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Performance Evaluation of a Self-Organising Scheme for Multi-Radio Wireless Mesh Networks

TL;DR: A generic self-organisation algorithm that addresses the two key challenges of scalability and stability in a WMN, that of a distributed, light-weight, co-operative multiagent system that guarantees scalability.
Abstract: Multi-radio wireless mesh networks (MR-WMN) can substantially increase the aggregate capacity of the wireless mesh networks (WMN) if the channels are assigned to the nodes in an intelligent way so that the overall interference is limited. We propose a generic self-organisation algorithm that addresses the two key challenges of scalability and stability in a WMN. The basic approach is that of a distributed, light-weight, co-operative multiagent system that guarantees scalability. The usefulness of our algorithm is exhibited by the performance evaluation results that are presented for different MR-WMN node densities and typical topologies. In addition, our work complements the Task Group 802.11s extended service set (ESS) mesh networking project work that is in progress.

Summary (3 min read)

Introduction

  • Wireless Mesh Networks (WMNs) have emerged as a feasible way of providing the last mile connectivity between the access networks and the Internet.
  • In a single radio network the throughput of the link between each hop progressively decreases due to the co-channel interference between the adjacent hops as well as interference from the neighbouring links [1].
  • These limitations have lead to the introduction of multiple radio interfaces on each node to form a multi-radio Wireless Mesh Networks (MR-WMN).
  • Through an extensive study the authors have identified scope for improvement in the key areas of scalability and stability for the channel assignment process.
  • The authors have validated both the scalability and stability aspects of their algorithm by means of analysis and provided key simulation results that show the impact of node density and MR-WMN typical topologies on the algorithm performance.

3. Proposed algorithm

  • Before the authors explain their proposed algorithm for channel assignment the notations and assumptions used in the remainder of this section are stated below: Available channels: 1, . . ,K. A node is a set of radio interfaces where each interface is associated with a particular channel, together with a controller that assigns the channel to each interface.
  • A locked interface is only locked for a ‘very short’ period during the operation of each of those procedures.
  • SNIR means “signal to noise plus interference ratio”.
  • It begins by building a spanning tree from a root interface (mesh portal) that spans a designated area of the mesh network.
  • All these nodes respond to the seed node with an accept Hello packet.

C) Self- Organisation: Proactive Logic

  • The authors solution is based on the distinction in multiagent The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007) © 2007 Authorized licensed use limited to: IEEE Xplore.
  • The following methods are independent of the operation of the load balancing algorithm.
  • The process for proactive logic involves that the node broadcast a “Hello” packet at frequency f1 and it then determines the sum of the interference cost function between its link and each of the other links (one-by-one) with respect to each other.
  • The stability of this procedure follows from the fact that it produces a net improvement of the interference cost within Sa.

4.1 Simulation model and attributes

  • The authors present the details of the Java simulation framework developed by their team to test the performance and behaviour of the algorithms.
  • ICA - Interference Caused by the link After self organisation algorithm has been triggered.
  • Below, the authors state the key attributes of the simulation model: The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007) © 2007 Authorized licensed use limited to: IEEE Xplore.
  • The simple grid - the routers were positioned from each other in a uniform grid with their in between distances randomly varying 5%.
  • The authors generated 5252, 5292 and 5064 links for simple grid, random grid and completely random topology respectively.

4.2 Results and Discussion

  • The interference cost reduction for a link discussed herein is measured as the difference between absolute interference (AI) values obtained before the channel assignment process and after the channel assignment process.
  • The mean of IC reduction across all topologies and network densities is 36.7.
  • 2.1 Impact of network density on the performance.
  • This trend is shown across all the topologies.
  • From Fig. 1 it can also be observed that the range of the interference reduction across the topologies at router densities of 35 routers and 100 routers is 1.55 and 1.58, respectively.

4.2.2 Impact of typical topologies on the

  • Figure 2 shows the variation in the interference cost reduction as function of network topology and it can be deduced that the impact of the topologies on the performance of the algorithm (i.e. in terms of interference cost reduction) is insignificant.
  • The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications (AusWireless 2007) © 2007 Authorized licensed use limited to: IEEE Xplore.
  • The mean of IC reduction calculated from the data obtained shows that the topology with the smallest average IC reduction is the completely random with a mean of 36.02 and topology with the most IC reduction is the random grid with a mean of 37.12.
  • The difference in performance between best and worst case is just 1.1 which confirms that the performance of the algorithm is almost completely independent of the type of topology.
  • In addition to previously discussed results for the algorithm, the authors have calculated the 98% confidence bounds per link for absolute interference values across all topologies and different network densities.

