Centralized channel allocation algorithm for IEEE802.11 networks

Helga Balbi, Natalia Fernandes, Felipe Souza, Ricardo Carrano,Celio Albuquerque, Debora Muchaluat-Saade, Luiz Magalhaes

Universidade Federal Fluminense (UFF)

Niteroi, RJ - Brazil

E-mails: {helgadb, natalia, ferolim, carrano, celio, debora, schara}@midiacom.uff.br

Abstract—The sharing of the wireless spectrum is a majorconcern of network administrators. Access points in the samenetwork interfere with each other, degrading the aggregateperformance of stations. Moreover, wireless networks usuallycoexist with others applications that share the same spectrumand negatively impact the packet transmission. To overcome theseissues, we propose the channel allocation algorithm designed forcentral controllers of infra-structured IEEE 802.11 networks.Our algorithm reduces the interference in controlled access pointsthrough the dynamic choice of their operating channels and,unlike other proposals, was designed to operate in a networkcomposed of low cost devices from different brands, and opensource software. Furthermore, we also consider the interferencecaused by unmanaged networks, adjusting the settings of themanaged access points according to the wireless environment.The proposal was implemented and evaluated in an open testbed,and the results show that our controller efficiently managesthe spectrum with low cost equipment and a low complexityalgorithm.

I. INTRODUCTION

With the popularity of IEEE 802.11, scale issues began to

emerge. One of these issues is the difficulty to individually

manage each access point (AP) when the network includes

several APs. Another problem is the low management support

for coordinating APs. Lower cost devices have only a web

management interface, which, usually, do not consider other

APs, even those that are under the same management domain.

In order to provide a good throughput in a wireless network,

the administrator must reduce the interference among APs.

Nevertheless, even if the administrator manually allocates a

proper channel to each AP in order to minimize interference,

a static configuration will be obsolete in a fast changing

environment where new networks can be installed or excluded.

Likewise, even if there are no other on-site wireless networks,

the configuration and maintenance of each device must be

frequently performed, which is a hard task for larger networks.

To address these issues, the Project SCIFI - Intelligent Con-

trol System for Wireless Networks was created. The system

comprises a central controller which performs automatic and

dynamic configurations on low cost APs that run OpenWrt

embedded system [1].

This paper presents the main algorithm implemented in

SCIFI, responsible for defining the channel allocation of APs.

The algorithm was implemented and tested on an open testbed

in the School of Engineering of the Universidade Federal

Fluminense (UFF). Unlike many other proposals, SCIFI is ca-

pable of running with standard clients, increasing the system’s

compatibility with current devices. Furthermore, the system

considers the interference generated by APs belonging to other

networks, not only by managed APs, with the only require-

ment of using APs compatible with OpenWrt [1]. Hence, it

reduces costs when compared to other proprietary solutions

that demands special hardware. The tests results show that

our controller efficiently allocates channels to APs, increasing

the overall performance for both controlled and uncontrolled

networks that share the same radio medium.

This paper is organized as follows: Section II presents

prior related work on channel allocation techniques for 802.11

networks; Section III presents the channel allocation algorithm

implemented in SCIFI; Section IV presents the evaluation of

the algorithm, and finally Section V presents the conclusions

and future perspectives for SCIFI.

II. RELATED WORKS

At present, two unlicensed spectrum bands are available

to 802.11 standards: 2.4 GHz and 5 GHz. The 2.4 GHz

band, which is used by 802.11 b and g standards, has up to

14 channels available according to the region of the world.

However, in most countries, only three of these 14 channels

do not overlap [2]. In Brazil, for instance, the orthogonal

channels are 1, 6 and 11, since 12, 13 and 14 are not available.

When installing a wireless network, it is recommended that

neighboring APs are configured to operate in non-overlapping

channels in order to reduce interference. However, due to the

lack of such channels, the channel reuse becomes necessary in

dense networks. In this case, channel allocation techniques can

be used to determine a channel configuration that minimizes

the interference caused by the APs.

