Algorithms for Bandwidth Efficient Multicast Routing in
Multi-channel Multi-radio Wireless Mesh Networks
Hoang Lan Nguyen and Uyen Trang Nguyen
Presenter: Hoang Lan Nguyen
Department of Computer Science and Engineering
York University, Canada
1
Outline

Motivation

Problem Formulation

The Proposed Algorithms

Performance Evaluation

Conclusion and Future Work
2
Motivation



Theoretical results indicate that the throughput capacity of
a single-channel wireless mesh network becomes
unacceptable low as the number of nodes increases.
One of the most effective approaches to enhance network
throughput is to use systems with multiple channels and
multiple radios (MCMR) per node.
Research on multicast (one-to-many communication) has
focused mostly on networks with a single channel.
–
Traditional multicast routing algorithms designed for single-channel
environment, for example Shortest Path Tree (SPT) or Minimum
Steiner Tree (MST), are not suitable for multi-channel multi-radio
networks as they did not consider the channel diversity.
3
Motivation (cont.)


The problem of multicasting in multi-channel multiradio networks has only been studied recently.
These studies addressed the multicast problem based
on the following approach:
–
First, a mutlicast tree is constructed based only on network
topology
–
Then, a channel assignment is applied on top of the
constructed tree to optimize an objective function (such as
maximizing throughput or minimizing delay).
–
We call this approach “Routing first, Channel Assignment
second”
4
Motivation (cont.)
• Drawbacks of the “Routing first, Channel
Assignment second” approach:
•
It does not consider existing channel
assignments currently used by other types of
communications such as unicast
•
The new channel assignment for multicast may
conflict with the current unicast channel
assignment.
5
Our Approach
• We instead consider the reverse approach called
“Channel Assignment first, Routing second”:
• We construct multicast trees on top of currently deployed channel
assignments.
• Advantages of this approach:
• Re-use existing channel assignments
• Have no channel conflict with other types of communications
• Easy to deploy in any existing multi-channel multi-radio systems
6
Problem Formulation
• Given a multi-channel multi-radio wireless
network with already allocated channel
assignments, the objective is to:
• construct a multicast tree with minimum bandwidth
consumption
• minimize the number of transmissions used by the
multicast tree
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Problem Formulation (cont.)
• The problem focuses on the number of transmissions each
forwarding node requires to multicast a packet to its downstream
node(s) in the multicast tree
–
Note: in single-channel network, it is always one
–
However, in multi-channel network, it may be greater or equal to
one due to channel diversity
• For example, for the multicast tree below (drawn in blue arrows), for
every packet, forwarder N has to make two transmissions, one on
channel 1 to node I and the other on channel 3 to node K
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Problem Formulation (cont.)
• The number of transmissions consumed by a multicast
tree is the total of the numbers of transmissions summed
over all forwarding nodes in the multicast tree
• We have proved that constructing a multicast tree with
minimum number of transmissions in a multi-channel
multi-radio network is a NP-hard problem
• We, therefore, find approximate solutions by proposing
heuristic algorithms
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The Proposed Algorithms



We propose multicast routing algorithms that take into account the
channel diversity in multi-channel systems in order to minimize the
amount of network bandwidth consumed by the multicast tree.
Specifically, given a multi-channel multi-radio network with already
allocated channel assignments, the algorithm constructs a multicast
tree that minimizes the total number of transmissions required to deliver
a data packet from the source to all multicast destinations.
It uses a proposed routing metric that maximizes the wireless broadcast
advantage and minimizes interference among nearby (one-hop away)
forwarding nodes.