4.2.4 Performance Comparison across the Network

  • The authors obtained Interference cost in different regions of the MR-WMN for the same set of links before and after the self-organisation algorithm is invoked.
  • From Fig 3 the authors can see that there were no nodes (blue dots) that caused more interference after the self-organisation than it had caused before (red dots) the selforganization was invoked.

5. Conclusions

  • The authors have proposed a self-organization algorithm that addresses the two major challenges of scalability and stability.
  • Scalability is ensured by progressively assigning the channels to nodes in clusters during the wireless mesh network system start up phase.
  • The stability is offered by means of the proactive and reactive logic of the algorithm.
  • Key performance evaluation results obtained from extensive simulations showed the effectiveness of the algorithm for different node densities, topologies and across different parts of the multi-radio mesh network.

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Performance Evaluation of a Self-Organising Scheme for Multi-Radio
Wireless Mesh Networks
Ante Prodan, Vinod Mirchandani, John Debenham and Les Green
Faculty of Information Technology
University of Technology, Sydney
NSW 2007, Australia
{aprodan, vinodm, debenham, lesgreen}@it.uts.edu.au
Abstract
Multi-Radio Wireless Mesh Networks (MR-WMN)
can substantially increase the aggregate capacity of
the Wireless Mesh Networks (WMN) if the channels
are assigned to the nodes in an intelligent way so that
the overall interference is limited. We propose a
generic self-organisation algorithm that addresses the
two key challenges of scalability and stability in a
WMN. The basic approach is that of a distributed,
light-weight, co-operative multiagent system that
guarantees scalability. The usefulness of our algorithm
is exhibited by the performance evaluation results that
are presented for different MR-WMN node densities
and typical topologies. In addition, our work
complements the Task Group 802.11s Extended
Service Set (ESS) Mesh networking project work that is
in progress
.
1. Introduction
Wireless Mesh Networks (WMNs) have emerged as
a feasible way of providing the last mile connectivity
between the access networks and the Internet. The
growing impetus of WMNs prompted IEEE in 2004 to
initiate an ESS Mesh Networking task group - 802.11s.
Initially, the research in the area of mesh networks
based on 802.11a,b/g standards focused on a single
radio (single channel) WMN. However, in a single
radio network the throughput of the link between each
hop progressively decreases due to the co-channel
interference between the adjacent hops as well as
interference from the neighbouring links [1]. These
limitations have lead to the introduction of multiple
radio interfaces on each node to form a multi-radio
Wireless Mesh Networks (MR-WMN). The key
benefits offered by MR-WMN are:
(i) Cost effectiveness in providing the last-mile
connectivity to the Internet.
(ii) Increased scale of deployment and Reliability.
(iii) Creates disjoint collision domains due to which
an overall increase in network capacity is
realised.
The 802.11 standards provide a limited number of
non-overlapping channels however the interference
caused by the reuse of these channels from
neighbouring links represents the key factor that limits
the performance. Through an extensive study we have
identified scope for improvement in the key areas of
scalability and stability for the channel assignment
process. Scalability is important because WMNs will
be deployed over large metropolitan areas and hence
the self-organisation process should occur within a
reasonable time. By stability we mean that the process
should be robust enough to sustain the assignment of
channels over a period of time rather than trigger a
frequent assignment of channels.
In this paper, we propose a self-organising
algorithm in a multi-radio WMN that is based on the
approach of a distributed, light-weight, co-operative
multi-agent system that guarantees scalability. We
have validated both the scalability and stability aspects
of our algorithm by means of analysis and provided
key simulation results that show the impact of node
density and MR-WMN typical topologies on the
algorithm performance. Our self-organisation
mechanism operates over MR-WMN so that the
interference between the channels of routers in its
interference range is reduced. Each hop in a MR-
WMN has a throughput that is dependent mainly on
This work is supported by a grant from Alcatel-Lucent and the
Australian Research Council.
The 2nd International Conference on Wireless
Broadband and Ultra Wideband Communications (AusWireless 2007)
© 2007
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the radio type, the distance between the transmitter and
receiver, the modulation schema, and interference.
The paper is organised as follows: In Section 2, we
review some of the important work related to the
channel assignment in wireless networks. Section 3
presents and explains our algorithm for channel self