Proposals for channel allocation are divided in two main

groups: proposals that consider only the interference observed

by the APs, such as [3]–[8], and proposals that also consider

the interference observed by the client devices, such as [9]–

[11]. The use of client data reveals interference scenarios that

cannot be detected with AP data. However, this client data

depends on special clients that perform spectral scanning to

find other clients or APs that may interfere. Considering that

SCIFI requires compatibility with the full range of current

client devices, it does not depend on specific characteristics of

GIIS'12 1569662601

1

978-1-4673-5216-1/12/$31.00 ©2012 IEEE

client devices nor introduces changes to them. Therefore, our

channel allocation algorithm only considers the interference

caused between APs.

In some proposals, the channel allocation method allows

near APs to use non-orthogonal channels, i.e., channels whose

spectrum overlaps, as in [3]–[6], [9]–[11]. Mishra et al is said

to be the first proposal to explore this area, indicating that it is

more interesting that two APs operate in overlapping channels

than on the same channel [2]. Since in the first case the

interference radius of APs is reduced compared to the second,

it enhances the spatial reuse in a WLAN, increasing the

frequency with which the same channel can be used by other

APs. Riihijarvi et al, however, shows through experimental

results that, in cases where the channel interval between APs

is smaller than 4, using them on the same channel is more

interesting than in partially overlapping channels [6]. In fact,

the 802.11 MAC works better for APs operating on the same

channel than in nearby overlapping channels. Given that the

use of partially overlapping channels can cause significant

reduction in network throughput, in our proposal, as well as [7]

and [8], we chose to not use overlapping channels, although

our algorithm can be extended to consider the use of such

channels when including an appropriate weighting factor.

As highlighted by Bernaschi et al, another issue that can

be addressed during the channel allocation in a WLAN

is the interference caused by APs that are not under the

same management domain and, therefore, will not have their

channels changed by the algorithm [4]. We consider this

interference important for our system because, unlike most of

the proposals found in the literature, our algorithm will operate

in a controlled network that will share the medium with APs

that are not under the controller management. Therefore, our

proposal considers the interference caused by the operation of

nearby APs belonging to different management domains.

The proposals found in literature also differ in how the

computation of channel allocation is performed, and may use

linear programming or a heuristic. Proposals based on linear

programming, as in [11], have the disadvantage of requiring

considerable computational processing due to their complexity.

Because of their large processing time, these proposals are

more applicable to static cases, i.e., cases where the channel

allocation is performed only once or at long intervals. On the

other hand, proposals based on heuristics, such as [3], [5]–

[10], are interesting because of their reduced computational

complexity. In these cases, a suboptimal solution is acceptable.

Indeed, because of its fast computation, the channel allocation

can be performed at shorter intervals, in a dynamic and

adaptive way. As our solution aims to be adaptive, we chose

a heuristic to compute channel allocation.

Among the proposals based on heuristics, [3] and [7] use

propagation models for calculating the signal attenuation be-

tween interfering APs. The use of such models is not desirable

in our proposal because they do not accurately characterize

certain environments, such as indoor environments, in which

SCIFI system is intended to be used. The proposals that most

closely aligned with our requirements are [5], [6], [8]. In

[8] the authors showed that graph coloring techniques can be

used as theoretical basis to channel allocation algorithms for

802.11 networks. After verifying that the coloring problem

is NP-hard, the authors propose the use of DSATUR [12]

heuristic to its solution. In [5] the authors present algorithms

that use partially overlapping channels and adaptively perform

the coloring according to changes in network topology. In

[6] the authors complement DSATUR heuristics inserting a

new mechanism for using partially overlapping channels and

define the implementation architecture of the algorithm, which

includes a central server, on which the algorithm is executed,

and a protocol for exchanging messages between the server

and the APs and among APs.

