Performance Evaluation 68 (2011) 825–840

Contents lists available at ScienceDirect

Performance Evaluation

journal homepage: www.elsevier.com/locate/peva

Improving densely deployed wireless network performance inunlicensed spectrum through hidden-node aware channel assignmentSuparerk Manitpornsut a,∗,1, Björn Landfeldt a,∗, Azzedine Boukerche b

a School of Information Technologies J12, University of Sydney, NSW 2006, Australiab School of Information Technology and Engineering, University of Ottawa, Canada

a r t i c l e i n f o

Article history:Available online 19 January 2011

Keywords:Wireless LANRadio spectrum managementWireless network performanceChannel assignment

a b s t r a c t

It is well known that a wireless local area network (WLAN) based on the IEEE 802.11standard suffers from interference and scalability problems due to the limited numberof non-overlapping channels. In order to mitigate the interference problem, channelassignment algorithms has been a popular research topic in recent years. It has been shownthat such algorithms can greatly reduce the interference among wireless access points.However, in this paper, we show that previously proposed channel assignment algorithmsmay lead to an increased number of hidden nodes in dense network deployments. We alsoshow that this can significantly decrease the performance of the network. Furthermore,we present results from experiments showing that the Request to send (RTS)/Clear tosend (CTS) mechanism is unable to solve the hidden node problem in infrastructureWLANs, and therefore careful consideration needs to be taken when choosing channelassignment strategies in densely deployed wireless networks. To this end, we proposeboth a centralized channel assignment algorithm and a distributed channel assignmentalgorithm.Using a simulation study,we show that the proposed algorithms can outperformtraditional channel assignment in densely deployed scenarios, in terms of QoS sensitiveVoIP support without compromising the aggregate throughput, and that they are thereforea better performing alternative in such settings.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

The IEEE 802.11 wireless local area network (WLAN) has emerged as a network technology, popular in both residentialand commercial applications. It has been shown in [1] that the popularity of 802.11 WLAN devices can threaten theirperformance, especially in residential settings where wireless networks are unplanned and densely deployed. These resultsfollow thewide recognition that signal interference in the ISMband can reduce theWLANperformance. Several studies [2–8]have shown the effect of interference on WLAN performance from household appliances, e.g. microwave ovens, Bluetoothdevices, and cordless phones. In addition, co-channel interference can also impact onWLAN performance since the numberof non-overlapping channels in IEEE 802.11 [9] is limited (e.g. three channels in 802.11 b/g). In recent years, channelassignment algorithms [10–14] have been proposed tomitigate this problem. Themain focus of these algorithms is to assigna channel to eachwireless network so that the overall interference isminimized using space division and enhancing channelreuse, as shown in Fig. 1.

∗ Corresponding author.E-mail addresses: suparerk@it.usyd.edu.au (S. Manitpornsut), bjorn.landfeldt@sydney.edu.au (B. Landfeldt), boukerch@site.uottawa.ca (A. Boukerche).

1 On leave from the Department of Computer Engineering, the University of the Thai Chamber of Commerce, Thailand. Currently he is with the Schoolof Information Technologies, University of Sydney, Sydney, Australia.

0166-5316/$ – see front matter© 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.peva.2011.01.002

826 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

a b c

Fig. 1. WLAN topology. (a) Original topology: only access points (APs) are shown. (b) Assuming that only three non-overlapping channels (black, grey,and white) are available and assigned to APs as well as their associated wireless stations (not shown). (c) Space division and channel reuse (highlighted onwhite channel only).

We investigated the effectiveness of these algorithms using a simulation study, and found that it is only when thefollowing two conditions are satisfied that space division from existing channel assignment algorithms can improve theoverall performance of a WLAN:• the network density DN is low, and• the average number of wireless stations per network Ns is low.

We have investigated the cause of this behavior and found that, asDN andNs increase, the hidden node problem becomespronounced, which in turn leads to significantly degraded network performance, especially in terms of voice support. Withthe rising popularity of residential voice over WLAN, this finding points to a foreseeable degradation in user satisfaction, asWLAN deployments increase in density.