organisation. It also presents validation by means of
analysis. The simulation results obtained for algorithm
performance are presented and discussed in Section 4.
The paper concludes in Section 5.
2. Related work
The proposals in literature that are discussed herein
can be classified for use in either: (i) cellular systems
and infrastructure mode 802.11 networks (BSS) or (ii)
MR-WMN. Although the problems addressed by the
first group are somewhat different, important parallels
justify the coverage of related methods in this review.
The use of channel assignment approach in cellular
systems for WLANs has been exhaustively reviewed
in [2]. A conclusion of particular interest is that as a
cell size becomes smaller distributed scheme becomes
more attractive because of high centralisation
overhead. The review of this work also reveals the
main differences between cellular and 802.11 based
networks viz. (i) the usage of an unlicensed spectrum
in 802.11 that is public (ii) base stations in cellular
networks are at fixed distances whereas 802.11 APs
are in most cases at random distances from each other.
In most of the reviewed papers the graph colouring
theory is used as a base for the theoretical modelling of
channel assignment. References [3,4] use weighted
graph colouring with the weight calculation based on a
number of clients that are affected by the interference
affecting an AP on a particular channel. Reference [5]
uses waited graph colouring in form of interference
graph. In this model each vertex represents a WLAN
and edges represent interference between
corresponding WLANs. Although models based on
graph colouring theory have proven their usefulness in
modelling interference in infrastructure based WLANs,
we agree with the conclusion of [6] that graph
colouring models do not adequately capture all the
constraints of a multi radio WMN.
We focus in the remainder of this section on the
performance, complexity, scalability and stability of
WMN related proposals. The work in [7] specifically
targets the channel assignment problem on WMN.
Authors have adopted their theoretical work in [7] and
created a self-stabilizing distributed protocol and an
algorithm for channel assignment. Reference [7]
assumes that the interference is symmetric and is based
on an interference range of three hops. This results in
improvements of only 20% compared to random
channel assignment. In reality, most of the times
interference will be asymmetric because neighbouring
node interface may transmit on the same channel at
different powers. In contrast, our proposal does not
assume symmetric interference and does not require a
dedicated channel for frequency co-ordination, which
is a significant advantage. The other main limitation of
proposal in [7] is the use of a common channel on each
node for the management of channel assignment. We
have avoided this approach because it can be wasteful
of bandwidth and imposes severe limitations on
network capacity especially when nodes have only two
interfaces. Furthermore, a strong source of interference
on the frequency that is used for the coordination of
channels can render the throughput of parts or the
whole network unsatisfactory.
3. Proposed algorithm
Before we explain our proposed algorithm for
channel assignment the notations and assumptions
used in the remainder of this section are stated below:
Available channels: 1, . . ,K.
A node is a set of radio interfaces where each
interface is associated with a particular channel,
together with a controller that assigns the channel to
each interface. The node has blocks of interfaces that
belong to different radio types. In its current state all
existing wireless standards need bridging (relaying on
layer 2) or routing (relaying on layer 3) functionality to
connect with other wired or wireless networks. We
assume that each node provides such functionality.
• A link is a pair of interfaces where each interface is
assigned the same channel. The idea is that two
interfaces communicate through a shared link. That is,
if an interface is part of a link its state will be
“listening and transmitting”, otherwise its state will be
“listening only”.
• Notation: nodes are denoted by Latin letters: a, b, c,...
the interfaces for node a are denoted by: a[i] for i = 1,
. . , and links are denoted by Greek letters: α, β, γ...The
interfaces communicate using an illocutionary
communication language that is defined informally
(for the time being) with illocutions being
encapsulated in quotation marks: “•”.
• For any node n, S
n
is the set of nodes in node n’s
interference range. Likewise, for any link α, S
α
is the
set of links that contain nodes n’s interference range.
Given a node “a”, define V
a
=
U
α
Sn
n
S
t
x
Γ is channel used by x to communicate at time t
where x may be either an interface or a link.
f(•, •) is an interference cost function that is defined
between two interfaces. It estimates the cost of
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© 2007
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interference to one interface caused by transmission
from the other interface. This function relies on