The graph coloring through DSATUR heuristic is easily

implemented and presents short execution time. Hence, we

choose DSATUR as the basis of the channel allocation al-

gorithm implemented in SCIFI. To meet all the SCIFI re-

quirements, we modified the original algorithm, as can be

seen in the next section. The intended goal was to create

a centralized algorithm with easy implementation and fast

execution, which was able to define the channel allocation

in an WLAN reducing the interference caused between APs

that shares the same radio medium, including the managed and

unmanaged ones, without the need of deep changes in IEEE

802.11 standard nor in client devices, just by using a central

controller and APs compatible with OpenWrt firmware [1].

We opted for a centralized algorithm because, as the authors

conclude in [4], it prevents transient channel settings on the

network that would be caused if each access point could make

independent decisions in a decentralized manner. Moreover,

the convergence of the algorithm becomes faster when center-

ing channel allocation. On the other hand, as the authors in

[13] conclude, in terms of scalability decentralized algorithms

are considered more scalable, however, SCIFI aims to meet

small/medium sized institutional networks that do not have

large scale.

III. CHANNEL ALLOCATION ALGORITHM

This section describes the channel allocation algorithm

implemented on SCIFI, which is a system that aims to make

automatic and dynamic configurations of APs, improving the

manageability of IEEE 802.11 infra-structured networks. In

SCIFI, interference reduction is accomplished through the use

of centralized techniques for channel allocation, transmission

power control and client balancing among APs. The controller

orders each AP to execute certain tasks, such as performing

a spectral scanning or collecting statistics about associated

clients. Based on the collected data, the controller executes

the proposed algorithms. SCIFI is compatible with APs that

support OpenWRT operating system, and requires no deep

modification of the standard IEEE 802.11 protocols nor in

client devices, enabling the creation of large networks com-

posed by low cost devices from different brands.

Our channel allocation algorithm is modeled by an ”inter-

ference graph” in which nodes (vertices) represent APs and

if there’s interference between them, they must be connected

2

by unidirectional edges. Thus, the channel allocation problem

becomes the classic graph coloring problem, in which colors

represent the potential channels that APs can use. As the graph

coloring problem is classified as NP-hard, a heuristic is needed

for it to be solved in feasible time, such as DSATUR [8], [12].

The original DSATUR algorithm first calculates a ”saturation

degree” for each vertex, i.e., the number of different colors

that are already in use by neighboring vertices. If there’s only

one vertex, this is chosen to be colored. If there are more

than one vertex, they are ordered decreasingly according to

their saturation degree. Thus, the vertex with higher degree is

colored with the first available color.

In SCIFI, we improved this heuristic DSATUR according

to our requirements. First, to reduce the algorithm complexity

and take advantage of the global information of network, we

defined a centralized deployment, in which a central controller

collects information from APs and use them as basis for the

channel allocation. Moreover, we introduced the calculus of

the interference caused by networks that are not under the

same administrative domain. Also, the color choice is not made

in a greedy manner, like in original algorithm [8], [12]. Instead,

it is based on the quality of interfering signal received from

other APs. Furthermore, our algorithm considers the number

of associated clients with each controlled AP as a tiebreaker

for cases where there are more than one AP with the same

saturation degree.

The purpose of SCIFI’s channel allocation algorithm is to

define a set of channels for the controlled APs that comes

close to ideal in terms of minimizing the interference generated

by neighboring APs. In centralized deployment conducted for

validation and testing, the channel choice is restricted to one

of the three non-overlapping channels of 802.11g spectrum,

that is channels 1, 6 or 11, but the algorithm could easily be

adapted to 802.11a.

Figure 1 presents the basic pseudo code of the channel

allocation algorithm. Initially the central controller collects

data from APs, such as spectral scanning information (APs-

DataList[]). Based on this data, the algorithm determines the

interfering neighbors for each controlled AP, including APs

belonging to other management domains. Next, the controller

constructs the interference graph (”Graph” in line 1). This

graph consists of vertices representing the APs, and weighted

edges, which represent the interference between them. The

directional edges, which weighting value represents the quality

of received signal, are created if an access point is able to

receive signal from other. APs that do not belong to SCIFI’s

administrative domain are represented by already colored

vertices. Subsequently, the controller creates a list containing

the vertices that are not colored (”DiscoloredVerticesList[]” in

line 2). At first, discolored vertices are the controlled APs.