In this paper, we present our study of channel assignment and present methods to mitigate the shortcoming of previousproposals, making the following contributions.• We investigate the impact that the hidden node problem has on voice sessions in terms of mean opinion score (MOS) as

a metric of voice quality. In doing so, we revisit the performance of the RTS/CTS handshaking mechanism and reaffirmthat it does not solve the hidden node problem.• We put forward that the hidden node problem is an undesired side-effect of currently proposed channel assignment

algorithms and show that, in densely deployed WLANs, this has a significant negative effect on voice over WLANperformance.• We propose two novel approaches for densely deployed WLAN scenarios, which consider the hidden node problem

in order to overcome this limitation, a centralized channel assignment algorithm and a distributed channel assignmentalgorithm.

In the next section, we discuss related work before presenting the hidden node problem and its effect on infrastructureWLANs in Sections 3 and 4. Our proposed algorithms are introduced in Section 5, and then the results of our algorithmsand those of existing algorithms are compared in Section 6. We discuss our centralized channel assignment algorithm inSection 7, explain our distributed channel assignment algorithmand compare its performancewith the centralized approachin Section 8. Finally, we conclude our work in Section 9.

2. Related work

There are twomediumaccessmechanisms specified in the IEEE 802.11 standard [9], the distributed coordination function(DCF) and the point coordination function (PCF).

The DCF is based on carrier sense multiple access with collision avoidance (CSMA/CA), and it is also the fundamentalmedium access protocol for a WLAN. The PCF on the other hand, is a polling-based mechanism which is optional.

There exist numerous analytical and experimental studies, e.g. [15–20], on the performance of the DCF. It is well knownthat multiple DCF wireless networks are able to seamlessly share the medium capacity without the use of a centralizedcontroller.

Several studies [21–23] show that enabling the PCF and fine-tuning the operation parameters can outperform WLANswith only the DCF enabled. However, it has been shown [24], that multiple uncooperative PCF-enabled networks that arewithin interference range can suffer from severe interference issues, leading to poor support for delay-sensitive applicationssuch as VoIP. Due to the complexity of the PCF, we therefore concentrate our study on DCF-based networks in this paper.

To boost spectrum reuse and spectrum utilization, several channel assignment algorithms have been proposed in recentyears.

Riihijarvi et al. [25] introduced a graph-based representation of interference among nodes in WLAN topologies. In orderto maximize the system throughput, the authors proposed a channel allocation algorithm based on graph coloring. Thealgorithm was shown to perform well when the interference graph was of low degree.

Mishra et al. [14] applied a weighted channel assignment (WCA) algorithm, also based on graph coloring, to promotechannel reusewhile considering adjacent channel interference. The proposed algorithmwas shown to performwell in termsof the number of conflict edges and number of channels used in the tested topologies.

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 827

From previous studies [26], it has been shown that greedy channel assignment algorithms (e.g. WCA) perform poorly ifthe networks become too dense. Furthermore, the input, based only on the interference among access points (APs), usuallyignores the hidden node problem which can occur if the network density is sufficiently high that the channel assignmentalgorithm cannot provide low interference levels. By their nature, the algorithms try to assign channels to APs so that theinterfering APs are as spatially separated as possible. Thismechanismworkswell at low network density, but at high densitythe algorithm cannot provide a low interference level, which exacerbates the hidden node problem, as we show in the nextsection. Later in the paperwe showhow this leads to suboptimal performance, and therefore the severity of the hidden nodeproblem should be considered when assigning channels in dense WLAN deployments.

3. Hidden node problem in infrastructure WLANs

The hidden node problem is one of the classic problems in wireless networks. We refer readers who are interested in aperformance analysis of WLANs under the hidden node problem to [27] for further details. In this section, we explain theproblem and show results from a simulation study on the probability of nodes being hidden from each other.

