Gossiping: Adaptive and Reliable Broadcasting in
MANETs
Abdelmajid Khelil & Neeraj Suri
LADC’07, Morelia, Mexico
Dependable Embedded Systems & SW Group
© Neeraj Suri
www.deeds.informatik.tu-darmstadt.de
EU-NSF ICT March 2006
 WLAN, Bluetooth, ZigBee, WiMax ..
 Mobile Ad Hoc Networks (MANET)
 Diversity of application scenarios
 Rescue, military scenarios
 Vehicle ad hoc network, and many others.
 Main characteristics
 Hop-by-hop communication
 Node mobility
 Limited resources (energy, processing,
storage etc.)
© A. Khelil
2
802.11{a,b,g,p}
802.15.{1,3,4}
802.16{a,e}
IEEE
 Ad hoc communication
IEEE
Motivation
802.11{a,b,g,p}
802.15.{1,3,4}
802.16{a,e}
Motivation (cont.)
 A MANET may show
 Frequent perturbations
• Continuously changing network topology
• Comm. failures, power ...
 Strong heterogeneity
• Node spatial distribution
• Node movement
 Evolving properties
• Temporal (daytime ..)
• Technological (deployment stages ..)
© A. Khelil
3
com.
range
Outline
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© A. Khelil
Problem Statement
Related Work
System and Fault Model
Epidemic Model for Gossiping
Adaptation of Gossiping
Evaluation
4
Problem Statement
 Broadcasting is widely used in MANETs
 Flooding is a common approach
• Nodes forward messages to all
neighbors, using MAC broadcast
source
 Flooding encounters one main problem:
 Broadcast storms, i.e.,
• Collision,
• Contention, and
• Unnecessary forwards.
Plain flooding
A
p(A) low!
 Restrict Forwarding
B
 Gossiping: Nodes forward messages with a
certain probability p
 How should nodes select the forwarding
probability p?
© A. Khelil
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p(B) high!
com.
range
Related Work - Classification
broadcast in MANET
heuristic-based
plain
flooding
probabilitybased
probabilitybased
(gossip)
counterbased
© A. Khelil
topology-based
areabased
localdecision
imposeddecision
energy-efficient
transmissionpower-based
distancebased
self-pruning
multipoint-relay
locationbased
SBA
dominant-pruning
scoped
flooding
AHBP
LENWB
cluster-based
CDS-based
DCB
6
directionalantennabased
Related Work – in Density-Mobility-Space
comm.
range
DENSITY
Energy-efficient
Topology-based
Broadcast-in-space
heuristic-based
MOBILITY
Heuristic-based
plain
flooding
Adaptive counter-based
Adaptive probabilistic
probabilitybased
topology-based
areabased
localdecision
imposeddecision
energy-efficient
transmissionpower-based
gossip
distancebased
self-pruning
multipoint-relay
counterbased
locationbased
SBA
dominant-pruning
scoped
flooding
AHBP
LENWB
cluster-based
CDS-based
DCB
directionalantennabased
ACB
STOCH-FLOOD
restrict forwarding
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
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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 mobility
 Nodes do not need
• Location / velocity information
 HELLO beaconing to acquire neighborhood information
 Messages are uniquely identified
 Failures
 Communication: Collision, contention and frequent link breakage.
 Topology: Continuous change.
© A. Khelil
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A
comm.
range
Epidemic Model for Gossiping
Broadcast in MANETs
I
analytical
N
1  ( N  1)  e a N time
Infection
rate a
Broadcast protocol
simulation
- Protocol: SPIN
- Random waypoint
- N=100
Fittin
g
time [s]
© A. Khelil
I
N0 N1
fitting
#Reached/N
# Infected 
S
9
N2
N3
Time
Spreading
Time
point Spreading
ratio
Time
point Spreading
ratio
0 Spreading0.1
Time
point0
ratio0.1
0.2
point0 4 ratio
0.1
time
Infection transmission
S
Infective
Movement pattern
Susceptible
Contact pattern
Infective
#Nodes: N
Susceptible
#Individuals: N
4
6
0 4
6
7
4 6
7
7.5
6 7
7.5
8
7 7.5
8
8.5
7.5 8
8.5
9
8 8.5
9
20
8.5 9
20
9 20 30
30
20 30
30
0.2
0.10.2 0.3
0.3
0.20.3 0.4
0.4
0.30.4 0.5
0.5
0.40.5 0.6
0.6
0.50.6 0.7
0.7
0.60.7 0.8
0.8
0.70.8 0.9
0.9
0.80.9 1.0
1.0
0.91.0
1.0
#Reached
Spread of infectious disease
“Infection”
rate a
N
# Reached 
1  ( N  1)  e a N time
Adaptation of Gossiping to Node Density
 Compute infection rate a(d,p) for
STEP 3
 Determination of optimal
probability:
Optimal p
STEP 2
#Neighbors
For a given node density d0 ,
find p such that a(d0,p) is maximal
Node density d (km-2)
 Nodes set p depending
on #Neighbors
 Adaptive gossiping
© A. Khelil
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Localization &
Interpolation
STEP 1
 Different node densities d in [50,800]
km-2
• Uniform node distribution
• Fixed comm. range (100m)
 Different probabilities p in ]0,1]
• All nodes use the same p
Infection rate
maximization
a(d,p)
Simulation Parameters
Parameter
Area
Number of nodes
1km x 1km
N = 50 .. 500
Mobility model
- Max speed
- Pause
Random waypoint
vmax = 3 .. 30 m/s
0 .. 2 s
Number of senders
Packet rate
Forwarding delay
Simulation runs
25
0.001 pkt/s
Random in [0 , 10] ms
10
MAC layer
Communication range
Bandwidth
Message size
CSMA/CA
100 m
r = 1 Mbps
280 Bytes
HELLO beaconing interval
Random in [0.75 , 1.25] s
- Collision
- Contention
© A. Khelil
Values
- Frequent link breakage
- Continuous topology change
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Group- & graph-based
mobility also considered
ns-2 simulator
Reachability = #Reached_Nodes / #Total_Nodes
High
reachability
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Average Number of Partitions
Network
partitioning
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Reliability of Adaptive Gossiping (1)
Comparison
to the
optimal case
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Reliability of Adaptive Gossiping (2)
Gossip reaches
either
almost all nodes
or
only the source
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MNF: Mean Number of Forwards per Node & per Msg
Max MNF: 1.0
High
efficiency
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Comparison to Related Work: Tunable Thresholds
- ACB stops
to adapt after 12
neigh
-Gossiping saves
more forwards till
30 neigh
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Comparison to Related Work: Reachability
Node speed: 3 m/s
Comparably
high
reachability
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Comparison to Related Work: MNF
Plain
flooding
Node speed: 3 m/s
Highest
efficiency
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Comparison to Related Work: End2End Delay
Node speed: 3 m/s
Lowest delay
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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,
Node mobility,
Deployment stages,
Technology penetration rate,
On-off usage, etc.
 Extensions
 Broadcast repetition to cope with network disconnections
• Broadcast extinction at the source,
• Network partitioning,
• Reboot, etc.
© A. Khelil
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Thanks for your attention!
Abdelmajid Khelil and Neeraj Suri
Department of Computer Science
TU Darmstadt, Germany
{khelil, suri}@informatik.tu-darmstadt.de
Dependable Embedded Systems & SW Group
© Neeraj Suri
www.deeds.informatik.tu-darmstadt.de
EU-NSF ICT March 2006