Transcript Document 7843454

Universität Stuttgart
Institute of Parallel and
Distributed Systems (IPVS)
Universitätsstraße 38
D-70569 Stuttgart
Hypergossiping: A Generalized Broadcast
Strategy for Mobile Ad Hoc Networks
A. Khelil, P.J. Marrón, C. Becker, K. Rothermel
Overview
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•
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Motivation
Related Work
System Model
Hypergossiping
Evaluation
Conclusion and Future Work
Research Group
“Distributed Systems”
Universität Stuttgart
2
IPVS
Motivation (1)
• Ad hoc communication
◦ WLAN, Bluetooth, UMTS (UTRA-TDD)
 Mobile Ad Hoc Networks (MANET)
• Examples of applications
◦ Vehicle ad hoc network
◦ Rescue scenarios
• MANETs may show
◦ significant variation in node spatial distribution
◦ significant variation in node movement
• Broadcasting is widely used in MANETs
◦ Flooding is a common approach
Research Group
“Distributed Systems”
Universität Stuttgart
3
IPVS
Motivation (2)
• Flooding encounters two main problems:
◦ In dense MANETs: broadcast storms
▪ Collision, contention and redundancy
◦ In sparse MANETs: network partitioning
▪ Flooding reaches only nodes of one partition
• Gossiping is probabilistic flooding
◦ Nodes forward messages with a certain probability to all neighbors,
using MAC broadcast
◦ Variation in node density  we adapted gossip probability to number of
neighbors  reduces broadcast storms
◦ Gossip still reaches only nodes of one partition
•  Broadcast repetition strategy is needed
Research Group
“Distributed Systems”
Universität Stuttgart
4
IPVS
Overview
•
•
•
•
Motivation
Related Work
System Model
Hypergossiping
◦ Partition Join Detection
◦ Rebroadcasting
• Evaluation
• Conclusion and Future Work
Research Group
“Distributed Systems”
Universität Stuttgart
5
IPVS
Related Work
density
dense
sparse (partitioned)
low mobile
(e.g. pedestrians)
non-partition-aware
protocols, e.g.
adaptive gossiping
scoped flooding
Integrated
Flooding (IF)
negotiation-based
protocols
mobility
plain flooding
highly mobile
(e.g. vehicles)
hyper flooding
repeat forwarding
restrict forwarding
Research Group
“Distributed Systems”
Goal: a generalized
strategy that supports
a wide range of
densities and
mobilities
Universität Stuttgart
6
IPVS
System Model
• MANET
A
R
◦ N mobile nodes populating a fixed area A
(density: d=N/A)
+
◦ Mobility is required to overcome partitioning
• Assumptions
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+
+
◦ Fixed communication range R
◦ Nodes do not need
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+
+
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▪ Location information
▪ Velocity information
+
• Hello beaconing to acquire neighborhood information
• Broadcast data is relevant up to lifetime
◦ Source sets the initial lifetime
◦ Nodes decrement lifetime
• Messages are uniquely identified by “source.seqNum”
Research Group
“Distributed Systems”
Universität Stuttgart
7
IPVS
Overview
•
•
•
•
Motivation
Related Work
System Model
Hypergossiping
◦ Partition Join Detection
◦ Rebroadcasting
• Evaluation
• Conclusion and Future Work
Research Group
“Distributed Systems”
Universität Stuttgart
8
IPVS
Our Approach: Hyper-Gossiping (HG)
• Goal: maximize reachability efficiently within the given max delay
(lifetime)
• MANET:= set of partitions that split or join over time.
• Approach: we combine two strategies
◦ Gossiping for intra-partition forwarding
Gossiping
(forwarding)
Repetition
(rebroadcasting)
◦ Broadcast Repetition
Gossiping
(rebroadcasting)
Research Group
“Distributed Systems”
Universität Stuttgart
9
IPVS
Broadcast Repetition: Basic Idea
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partition join detection
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MANET is partitioned
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rebroadcasting
broadcast repetition
Research Group
“Distributed Systems”
Universität Stuttgart
10
IPVS
Partition Join Detection Heuristic
• Nodes maintain a list of the IDs of Last
Broadcast packets Received ( LBR)
• Nodes share LBRs with neighbors using
A
B
existing HELLO beacons
• Detection heuristic
If
LBR _ own  LBR _ recv
LBR_own
ID1
 IS _ threshold
LBR _ own
then partition join is detected
LBR_recv
ID2
..