estimates of the interference level and the level of load
(i.e.: traffic volume).
• An interface is either ‘locked’ or ‘unlocked’. A
locked interface is either locked because it has
committed to lock itself for a period of time on request
from another interface, or it is ‘self-locked’ because it
has recently instigated one of the self-organisation
procedures explained in this section. A locked
interface is only locked for a ‘very short’ period during
the operation of each of those procedures. This is
simply to ensure that no more than one alteration is
made during any one period— this is necessary to
ensure the stability of the procedures. We also say that
a node is locked meaning that all the interfaces at that
node are locked.
• SNIR means “signal to noise plus interference ratio”.
The proposed algorithm is explained below in
different steps that correspond to the different states of
the system.
A) Initialising the system.
This procedure initialises a network from system
start-up. It begins by building a spanning tree from a
root interface (mesh portal) that spans a designated
area of the mesh network. Such a tree may also be used
if the network operator requires a systematic method to
communicate with all nodes such as updating the
nodes’ algorithms — this use of spanning trees is not
discussed further here. The algorithm has three steps:
1. Construct a spanning tree with the property that any
node in the area is within the interference range of a
node on the tree. The spanning tree’s nodes are called
seed nodes. The construction of a good spanning tree
requires reference to topological information that may
be obtained by a low cost GPS chipset — we do not
discuss this here. Operational parameters such as
transmit power, obtained from the nodes within the
interference range of each seed node are stored in a
table at the seed node. This information we term as
“infoa”.
2. Each seed node in turn then builds a cluster of nodes
around itself. The seed node builds its cluster one node
at a time. Each seed node is strategically chosen so that
the clusters formed around the seed nodes cover most
of the area in the wireless mesh region. The cluster
formation process involves that the seed node
broadcasts a “Hello” packet at frequency f
1
to all the
nodes in its interference range. All these nodes respond
to the seed node with an accept Hello packet. The seed
node then assesses the SNIR value of the transmission
between itself and each of the responding nodes. It will
then assign the frequency f
1
to the responding node
(interface) for which a maximum value of SNIR was
obtained. The following algorithm represented in an
illocutionary language summarises this process (Notes:
id
a
is a MAC identifier.)
for j=1,……,.K do {
transmit “inform hello[id
a
]” with a[j] on channel j;
set b
arg max
x
{SNIR(receive “accept hello[id
a
, id
x
] on
channel j”)};
transmit “inform channel [id
a
, id
b
, j]”; };
3. In the event that the above procedure fails to
establish links with all nodes (due perhaps to
unforeseen external events) we assume that those
unconnected nodes will invoke the procedure
described in part B below.
B) Process for adding a new node.
The objective of this process is for a new node that is
introduced to the mesh topology to join the mesh. The
description is from the point of view of node a that
wishes to join its interface a[i] to the mesh. The aim of
the joining process is for a node to establish
connectivity with a node in its interference range. For
this the joining node broadcasts a “Hello” packet at
frequency f
1
. The “Hello” packet is essentially a
Registration packet. Whichever nodes can provide
connectivity to the joining node they respond back
with an “accept Hello” packet. The joining node then
selects the node with which it wants to establish
connectivity on the basis of the maximum SNIR
transmission value between itself and the responding
node. The following algorithm represented in an
illocutionary language summarises this process.
for j =1,….,K do {
transmit “inform hello[id
a
]” with a[i] on channel j
if (SNIR (receive “accept hello[id
a
, id
x
] on channel j”) > κ {
set
Γ
j;
break; } else {set
Γ
arg max
x
{SNIR (receive accept
hello[id
a
, id
x
] on channel k”)};}}
in time [t –1, t];
set b
arg max
x
{ SNIR receive “accept hello[id
a
, id
x
] on
channel k”)};
transmit “request link[id
a
, id
b
, Γ ]” at time t;
if receive “accept link[id
a
, id
b
, Γ ]” by time t+s then
transmit “inform info[info
a
]” with a[i] on channel
Γ
and
stop; else start again;
Notes: constant s is set to be sufficient to permit node
b to be released from a locked state in the event that it
is locked. The constant κ represents an acceptable level
of SNIR that the node will accept without further
consideration. id
a
is a MAC identifier.
C) Self- Organisation: Proactive Logic
Our solution is based on the distinction in multiagent
The 2nd International Conference on Wireless
Broadband and Ultra Wideband Communications (AusWireless 2007)
© 2007
Authorized licensed use limited to: IEEE Xplore. Downloaded on March 1, 2009 at 18:32 from IEEE Xplore. Restrictions apply.