Each of discolored vertices will be colored in the block

”while” that begins in line 3 of Figure 1. Initially, the vertices

are ordered by priority in channel selection (line 4). Priority

is given to the vertex that has the highest saturation degree.

This degree indicates the number of different colors used by

neighboring vertices and does not consider vertices that were

not colored. If more than one vertex has the same saturation

degree, among them, the one with more associated clients will

have preference in channel selection. If there is a tie again,

priority is given based on the IP address of APs, so that a

lower IP has priority.

In our proposal, the interference generated by neighborhood

in channels different from 1, 6 or 11 is considered as it was oc-

curring in one of these channels (the nearest), since the spectra

of these channels partially overlaps. Given that this practice

can insert a small error on the interference calculating, in the

future we intend to implement a more refined mechanism for

interference estimates caused by overlapping channels. At first,

the interference generated by controlled APs is not considered

in the calculation of the saturation degree, as its new channel

will be determined only at the end of the algorithm execution.

But when an AP gets a new channel, it begins to affect the

saturation degree of its neighbors.

After determining which vertex will be colored, its color

must be defined. This task is accomplished by the function

”ColoringVertice” (Figure 1 - line 6), which simplified pseudo

code can be seen in Figure 2. Initially, a list containing all

interference edges of the vertex is created (line 1). Next, the

controller traverses this list to verify which of the possible

channels are occupied, i.e., which of them have an operating

AP. Such channels are removed from the list, leaving only

unoccupied channels (line 2). If there are unoccupied channels,

first channel of the list will be selected (line 4). If there are no

unoccupied channels, the controller will search for the channel

with less interference. For this, it traverses the edges list again

and performs the sum of edge qualities for each channel. The

channel that achieves the smallest sum of qualities will be

chosen (line 8).

We proposed this heuristic to select the channels because

we consider that the lower the observed signal quality of a

neighbor AP, the smaller the interference area of that neighbor

AP. Indeed, if the signal to noise ratio of the channel is low,

it indicates a greater distance between the APs, as shown

in Figure 3. This figure shows two scenarios that exemplify

our considerations on the relationship between the quality of

the received signal and the area of interference between two

access points. In scenario A, AP A receives signal from AP

B with high quality, and vice versa. In scenario B, AP A

receives signal from AP B with low quality, and vice versa.

In the latter scenario, as the lower quality indicates greater

distance between access points, the interference area between

them becomes smaller compared to the interference area in

scenario A. Thus, the controller must select the channel with

lower quality to reduce interference area.

If there is more than one neighboring AP on the same

channel, our algorithm considers the sum of all the neighbor

qualities in the channel. This sum reflects both the sum of the

interference areas and the number of APs that share medium

access. Furthermore, this metric also facilitates power control

and load balancing mechanisms, which can be executed in

SCIFI to increase the network performance, but these mecha-

nisms will not be discussed here for lack of space.

3

AllocateChannels(APsDataList[], PossibleChannelsList[])

1: Graph← BuildInterferenceGraph(APsDataList[])2: DiscoloredV erticesList[]← ListDiscoloredV ertices(Graph)3: while sizeofDiscoloredV erticesList[] > 0 do4: OrderedV erticesList←

OrderV erticesByPriority(DiscoloredV erticesList[])5: SelectedV ertice← OrderedV erticesList[0]6: ColoringV ertice(SelectedV ertice, PossibleChannelsList[])7: DiscoloredV erticesList[]←

RemoveV ertice(SelectedV ertice,DiscoloredV erticesList[])8: end while9: SetChannelsOnAPs();

Fig. 1. Basic pseudocode of SCIFI’s channel allocation algorithm.