In infrastructure WLANs, the hidden node problem can be categorized into internal and external hidden node scenarios.The internal hidden node scenario occurs when a wireless station is within transmission range of an access point (AP)

but it is not within range of other wireless stations in the same wireless network.In the external hidden node scenario, an AP in one network is unaware of transmissions from APs in other networks,

thus leading to packet collision at a wireless station covered by both APs. The external hidden node scenario is becomingincreasingly common asWLANs are deployedwithmore andmore overlapping private home networks, enterprise networksand public networks.

3.1. Probability of nodes being affected by internal hidden nodes

We carried out a simulation study to find the likelihood of nodes being affected by hidden nodes. We assumed that allSTAs had the same transmission range of 100m andwere placed randomlywithin transmission range of the AP (also 100m).The average number of STAs per BSS (basic service set) was then varied from 1.5 to 5.5. We obtained the rather intuitiveresult that the higher the number of STAs per BSS is, the more likely it is that nodes are affected by at least one internalhidden node.

3.2. External hidden node scenario

In order to examine the probability of nodes being affected by at least one external hidden node, we simulated traffic ina network with size 200× 200 m2 while varying the network density DN :

DN =Nbss

A, (1)

whereNbss is the number of BSSs (a BSS consists of one AP and a number of STAs) and A is the network area (m2). The averagenumber of STAs per BSS in these experiments was 2.

In order to have a baseline performance to measure against we simulated the worst possible performance, allocating thesame channel to all networks. We also studied the performance of random channel allocation to investigate if WCA has anoverall positive or negative performance impact. In all experiments we used three non-overlapping channels (1, 6 and 11).There is an overlapping channel allocation feature inWCA that was disabled in our experiments. The results from 10 samplenetworks for each data point with confidence interval 0.95 presented in Fig. 2 show that the random channel assignmentalgorithm can provide lower probability of nodes affected by at least one external hidden node than WCA does.

Since WCA considers the interference only at APs, the algorithm can provide space division among APs but this does notinclude STAs. Later in this paper, we show that this side effect can lead to poor support for delay-sensitive applications suchas voice.

After having established the probability of nodes being affected by the hidden node problem, we investigate the effect ofhidden nodes on the performance of infrastructure WLANs.

4. Effect of hidden nodes in infrastructure WLANs

The RTS/CTS handshaking mechanism was introduced in order to overcome the hidden node problem. The performanceof RTS/CTS has been studied using both analytical and experimental approaches; see, e.g., [28–31]. However, recentstudies [32–34] show that the RTS/CTS mechanism might not be an ideal choice to solve the hidden node problem.

We contribute by investigating the effect of the hidden node problem on voice sessions through simulation and liveexperiments on a wireless test-bed. In doing so, we also reinforce the results that RTS/CTS does not solve the hidden nodeproblem. Note that the results presented herein are plotted from the average values from 10 runs for each data point with0.95 confidence intervals.We intentionally omitted the error bars in all graphs since the intervals are too small and the errorbars make the graphs unclear and this would distract readers from the points we would like to present.

828 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

Fig. 2. Probability of nodes being affected by external hidden nodes.

4.1. Simulation study on the performance of WLANs under the hidden node problem

We used the OPNET Modeler [35] to carry out performance studies of WLANs under internal and external hidden nodescenarios. The simulation study was carried out to investigate the basic performance of RTS/CTS compared with the basicoperation (without RTS/CTS enabled). Fine-tuning of parameters for specific scenarios were therefore beyond the scope ofthese experiments.

4.1.1. Internal hidden node scenarioThe experimental set-up for this scenario consisted of a single APwith two stations, STA-1 and STA-2. In the experiments,

we assign FTP traffic to both STA-1 and STA-2. The offered load of STA-2 is kept constant at 6 Mbps and the offered load ofSTA-1 is varied from 1 to 6 Mbps. Sandvine [36] presented statistical data that indicates that upstream traffic is increasingdue to the popularity of peer-to-peer traffic. We accommodated for this by using 100% downstream FTP traffic (all traffic isfrom the AP to the STA) and a mix of 50% downstream 50% upstream FTP traffic (the overall offered traffic is 50% from theAP to the STA and 50% from the STA to the AP) in the internal hidden node scenario. We compared the normalized receiverthroughputs of STA-1 and STA-2 when the offered loads were 100% downstream traffic, where the normalized throughputof node i, Si, is defined as