systems between proactive and reactive reasoning.
Proactive reasoning is concerned with planning to
reach some goal. Reactive reasoning is concerned with
dealing with unexpected changes in the agent’s
environment. So in the context of self-organising
networks we distinguish between:
• a reactive logic that deals with problems as they
occur. The aim of our reactive module is simply to
restore communication to a workable level that may be
substantially sub-optimal.
• a proactive logic that, when sections of the network
are temporarily stable, attempts to adjust the settings
on the network to improve performance.
The reactive logic provides an “immediate fix” to
serious problems. The proactive logic, that involves
deliberation and co-operation of nearby nodes, is a
much slower process. The following methods are
independent of the operation of the load balancing
algorithm.
Method for adjusting the channels - Proactive logic
Informally the proactive logic uses the following
procedure:
• Elect a node a that will manage the process
• Choose a link α from a to another node — precisely a
trigger criterion permits node a to attempt to improve
the performance of one of its links with a certain
priority level.
• Measure the interference
• Change the channel setting if appropriate
The process for proactive logic involves that the
node broadcast a “Hello” packet at frequency f
1
and it
then determines the sum of the interference cost
function between its link and each of the other links
(one-by-one) with respect to each other. Note: Due to
non-symmetrical nature of transmission caused by
different transmission powers the interference cost
function may not be symmetrical. If the sum of non-
symmetrical interference cost function for a frequency
f
1
is below a threshold range then the frequency f
1
is
assigned to the node interface for which the proactive
logic was applied. Our proactive logic is a
development of the ideas in [7,8].
Selflock in the algorithm is to prevent a from having
to activate the method too frequently. The constant ε <
1 requires that the improvement be ‘significant’ both
for node a and for the set of nodes S
a
. The stability of
this procedure follows from the fact that it produces a
net improvement of the interference cost within S
a
. If a
change of channel is effected then there will be no
resulting change in interference outside S
a
. The above
method reduces the net observed inference cost in the
region V
a
. The following algorithm represented in an
illocutionary language summarises the proactive logic
process.
choose node a at time t – 2; set V
a
=
U
α
Sn
n
S
;
x V
a
transmit “propose organise[a, x, p]”;
unless
x
V
a
receive “overrule organise[a, x, q]”
in [t – 2, t – 1] where q > p do {
x V
a
transmit
“propose lock[a, x, t, t+1]”;
if
x V
a
receive “accept lock[a, x, t, t+1]” in [t- 1,
t] then {unless
x
V
a
receive “reject lock[a, x, t,
t+1]” do {improve a;}}}
where: improve a = {choose link α
a on channel
t
α
Γ ;
set B
α
β
βα
S
f )|( +
α
β
αβ
S
f )|( ;
if (feasible) re-route α’s traffic;
for
α
Γ
= 1,……K
α
Γ
t
α
Γ do {
if
α
β
βα
S
f )|( +
α
β
αβ
S
f )|( < B x ε
then {
1+
Γ
t
α
α
Γ
; selflock node a in [t + 1, t + k];
break;};};
x V
a
transmit “α’s interference test signals”;
apply load balancing algorithm to S
a
;}
4. Performance Evaluation
4.1 Simulation model and attributes
In this section, we present the details of the Java
simulation framework developed by our team to test
the performance and behaviour of the algorithms.
Each link was initially generated with a randomly
assigned channel. By recursively using this approach
all the routers (mesh nodes) were connected to the
network. When one or more routers were left without
any connectivity to the rest of the network (often in
completely random topology) the simulation is
repeated until the topology with all routers connected
is obtained. Four values of interference cost were
calculated for such a network and in the rest of the
paper are labelled with the following abbreviations:
ICB - Interference Caused by the link Before self
organisation algorithm is triggered. IAB - Interference
At the link Before self organisation algorithm is
triggered. ICA - Interference Caused by the link After
self organisation algorithm has been triggered. IAA -
Interference At the link after the self-organisation
algorithm has been triggered. ICB and IAB are used as
reference values to calculate the decrease in
interference cost after the self-organisation algorithm
was applied. Below, we state the key attributes of the
simulation model:
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Broadband and Ultra Wideband Communications (AusWireless 2007)
© 2007
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32.00
33.00
34.00
35.00
36.00
37.00
38.00
39.00
40.00
41.00
42.00
35 70 100
Node Density
% of Interference Cost Reduction
Simple GRID
Random GRID
Completely Random
The self-organising channel assignment process
was limited to a single channel change per link.
All radio interfaces were static, deployed with
omni-directional antennas, based on 802.11g
