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

Sara Omar al-Humoud

END’s Talk

Outline

• Introduction

• Cbase

• Mobility Models

• RWP

• MMM

• Results

• Future Direction

Research Outline

Routing

Broadcasting

Wireless MANET

Flooding

Probabilistic Deterministic

ACBase1

Introduction

Related

work

Contribution ACBase2

Counter Base Broadcast

• 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 > C.

Scheme

Get the Broadcast IDGet degree n of node X

c = 1

n < avg

C = c1Tmax = Tmax1

C = c2Tmax = Tmax2

Set RAD [0..Tmax]

While (RAD)

same packet heard

c = c + 1

End while

C < c

drop packetTrans packet

Get the Broadcast IDc = 1

Set RAD [0..Tmax]

While (RAD)

same packet heard

c = c + 1

End while

C < c

drop packetTrans packet

Get the Broadcast ID

same packet heard

drop packetTrans packet

Adjusted Counter-based1

Comparison

Flow charts

between

Counter-based Flooding

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Adjusted Counter-Based Broadcast

• 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

ACBase1 Scheme

Avg

Sparse DenseNeighbourhoodDensity

Outline

• Introduction

• Cbase

• Mobility Models

• RWP

• MMM

• Results

• Future Direction

Mobility Models

• Traces

• Synthetic Model

– Entity

– Group

MobiLib Dartmouth

Random Way Point Mobility Model

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.

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RWP

25 nodes

Manhattan Mobility Model

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

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MMM

25 nodes (3x3 street)

MANHATTANHOR_STREET_NUM 3VER_STREET_NUM 3LANE_NUM 12

Outline

• Introduction

• Cbase

• Mobility Models

• RWP

• MMM

• Results

• Future Direction

Prameters

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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 SpeedMax = 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.

Results

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SRB

Results

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Reachability

Results

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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

• 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

Broadcasting Applications

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Introduction

• Flooding may lead to– Redundancy

x Consume limited bandwidth

– Contentionx Increase in delay

– Collisionx High packet loss rate

– Broadcast storm problem!

Broadcasting Applications

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7 8 9 10

Nu

mb

er

of M

es

sa

ge

s

Number of Nodes

f(n) = n2 – 2n + 1

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Related work

• 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

node’s range that is yet to be covered by the broadcast > Ath

• Distance-based– Rebroadcast if the node did not receive

the msg from another node at a distance less than Dth

Probabilistic Broadcasting Methods

Receiver rebroadcast

decision

Simple Implementation RD based on instantaneous information from broadcast msgs

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Related work

• Reliable Broadcast

• Self-pruning

• Scalable broadcasting

• Dominant Pruning

• Cluster-based

Deterministic Broadcasting Methods

Sender rebroadcast

decision

Elaborate Implementation Rebroadcast decision based on neighbourhood study

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Related work

1. Counter-based broadcast

– Adaptive Counter-based broadcast [Tseng2003]

– Adjusted Counter-Based [Aminu2007]

2. Color-based broadcast [Haddad 2006]

3. Distance-aware counter-based broadcast

[Chen 2005]

Counter-Based related Broadcasting Methods

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Questions

• 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: It’s Time for REAL

Mobility Models, ACM's Mobile Computing and Communications Review, to appear 2008

Towards a better simulation

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Questions

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

Towards a better simulation

iNSpect

NS-2Mobility files Trace files

OpenGL animation

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Questions

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

Towards a better simulation

SCORES

Num nodesNode coverage

Simulation area

Transmission range

Nw diameter

Neighbor count

Foot print

…

Mobility file topology change rate

Delivery ratio, end-to-end delay, throughput, overheadMetamodels

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Questions