The wireless broadcast advantage refers to the concept that a single transmission
from a node can reach all of its one-hop neighbors. This is true in single-channel
networks.
However, this is not always the case in multi-channel networks due to channel
diversity
10
The Proposed Routing Metric
• Channel utilization µu(c) at node u is the
number of incident links on u that are
assigned channel c
• Channel metric δu(c) at node u is defined
as 1 / µu(c)
– small δ values imply high channel utilization
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The Proposed Routing Metric (cont.)
• Link cost w(u,v) of link (u,v) is defined as δu(c) / δv(c)
where c is the channel used by link (u,v)
– The term δu(c) in the link cost favors a transmitter with a channel
highly utilized so that the channel can be used for as many
receivers as possible. This is to maximize the wireless broadcast
advantage.
– Given link (u,v) on channel c, the next-hop link (v,z) to be added
should avoid channel c so that transmissions from u and v do not
interfere
– Therefore, given a transmitter u with highly utilized channel c, we
should choose v with lowly utilized channel c. This explains the
term 1/ δv(c) in the link cost. This is to minimize interference
among forwarding neighbors.
• Path cost of a path is the sum of link costs of the links on
the path
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The Centralized Algorithm
• The centralized algorithm then uses the
proposed link costs to build a minimum cost
multicast tree based on a Steiner heuristic and
greedy approach:
– The tree initially contains only the source.
– Then a multicast destination with the least path cost to
the tree is added to the tree
• This repeats until all multicast destinations are added to the
tree
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The Distributed Algorithm
• The distributed algorithm consists of two
phases:
- Phase 1: a broadcast tree with minimized
number of transmissions is built using
distance-vector routing
- Phase 2: multicast tree is then constructed by
pruning the broadcast tree
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Performance Evaluation
• We call the proposed multicast trees MCMNT (MultiChannel Minimum Number of Transmissions) and
compare with other types of multicast trees namely:
– Shortest Path Tree (SPT)
– Minimum Steiner Tree (MST)
– Minimum number of Forwarder Tree (MFT) [Ruiz et al., ISCC’05]
Recall that, unlike MCMNT, the SPT, MST and MFT multicast trees
are constructed based on network topology only, and do not take
the underlying channel assignments into account.
• The performance evaluation was done using Qualnet
simulator
15
Experiment Setup
• 100 nodes uniformly distributed over a 1700m x 1700m network with
random channel assignments
• Each node has a transmission range of 350m
• The data rate at the 802.11 physical layer is 11 Mbps
• The 802.11 CSMA/CA without RTS/CTS is used for multicast
communications
• At the transport layer we do not use any flow or congestion control
mechanisms to test the network performance under heavy loads
• The multicast source is placed at the center sending data at a
constant bit rate, while multicast destinations are randomly scattered
around the network
• Each experiment is run for 600 seconds of simulated time
• Each data point in the resulting graphs is averaged from five runs
using random seeds and plotted with a confidence interval of 95%
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Experiment Scenarios
• We consider the following scenarios:
– multicast group size: number of multicast destinations varies
from 20 to 80 nodes. The number of radios per node and the
number of channels are set to 3. The source transmits at a rate
of 200 packets/s
– multicast source rate: multicast source rate varies from 100 to
300 packets/s. The number of channels and radios per node is
3. The multicast group consists of 40 destinations.
– number of channels: number of channels is set to 1, 3, 5, and 7.
The multicast group contains 40 destinations and the source
rate is 200 packets/s
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Experiment Results: Group Size
•
MCMNT trees have the least transmission consumption and the highest packet
delivery ratio
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Experiment Results: Source Rate
• This is also the case for the source rate scenario under heavy traffic loads
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Experiment Results: Number of Channels
•
MCMNT also outperforms in various numbers of channels (except for the singlechannel case)
– Note: MCMNT is not optimized for single-channel systems as there is no channel
diversity in such environment
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Conclusion and Future Work
• We studied the problem of constructing multicast trees
with minimum number of transmissions in wireless mesh
networks where multiple channels and radios are used
• Our proposed algorithms outperformed traditional
multicast trees with respect to various performance
metrics.
• Our future work:
– includes traffic load into the link and path cost computations for
better load balancing and performance under dynamic network
conditions
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