standard, and transmits power for each interface
was generated randomly with a 50% variation.
Calculation of interference cost was based on the
following parameters:
Distance between interfaces.
Signal strength of transmitting interface
(consequently it is not symmetrical).
Interference factor between partially
overlapping channels as provided in [8].
All networks generated occupied an equal size area
of 750 X 500 meters. Three different densities of
routers per sq. unit of area were deployed in each
topology: 35, 70 and 100.
Three different topologies were generated:
o The simple grid - the routers were positioned
from each other in a uniform grid with their in
between distances randomly varying 5%. An
example of simple grid is the cellular network.
o The random grid – the same as previous only
with 50% of random variation.
o The completely random – in this topology the
arrangement of the routers was generated
completely randomly. An example of
completely random topology is the ad hoc
network.
The number of interfaces per router was generated
randomly to be between 3 and 5.
Each simulation for a topology with specific
random grid variation and router density was
repeated 12 times and a mean and confidence
interval was calculated (108 simulations in total).
We generated 5252, 5292 and 5064 links for
simple grid, random grid and completely random
topology respectively. This high number of
simulated links enabled us to obtain statistically
valid confidence interval of 98%.
4.2 Results and Discussion
The interference cost reduction for a link discussed
herein is measured as the difference between absolute
interference (AI) values obtained before the channel
assignment process and after the channel assignment
process. For example, if AI
before
= 5 and AI
after
=4 the
absolute difference is AD=1 which is 20% decrease in
the absolute interference. Consequently, the
performance is always expressed as a percentage of the
decrease. The mean of IC reduction across all
topologies and network densities is 36.7.
Our simulation studies consider realistic scenarios
of different node densities and topologies in a typical
wireless mesh network hence are more reflective of
evaluating the true performance of the algorithm.
4.2.1 Impact of network density on the performance
It can be seen from Fig. 1. that as the density of
network increases (i.e. an increase in the number of
routers located within the same area) the IC reduction
relatively decreases. This trend is shown across all the
topologies.
Figure 1: Interference cost reduction as a
function of network density
We attribute this result to the limited number of
non-overlapping channels available in IEEE 802.11b/g
standard that in tight proximities of the nodes (i.e.
increase in node densities) shows more effects of a
higher absolute interference and thus a relatively lower
interference cost reduction. Furthermore, the impact of
node density on the algorithm is relatively consistent
for all topologies at the same router densities. From
Fig. 1 it can also be observed that the range of the
interference reduction across the topologies at router
densities of 35 routers and 100 routers is 1.55 and
1.58, respectively.
4.2.2 Impact of typical topologies on the
interference cost. Figure 2 shows the variation in the
interference cost reduction as function of network
topology and it can be deduced that the impact of the
topologies on the performance of the algorithm (i.e. in
terms of interference cost reduction) is insignificant.
32.00
33.00
34.00
35.00
36.00
37.00
38.00
39.00
40.00
41.00
42.00
Simple GRID Random GRID Completely Random
Topology
% of Interference Cost Reduction
Density 35
Density 70
Density 100
Figure 2: Interference Cost Reduction
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Citations
More filters
Proceedings ArticleDOI
01 Aug 2007
TL;DR: An algorithm for self-organization that assigns the channels intelligently in multi-radio wireless mesh networks (MR-WMN) by way of spatial distribution of connectivity between the mesh nodes and the performance is evaluated.
Abstract: An algorithm for self-organization that assigns the channels intelligently in multi-radio wireless mesh networks (MR-WMN) is important for the proper operation of MR-WMN. The aim of the self-organization algorithm is to reduce the overall interference and increase the aggregate capacity of the network. This can be possible by addressing the two major challenges that are associated with the self-organization of MR-WMN - scalability and stability. In this paper, we have first proposed a generic self-organization algorithm that addresses these two challenges. The basic approach is that of a distributed, light-weight, co-operative multiagent system that guarantees scalability. Second, we have evaluated the performance of the proposed self-organization algorithm for two sets of initialization schemes. The initialization process results in a topology control of MR-WMN by way of spatial distribution of connectivity between the mesh nodes. The results have been obtained for realistic scenarios of MR-WMN node densities and topologies.

