Gossiping: Adaptive and Reliable Broadcasting in MANETs

© Neeraj SuriEU-NSF ICT March 2006

Dependable Embedded Systems & SW Group www.deeds.informatik.tu-darmstadt.de

Gossiping: Adaptive and Reliable Broadcasting in MANETs

Abdelmajid Khelil & Neeraj Suri

LADC’07, Morelia, Mexico

© A. Khelil 2

Motivation

Ad hoc communication WLAN, Bluetooth, ZigBee, WiMax ..

802.11{a,b,g,p}802.15.{1,3,4}

802.16{a,e} IEE

E

802.11{a,b,g,p}802.15.{1,3,4}

802.16{a,e}IEE

E

Main characteristics Hop-by-hop communication Node mobility Limited resources (energy, processing,

storage etc.)

Mobile Ad Hoc Networks (MANET) Diversity of application scenarios

Rescue, military scenarios Vehicle ad hoc network, and many

others. 10

10

1

1

00

11

10

10

1

1

0 01

1

10

10

1

1

0 01

1

© A. Khelil 3

Motivation (cont.)

A MANET may show Frequent perturbations

• Continuously changing network Continuously changing network topologytopology

• Comm. failures, power ...Comm. failures, power ... Strong heterogeneity

• Node spatial distributionNode spatial distribution• Node movementNode movement

Evolving properties• Temporal (daytime ..) Temporal (daytime ..) • Technological (deployment stages ..)Technological (deployment stages ..)

velocity

com. range

© A. Khelil 4

Outline

Problem Statement Related Work System and Fault Model Epidemic Model for Gossiping Adaptation of Gossiping Evaluation

© A. Khelil 5

Flooding encounters one main problem: Broadcast storms, i.e.,

• Collision,Collision,• Contention, and Contention, and • Unnecessary forwards.Unnecessary forwards.

Problem Statement

Restrict Forwarding Gossiping: Nodes forward messages with

a certain probability p

How should nodes select the forwarding probability p?

com. range

A

B

Broadcasting is widely used in MANETs Flooding is a common approach

• Nodes forward messages to all Nodes forward messages to all neighbors, using MAC broadcastneighbors, using MAC broadcast

source

Plain flooding

p(A) low!

p(B) high!

© A. Khelil 6

Related Work - Classification

broadcast in MANET

plainflooding

probability-based

area-based

topology-based

probability-based(gossip)

counter-based

distance-based

location-based

imposed-decision

local-decision

self-pruning

LENWB

SBA dominant-pruning

multipoint-relay

CDS-based

AHBP

heuristic-based energy-efficient

directional-antenna-

based

cluster-based

transmission-power-based

scopedflooding

DCB

© A. Khelil 7

Related Work – in Density-Mobility-Space

DENSITY

MOBILITY

restrict forwarding

Energy-efficient

Topology-based

Heuristic-based

Adaptive counter-based

Broadcast-in-space

plainflooding

probability-based

area-based

topology-based

gossip

counter-based

distance-based

location-based

imposed-decision

local-decision

self-pruning

LENWB

SBA dominant-pruning

multipoint-relay

CDS-based

AHBP

heuristic-based energy-efficient

directional-antenna-

based

cluster-based

transmission-power-based

scopedflooding

DCB

comm. range

ACBAdaptive probabilistic

STOCH-FLOOD

Adaptation purely relies on

simulations!Two comparative studies:- Gerla et al.: Efficient flooding in ad hoc networks: A comparative performance study. In ICC’03.- Williams et al.: Comparison of broadcasting techniques for mobile ad hoc networks. In Mobihoc’02.

© A. Khelil 8

System and Fault Model

A generalized MANET scenario N mobile nodes populating a fixed area A

(node density: d=N/A) Heterogeneous and evolving

• Node spatial distribution Node spatial distribution • Node mobilityNode mobility

Nodes do not need• Location / velocity informationLocation / velocity information

HELLO beaconing to acquire neighborhood information Messages are uniquely identified Failures

Communication: Collision, contention and frequent link breakage.