IDk
• Heuristic parameters
◦ Max LBR list size: maxLBRlength
◦ Max tolerated intersection of LBR lists:
IS_threshold
Research Group
“Distributed Systems”
Universität Stuttgart
11
IPVS
Rebroadcasting
• If a node detects a partition join, it sends the
IDs of all (still relevant) received packets
• Receiver sends missed packets
A
P1
P1
Node A
Node B
P2
P5
P2
P3
P3
P4
B
P4
P4
P5
P5
P6
P6
P7
P7
Buffer
(node A)
time
Research Group
“Distributed Systems”
Universität Stuttgart
12
IPVS
Overview
•
•
•
•
Motivation
Related Work
System Model
Hypergossiping
◦ Partition Join Detection
◦ Rebroadcasting
• Evaluation
• Conclusion and Future Work
Research Group
“Distributed Systems”
Universität Stuttgart
13
IPVS
Simulation Parameters
Area
1Km x 1Km
Number of nodes
N = 50 .. 500
Mobility model
Random waypoint
- Max speed
v in {3, 12.5, 20, 30} m/s
- Pause
2s
Lifetime
600 s
Buffer_size
infinity
Simulation time
650 s
Simulation runs
10
Wide density
range
Wide mobility
range
ns-2 simulator
Communication range
R = 100 m
Bandwidth
r = 1 Mbps
Data size
280 Bytes
HELLO beaconing
Random in [0.75 , 1.25] s
Research Group
“Distributed Systems”
Universität Stuttgart
14
IPVS
Hypergossiping Reachability
Reachability = number_of_reached_nodes / total_number_of_nodes
Research Group
“Distributed Systems”
Universität Stuttgart
15
IPVS
Hypergossiping MNFR
MNFR: Mean Number of Forwards and Rebroadcasts per node and per message
Research Group
“Distributed Systems”
Universität Stuttgart
16
IPVS
Integrated Flooding (IF)
• IMAHN project
• Integration of
◦ Plain flooding: every node forwards a newly received message once
◦ Scoped flooding: nodes forward a newly received message, only if a
certain ratio of neighbors is not covered by the sender
◦ Hyper flooding: Nodes buffer all packets for a fixed time (=60s), and on
discovering new neighbor rebroadcast all buffered packets
• Switch depending on relative speed
Scoped
Flooding
Plain
Flooding
Hyper
Flooding
low_threshold
high_threshold
(10 m/s)
(25 m/s)
Research Group
“Distributed Systems”
relative speed to
node‘s neighbors
Universität Stuttgart
17
IPVS
Comparison to Integrated Flooding (IF): Reachability
Reachability = number_of_reached_nodes / total_number_of_nodes
Research Group
“Distributed Systems”
Universität Stuttgart
18
IPVS
Comparison to Integrated Flooding (IF): MNFR
MNFR: Mean Number of Forwards and Rebroadcasts per node and per message
Research Group
“Distributed Systems”
Universität Stuttgart
19
IPVS
Conclusion and Future Work
• Hypergossiping is a generalized broadcast strategy for MANETs
◦ Adaptive gossiping for intra-partition forwarding
◦ Efficient broadcast repetition strategy on partition join
• Hypergossiping covers
◦ a wide range of node densities, and
◦ a wide range of node mobility levels
• Future Work
◦ Investigate different buffering strategies
◦ Adapt buffering parameters to node mobility
Research Group
“Distributed Systems”
Universität Stuttgart
20
IPVS
Universität Stuttgart
Institute of Parallel and
Distributed Systems (IPVS)
Universitätsstraße 38
D-70569 Stuttgart
Q&A
{khelil, marron, becker, rothermel}@informatik.uni-stuttgart.de