8 citations


Cites background from "Performance Evaluation of a Self-Or..."

  • ...This is not discussed herein....

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  • ...…of 802.11 transmission technology used i.e. 802.11b/g/a, Op = MAC protocol overhead; This depends on the type of 802.11 transmission technology used, Bt = Number of bits in a test frame; r = Transmission bit rate (Mb/s); εfr = frame error rate, based on the current conditions of the radio channel....

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Proceedings ArticleDOI
22 Oct 2007
TL;DR: In this paper, the authors evaluated the impact of initialization process on the self-organization algorithm performance as it involves mesh node selection for channel assignment in multi-radio wireless mesh networks.
Abstract: Multi-radio wireless mesh networks (MR-WMN) with a smart channel assignment scheme can be used as a viable cost-effective alternative for a last mile broadband access. The channel assignment algorithm should be such that it reduces the overall interference and increases the aggregate capacity of the network. In this paper, we have concisely presented the key results of our work in progress, which pertain to the evaluation of self-organization algorithm for multi-radio mesh networks. Specifically, the study focuses on the impact of initialization process on the self-organization algorithm performance as it involves mesh node selection for channel assignment. The initialization process results in a topology control of MR-WMN by way of spatial distribution of connectivity between the mesh nodes. The process for initiating the topology of self-organized mesh networks is also described. In order to conclusively show the merits of our initialization process, we have carried out this study for realistic densities of the mesh nodes and their topologies varying from completely random to being ordered in a grid.

4 citations

16 May 2010
TL;DR: The study focuses on the impact of initialization process on the self-organization algorithm performance as it involves mesh node selection for channel assignment and results in a topology control of MR-WMN by way of spatial distribution of connectivity between the mesh nodes.
Abstract: Multi-radio wireless mesh networks (MR-WMN) with a smart channel assignment scheme can be used as a viable cost-effective alternative for a last mile broadband access. The channel assignment algorithm should be such that it reduces the overall interference and increases the aggregate capacity of the network. In this paper, we have concisely presented the key results of our work in progress, which pertain to the evaluation of self-organization algorithm for multi-radio mesh networks. Specifically, the study focuses on the impact of initialization process on the self-organization algorithm performance as it involves mesh node selection for channel assignment. The initialization process results in a topology control of MR-WMN by way of spatial distribution of connectivity between the mesh nodes. The process for initiating the topology of self-organized mesh networks is also described. In order to conclusively show the merits of our initialization process, we have carried out this study for realistic densities of the mesh nodes and their topologies varying from completely random to being ordered in a grid.

4 citations

Book ChapterDOI
03 Sep 2008
TL;DR: To address the problem of topology control on multi-radio wireless mesh networks a distributed, lightweight, co-operative multiagent system that guarantees scalability has been developed and validated by simulation.
Abstract: To address the problem of topology control on multi-radio wireless mesh networks a distributed, lightweight, co-operative multiagent system that guarantees scalability has been developed and validated by simulation. Our goal is twofold, to select channels so to reduce interference, and improve connectivity by shortening paths between portal and client nodes. As this system is to be deployed over large networks the scalability and stability of the solution are of the main concern. The proposed algorithms have been implemented and evaluated with the Java based framework as well as NetLogo a multiagent simulation tool. The positive attributes of the algorithms are demonstrated through the comprehensive simulation result analysis.

1 citations


Cites methods from "Performance Evaluation of a Self-Or..."

  • ...The algorithms described in this paper leverage on the work on topology control that we have conducted in [ 3 ],[4]....

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References
More filters
Journal ArticleDOI
TL;DR: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits persecond under a noninterference protocol.
Abstract: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission's range is optimally chosen, the bit-distance product that can be transported by the network per second is /spl Theta/(W/spl radic/An) bit-meters per second. Thus even under optimal circumstances, the throughput is only /spl Theta/(W//spl radic/n) bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.

9,008 citations


"Performance Evaluation of a Self-Or..." refers background in this paper

  • ...However, in a single radio network the throughput of the link between each hop progressively decreases due to the co-channel interference between the adjacent hops as well as interference from the neighbouring links [1]....

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Proceedings ArticleDOI
13 Mar 2005
TL;DR: It is shown that intelligent channel assignment is critical to Hyacinth's performance, and distributed algorithms that utilize only local traffic load information to dynamically assign channels and to route packets are presented, and their performance is compared against a centralized algorithm that performs the same functions.
Abstract: Even though multiple non-overlapped channels exist in the 2.4 GHz and 5 GHz spectrum, most IEEE 802.11-based multi-hop ad hoc networks today use only a single channel. As a result, these networks rarely can fully exploit the aggregate bandwidth available in the radio spectrum provisioned by the standards. This prevents them from being used as an ISP's wireless last-mile access network or as a wireless enterprise backbone network. In this paper, we propose a multi-channel wireless mesh network (WMN) architecture (called Hyacinth) that equips each mesh network node with multiple 802.11 network interface cards (NICs). The central design issues of this multi-channel WMN architecture are channel assignment and routing. We show that intelligent channel assignment is critical to Hyacinth's performance, present distributed algorithms that utilize only local traffic load information to dynamically assign channels and to route packets, and compare their performance against a centralized algorithm that performs the same functions. Through an extensive simulation study, we show that even with just 2 NICs on each node, it is possible to improve the network throughput by a factor of 6 to 7 when compared with the conventional single-channel ad hoc network architecture. We also describe and evaluate a 9-node Hyacinth prototype that Is built using commodity PCs each equipped with two 802.11a NICs.

