
  random timer expires.
• Remarks
• With the added overhead, we may end with a bad
  case
• E.g. a node receive 3 messages with only c1 and
  this node will still rebroadcast the message.

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c1 c2 c3
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Related work
Counter-Based related Broadcasting Methods

• 3- Distance-aware counter-based broadcast
• Scheme
• Similar to the counter-based scheme in addition
  to
• Two distinct RADs are applied to the border and
  interior nodes
• SRAD to border nodes
• LRAD to interior nodes
• Remarks
• The use of distance as an enhancement factor to
  the original counter-based may be degraded
  knowing that real networks transmissions will be
  affected by obstacles.

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Motivations and objectives
Related work limitations - overhead

• Area-based scheme
• Rely on GPS
• Deterministic approaches
• High time overhead
• High number of control messages exchanged to
  broadcast one packet
• it demands accurate neighbourhood information and
  cannot ensure the coverage with outdated topology
  information.

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Motivations and objectives
Related work limitations - overhead

• Counter-based schemes
• Fixed counter-based
• Threshold c
• Adaptive counter-based
• Threshold C(n) where n is the number of
  neighbors
• The function C(n) is undefined yet
• Color-based
• Used with homogeneous density networks
• Rebroadcast when many duplicates received by the
  a partial set of colors
• Distance-aware counter-based
• Distance estimated by signal strength
• Not considering obstacle existence

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Contributions
Assumptions

• Simulate a university campus with the following
  assumptions
• Existence of pedestrians and vehicles equipped
  with IEEE 802.11 wireless transceivers
• Speed
• walk speed of 1 m/sec with appropriate pose times
  to vehicles having a maximum speed of 70 km/hour
  
• Area
• First study open unobstructed
• Second study open with obstacles
• Mobility
• First study Random way point (RWP) mobility
  model
• Second study Realistic Mobility Model
• We assume that a host can detect duplicate
  broadcast messages.
• Assume that nodes have sufficient power to
  function properly throughout the simulation time

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Contributions
Simulation parameters
Simulation parameter Value
Simulator ns-2 version (2.31)
Network Area 1500 x 500 meter
Transmission range 250 meter
Data Packet Size 256 bytes
Node Max. IFQ Length 50
Simulation Time 500 sec
Pause Times 0, 10, 20, 40 sec
Number of Trials 10
MAC layer protocol IEEE 802.11
Mobility model Random waypoint model, Realistic Mobility Model
Channel Bandwidth 2Mb/sec
Confidence Interval 95
Number of Nodes 20, 40, 50, 60, 80, 100
Packet Rate 10, 20, 40, 60, 80 100, 150 packets per sec
Counter threshold pairs 2,3, 2,4, 3,4
Max RADs 0.005, 0.01, 0.05, 0.01
Node Max. Speed 1, 5, 10, 15, 20 m/sec
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Questions
Towards a better simulation

• Is there a realistic mobility model?
• Obstacle Mobility Model Project 2005
• Ns2, GlomoSim
• the Mobility Management and Networking (MOMENT)
  Lab,
• the Networking and Multimedia Systems Lab (NMSL)
• and the Geometric Computing Lab (GCL).
• University of Califorrnia at Santa Barbara
• RealMobGen 2008
• Ns2
• Dartmouth's and University of Southern
  California's
• C. Walsh, A. Doci, and T. Camp, A Call to Arms
  Its Time for REAL Mobility Models, ACM's Mobile
  Computing and Communications Review, to appear
  2008

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Questions
Towards a better simulation

• Is there a visualisation tool to view network
  topology?
• iNSpect

NS-2
Trace files
Mobility files
OpenGL animation
iNSpect
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Questions
Towards a better simulation

• How to validate and compare scenarios?
• SCORES tool (SCenariO characteRizEr for
  Simulation)

SCORES
Node coverage
Num nodes
Nw diameter
Simulation area
Neighbor count
Transmission range
Foot print

Mobility file
topology change rate
Delivery ratio, end-to-end delay, throughput,
overhead
Metamodels
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Questions