ColoringVertice(SelectedV ertice, PossibleChannelsList[])

1: EdgesList[]← ListEdgesOfV ertice(SelectedV ertice)2: ListOfFreeChannels[]←

RemoveUsedChannels(EdgesList[], PossibleChannelsList[])3: if sizeofListOfFreeChannels[] > 0 then4: ChosenColor ← ListOfFreeChannels[0]5: DefineV erticeColor(SelectedV ertice, ChosenColor)6: return7: else8: ChosenColor ←

ChooseChannel(EdgesList[], PossibleChannelsList[])9: DefineV erticeColor(SelectedV ertice, ChosenColor)

10: return11: end if

Fig. 2. Basic pseudocode of SCIFI’s channel allocation algorithm.

Returning to Figure 1, after being colored (line 6), the

vertex is removed from the list of discolored vertices and is

marked as colored, so that its interference is considered by

the other discolored vertices. The process is repeated until

all vertices are colored. Finally, the channels of APs are

physically configured in accordance with the selected channels

(line 9). In order to adjust the channel allocation in front of

changes in the environment, such as the rise of new interfering

APs, the algorithm runs with time interval defined by network

administrator. Executions are scheduled by a timer in the

controller.

IV. ALGORITHM EVALUATION

We evaluated our algorithm through tests conducted on an

open infra-structured 802.11g testbed developed in Universi-

dade Federal Fluminense (UFF). This testbed was composed

of a personal computer that ran SCIFI controller, seven low

cost OpenWRT-based APs, and a switch that connected the

controller and the APs. In addition, seven laptops were used

as network clients, each one associated with a respective AP.

In the implementation of SCIFI used in tests, communication

between APs and controller was performed via SSH (Secure

Shell) and registration of APs to be controlled was done

manually, but future work include automatically discover of

APs.

Fig. 3. The quality of the received signal indicates the interference areabetween APs

Our test shows an example of a simple scenario among

several that can benefit with the use of SCIFI’s channel alloca-

tion algorithm. We demonstrate SCIFI benefits by comparing

the throughput obtained in the testbed operating with and

without our controller. In tests without SCIFI, we used a

channel configuration with minimal interference between the

controlled APs. This configuration, however, did not consider

neighboring APs of other administrative domains.

Figure 4 shows part of the plant of the area where the

testbed was set. Controlled APs are represented by a dark gray

circle and those that are not controlled are represented by a

circle containing an ”x”. Each client laptop was positioned

in a radius distance not greater than 2 meters from the AP

to which it was associated. Two models of APs were used

in the network, both manufactured by Ubiquiti [14]. One of

them was NanoStation Loco M2, which can be distinguished

in the figure by the presence of arrows representing the

direction of its 60◦ and 8 dBi sectorial antenna. Another

model was PicoStation 2, that has a 6 dBi omnidirectional

antenna. In all tests, the channels of the non-controlled APs

(represented by 04, 05, 06 and 07 in Figure 4) were 6, 6,

11 and 11, respectively. In the test without SCIFI, we set

the channels of controlled APs (C01, C02 and C03) as 6,

11 and 1, respectively, as shown in Figure 5. Notice that

this configuration avoids interference between controlled APs,

because it allocates a non-overlapping channel for each AP. By

using the described network structure, throughput tests were

performed to validate the implemented algorithm.

Iperf [15] was used to generate UDP traffic from each AP

to its respective associated client, using datagrams with 1470

bytes. UDP traffic was generated simultaneously from the

seven network APs and throughput samples were collected

once every 5 seconds. The first and last samples were dis-

carded, considering the small error that could be caused by

the non-simultaneously activation of Iperf in each AP. Each

sample reported by Iperf is the average throughput on that

interval.

In tests performed without SCIFI, 3626 samples were col-

lected and, in tests with SCIFI, 3430 samples were collected.