1,636 citations


"Performance Evaluation of a Self-Or..." refers background in this paper

  • ...Although models based on graph colouring theory have proven their usefulness in modelling interference in infrastructure based WLANs, we agree with the conclusion of [6] that graph colouring models do not adequately capture all the constraints of a multi radio WMN....

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Journal ArticleDOI
TL;DR: This article provides a detailed discussion on reuse partitioning schemes, the effect of handoffs, and prioritization schemes, and other important issues in resource allocation such as overlay cells, frequency planning, and power control.
Abstract: This article provides a detailed discussion of wireless resource and channel allocation schemes. The authors provide a survey of a large number of published papers in the area of fixed, dynamic, and hybrid allocation schemes and compare their trade-offs in terms of complexity and performance. We also investigate these channel allocation schemes based on other factors such as distributed/centralized control and adaptability to traffic conditions. Moreover, we provide a detailed discussion on reuse partitioning schemes, the effect of handoffs, and prioritization schemes. Finally, we discuss other important issues in resource allocation such as overlay cells, frequency planning, and power control.

1,273 citations

Journal ArticleDOI
TL;DR: It is proved that the weighted graph coloring problem is NP-hard and scalable distributed algorithms that achieve significantly better performance than existing techniques for channel assignment are proposed.
Abstract: We propose techniques to improve the usage of wireless spectrum in the context of wireless local area networks (WLANs) using new channel assignment methods among interfering Access Points (APs). We identify new ways of channel re-use that are based on realistic interference scenarios in WLAN environments. We formulate a weighted variant of the graph coloring problem that takes into account realistic channel interference observed in wireless environments, as well as the impact of such interference on wireless users. We prove that the weighted graph coloring problem is NP-hard and propose scalable distributed algorithms that achieve significantly better performance than existing techniques for channel assignment. We evaluate our algorithms through extensive simulations and experiments over an in-building wireless testbed.

394 citations


"Performance Evaluation of a Self-Or..." refers methods in this paper

  • ...References [3,4] use weighted graph colouring with the weight calculation based o n a number of clients that are affected by the interfer nce affecting an AP on a particular channel....

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Proceedings ArticleDOI
01 Mar 2007
TL;DR: This paper reports on the design and experimental study of a distributed, self-stabilizing mechanism that assigns channels to multi-radio nodes in wireless mesh networks that takes a modular approach by decoupling the channel selection decision from the data forwarding mechanism.
Abstract: To increase the utilization of the available frequency channel space in 802.11-based wireless mesh networks, recent work has explored solutions based on multi-radio stations. This paper reports on our design and experimental study of a distributed, self-stabilizing mechanism that assigns channels to multi-radio nodes in wireless mesh networks. We take a modular approach by decoupling the channel selection decision from the data forwarding mechanism, which makes our solution readily applicable to real-world operation when used with emerging multi-radio routing solutions. We demonstrate the efficacy of our protocol on a real-world, 14-node testbed comprised of nodes, each equipped with an 802.11a card and an 802.11g card. We show via extensive measurements on our testbed that our channel assignment algorithm improves the network capacity by 50% in comparison to a homogeneous channel assignment and by 20% in comparison to a random assignment.

283 citations


"Performance Evaluation of a Self-Or..." refers background in this paper

  • ...In addition to previously discussed results for the algorithm, we have calculated the 98% confidence bounds per link for absolute interference values across all topologies and different network densities....

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  • ...The work in [7] specifically targets the channel assignment problem on WMN. Authors have adopted their theoretical work in [7] and created a self-stabilizing distributed protocol and an algorithm for channel assignment....

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Frequently Asked Questions (1)
Q1. What are the contributions in "Performance evaluation of a self-organising scheme for multi-radio wireless mesh networks" ?

The authors propose a generic self-organisation algorithm that addresses the two key challenges of scalability and stability in a WMN.