Topology: Continuous change.

A

comm. range

velocity

© A. Khelil 9

Epidemic Model for Gossiping

Broadcast in MANETs

Broadcast protocol

Sus

cept

ible

Infe

ctiv

eS I

fitting

Fitting

time [s]

#Rea

ched

/N

- Protocol: SPIN - Random waypoint - N=100

timeNaeN

N

)1(1

Reached#

“Infection” rate a

simulation

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

time

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

0.24

1.030

0.920

0.89

0.78.5

0.68

0.57.5

0.47

0.36

0.10

Spreading ratio

Time point

#Rea

ched

N0 N1N2 N3

Spread of infectious disease

analytical

timeNaeN

N

)1(1

Infected#

Infection rate a

#Individuals: N

Contact pattern

Sus

cept

ible

Infe

ctiv

eS IInfection transmission

#Nodes: N

Movement pattern

© A. Khelil 10

a(d,p)

Adaptation of Gossiping to Node Density

Compute infection rate a(d,p) for Different node densities d in [50,800]

km-2

• Uniform node distributionUniform node distribution• Fixed comm. range (Fixed comm. range (100m)100m)

Different probabilities p in ]0,1] • All nodes use the same All nodes use the same pp

Loca

lizat

ion

&

Inte

rpol

atio

nIn

fect

ion

rate

max

imiz

atio

n

Determination of optimal probability: For a given node density d0 ,

find p such that a(d0,p) is maximal

Nodes set p depending on #Neighbors

Adaptive gossiping

ST

EP

1S

TE

P 2

ST

EP

3

#Neighbors

Node density d (km-2)

Op

tim

al p

© A. Khelil 11

Simulation Parameters

ns-2 simulator

ValuesParameterArea 1km x 1kmNumber of nodes N = 50 .. 500

Communication range 100 mBandwidth r = 1 MbpsMessage size 280 Bytes

Mobility model Random waypoint- Max speed vmax = 3 .. 30 m/s

- Pause 0 .. 2 s

HELLO beaconing interval Random in [0.75 , 1.25] s

Number of senders 25Packet rate 0.001 pkt/s

Forwarding delay Random in [0 , 10] msSimulation runs 10

Group- & graph-based mobility also considered

CSMA/CAMAC layer

- Collision- Contention

- Frequent link breakage- Continuous topology change

© A. Khelil 12

Reachability = #Reached_Nodes / #Total_Nodes

High reachability

© A. Khelil 13

Average Number of Partitions

Network partitioning

© A. Khelil 14

Reliability of Adaptive Gossiping (1)

Comparison to the

optimal case

© A. Khelil 15

Reliability of Adaptive Gossiping (2)

Gossip reaches either

almost all nodes or

only the source

© A. Khelil 17

Comparison to Related Work: Tunable Thresholds

- ACB stops to adapt after 12 neigh-Gossiping saves more forwards till 30 neigh

© A. Khelil 21

Conclusions

Adaptive Gossiping provides for efficient, scalable and reliable broadcast for a wide range of node densities and mobilities: Easy to use on a wide range of resource-limited devices Adaptation of forward probability is independent from cause of

changes in node density:• Application scenarios,Application scenarios,• Node mobility,Node mobility,• Deployment stages,Deployment stages,• Technology penetration rate,Technology penetration rate,• On-off usage, etc.On-off usage, etc.

Extensions Broadcast repetition to cope with network disconnections

• Broadcast extinction at the source, Broadcast extinction at the source, • Network partitioning, Network partitioning, • Reboot, etc.Reboot, etc.

© Neeraj SuriEU-NSF ICT March 2006

Dependable Embedded Systems & SW Group www.deeds.informatik.tu-darmstadt.de

Thanks for your attention!

Abdelmajid Khelil and Neeraj SuriDepartment of Computer Science

TU Darmstadt, Germany

{khelil, suri}@informatik.tu-darmstadt.de

Gossiping: Adaptive and Reliable Broadcasting in MANETs

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