Each test was performed on two different days. The average

4

values of samples obtained by each access point are presented

in Figure 6 with error margins given by a 95 % confidence

interval. The error bars were small because of the large number

of samples. Comparing the results shown in figure 6 (a) and

(b), we note that SCIFI improves the throughput per AP in

most of the cases for both controlled and unmanaged APs.

This shows that the implemented algorithm and its interference

metric are effective for defining a better channel configuration,

in terms of throughput.

The main reasons for the SCIFI results are explained in fol-

lowing. In the tests with SCIFI’s channel allocation algorithm,

the controller collected the interference information contained

in table I. This table shows which access points were found by

each controlled AP after performing a spectral scanning and

the quality of the received signal from each found AP. For

example, analyzing the intersection between column C01 and

line 05, we conclude that AP C01 receives signal from AP

05 with the highest quality (70/70). Cells with a ”-” represent

that there is no communication between the APs, as occurs

between C01 and C02. The range of qualities, which varies

from 0 to 70, is determined by the wireless interface driver.

The access points used in the tests work with Madwifi or

Ath9k drivers and the quality value reported by these drivers

represents the average Signal to Noise Ratio calculated for the

last received frames [16].

With the information from Table I the controller builds the

interference graph presented in figure 7, which is the basis for

the algorithm execution. Finally, figure 8 shows the channel

allocation calculated by SCIFI. As can be seen, the channels

1, 6 and 1 were assigned to the controlled APs C01, C02 and

C03, respectively. Although it seems worthwhile to use the

three managed APs in channels 6, 11 e 1 respectively (figure 6

- a), results were better when two of them operated on channel

1 (figure 6-b), since in this configuration they suffered less

interference.

Figure 9 shows the average aggregate throughput for both

the scenarios with and without SCIFI, considering error mar-

gins given by a 95% confidence interval. Figure 9 (a) shows

the aggregate throughput of the network, including the seven

APs. In this graph, we can note the significant increase of

network throughput (more than 29% in this scenario) when

SCIFI was used. Part of this increase was caused by the

throughput improvement of managed APs, as shown in figure

9 (b). Another part was due to the throughput improvement of

unmanaged APs, as shown in figure 9(c). These results show

that the network as a whole, including neighboring networks

that are not under the same administrative domain, may benefit

from the use of the algorithm described in this paper.

V. CONCLUSION

This paper presented a new centralized channel allocation

algorithm for infra-structured 802.11 networks designed to op-

erate in a central controller for low cost APs called SCIFI. The

main objective of the algorithm is to reduce the interference

among APs and adjust their settings automatically according

to the ambient conditions, considering the interference caused

Fig. 4. Position of APs in testbed

Fig. 5. Channel configuration used in test performed without SCIFI

Fig. 6. Graphs of average throughput per AP

by APs belonging to other administrative domains whose

5

TABLE IQUALITY OF RECEIVED SIGNAL

Channel

�������������Found APs:

AP whichexecutes scan: C01 C02 C03

- C01 x - 38/70

- C02 - x 50/70

- C03 35/70 46/70 x

6 04 39/70 34/70 70/70

6 05 70/70 - 29/70

11 06 - 70/70 49/70

11 07 31/70 49/70 70/70

Fig. 7. Interference Graph built by SCIFI

Fig. 8. Channel configuration used in test performed with SCIFI

channels can’t be changed by the controller.

Among the other proposals in the literature, ours is distin-

guished mainly by having focus on its applicability in current

networks, without requiring major changes in 802.11 standard

or client devices. To quantify the interference, the algorithm

uses a metric based on the quality of communication channel

among neighboring APs, whose value can be obtained through

beacon frames.

Finally, the algorithm was validated on a network composed

by seven APs, in which throughput tests were performed

comparing the implemented solution with another that does

not consider the interference caused by neighboring APs that

belongs to other administrative domains. The results showed

that the network as a whole, including neighboring unmanaged

networks, can benefit by the use of the algorithm described in

Fig. 9. Graphs of average agregated throughput

this paper. Currently, a network of 60 nodes controlled by

SCIFI is being installed in UFF for large-scale testing. In the

future, the system will be available under the GPL license.

ACKNOWLEDGMENT

The work described in this paper was supported by RNP

(Rede Nacional de Ensino e esquisa).

REFERENCES

[1] (2012) OpenWRT website. [Online]. Available: https://openwrt.org/[2] A. Mishra, E. Rozner, S. Banerjee, and W. Arbaugh, “Exploiting

partially overlapping channels in wireless networks: turning a peril intoan advantage,” in Proceedings of the 5th ACM SIGCOMM conferenceon Internet Measurement - IMC ’05. USENIX Association, Oct. 2005,pp. 29–29.

[3] R. Akl and A. Arepally, “Dynamic channel assignment in IEEE 802.11networks,” in IEEE International Conference on Portable InformationDevices - PORTABLE07, May 2007, pp. 1–5.

[4] M. Bernaschi, F. Cacace, A. Davoli, D. Guerri, M. Latini, and L. Vollero,“A CAPWAP-based solution for frequency planning in large scalenetworks of WiFi hot-spots,” Computer Communications, vol. 34, no. 11,pp. 1283–1293, Jul. 2011.

[5] J. Riihijarvi, M. Petrova, and P. Mahonen, “Frequency allocation forWLANs using graph colouring techniques,” in Second Annual Confer-ence on Wireless On-demand Network Systems and Services - WONS2005, Jan. 2005, pp. 216 – 222.

[6] J. Riihijarvi, M. Petrova, P. Mahonen, and J. Barbosa, “Performanceevaluation of automatic channel assignment mechanism for IEEE 802.11based on graph colouring,” in IEEE 17th International Symposium onPersonal, Indoor and Mobile Radio Communications, Sep. 2006, pp.1–5.

6

[7] K. Leung and B.-J. Kim, “Frequency assignment for IEEE 802.11wireless networks,” in IEEE 58th Vehicular Technology Conference -VTC, vol. 3, Oct. 2003, pp. 1422–1426.

[8] P. Mahonen, J. Riihijarvi, and M. Petrova, “Automatic channel allocationfor small wireless local area networks using graph colouring algorithmapproach,” in 15th IEEE International Symposium on Personal, Indoorand Mobile Radio Communications - PIMRC, vol. 1, Sep. 2004, pp.536–539.

[9] A. Mishra, V. Brik, S. Banerjee, A. Srinivasan, and W. Arbaugh, “Aclient-driven approach for channel management in wireless LANs,” inProceedings of the 25th IEEE International Conference on ComputerCommunications - INFOCOM 2006, Apr. 2006, pp. 1 –12.

[10] A. Mishra, S. Banerjee, and W. Arbaugh, “Weighted coloring basedchannel assignment for WLANs,” ACM SIGMOBILE Mobile Computingand Communications Review, vol. 9, no. 3, pp. 19–31, Jul. 2005.

[11] M. Haidar, R. Ghimire, H. Al-Rizzo, R. Akl, and Y. Chan, “Channelassignment in an IEEE 802.11 WLAN based on signal-to-interferenceratio,” in Canadian Conference on Electrical and Computer EngineeringCCECE, May 2008, pp. 1169–1174.

[12] D. Brelaz, “New methods to color the vertices of a graph,” Communi-cations of the ACM, vol. 22, no. 4, pp. 251–256, Apr. 1979.

[13] S. Chieochan, E. Hossain, and J. Diamond, “Channel assignmentschemes for infrastructure-based 802.11 wlans: A survey,” IEEE Com-munications Surveys Tutorials, vol. 12, no. 1, pp. 124 –136, first quarter2010.

[14] (2012) Ubiquiti networks. [Online]. Available: http://www.ubnt.com[15] (2012) Iperf. [Online]. Available: http://iperf.sourceforge.net[16] (2012) RSSI in MadWifi. [Online]. Available: http://madwifi-

project.org/wiki/UserDocs/RSSI

7