Si =Throughput

Offered Bandwidth. (2)

Note that we did not measure the AP throughput under the 100% downstream traffic scenario because the AP receivedMAC ACK frames almost exclusively. We noted that both STA-1 and STA-2 are unable to gain any benefit from RTS/CTShandshaking. Obviously, with 100% downstream traffic, we would expect only a few packet collisions at the AP (only MACACK collisions), so the number of retransmissions due toMACACK timeout is very limited. However, the RTS/CTSmechanismadds more overhead, in fact reducing the throughput.

Fig. 3 shows that with amix of 50% downstream 50% upstream traffic, RTS/CTS handshaking can enhance the normalizedthroughput at the AP by about 10% at the expense of STA-1 throughput, by 17% on average.

Next, we consider the ability ofWLANs to support VoIP under the internal hidden node scenario. We use the same set-upas in the previous simulation study except that STA-2 now carries bidirectional 64 kbps PCM voice traffic. Themean opinionscore (MOS) for downstream (from the AP to STA-2) and upstream (from STA-2 to the AP) voice traffic are shown in Fig. 4.

According to ITU-T Recommendation G.107 [37], in order to provide good quality voice communication, the MOS valuesof both upstream and downstream streams should be more than 3.6. The recommendation suggests how to estimate theMOS using the E-Model as follows:

MOS =

1 for R ≤ 01+ 0.035R+R(R− 60)(100− R)7× 10−6 for 0 < R < 1004.5 for R ≥ 100,

(3)

where the R-factor, R, is a function of the basic signal to noise ratio (R0), simultaneous impairment factor (Is), equipmentimpairment factor (Ie), delay impairment factor (Id), and the advantage factor (A).

R = R0 − Is − Ie − Id + A. (4)

The default value of R0 according to ITU-T Recommendation G.107 is 93.2. This implies that the theoretical maximumR-factor (without any advantage factor) is 93.2, as a result yielding a highest MOS value of 4.41. The following results arecalculated by using the default values defined in [37].

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 829

Fig. 3. Normalized throughput comparison of STA-2 and AP under the internal hidden node scenario with and without RTS/CTS when 50% downstream50% upstream traffic is applied in both STA-1 and STA-2.

Fig. 4. Mean opinion score of downstream (from the AP to STA-2) and upstream (from STA-2 to the AP) voice traffic under the internal hidden nodescenario.

From Fig. 4, we can infer that an STA can offer such a good quality voice connection only when the aggregate bandwidthof the internal hidden nodes is less than 2 Mbps, regardless of whether RTS/CTS is applied.

Under the internal hidden node scenario, from the results above, we can conclude that the RTS/CTS mechanism canenhance the throughput of an AP only when there is substantial upstream traffic. This situation can be found, for example,in file sharing using peer-to-peer protocols.

On the other hand, if themajority of the traffic is downstream traffic, the RTS/CTSmechanism cannot augment the systemperformance for both best-effort (FTP) and delay-sensitive (voice) applications under the internal hidden node scenario.

4.1.2. External hidden node scenarioIn this section, we extend our discussion to the performance of RTS/CTS in infrastructure WLANs under the external

hidden node scenario. Our experimental scenario consists of AP-1 with a single STA-1 attached and AP-2with a single STA-2attached, where STA-1 overhears transmissions from both APs.

We study FTP as an example best-effort application and VoIP as an example delay-sensitive application. First,downstream FTP traffic is assigned to both STA-1 and STA-2. The offered FTP bandwidth at STA-1 is held constant at 6Mbps and the offered bandwidth at STA-2 is varied from 1 to 6 Mbps. Since the link between STA-1 and AP-1 is hidden toAP-2, we expect that this link should perform poorly without RTS/CTS enabled, and it should have a better performancewhen RTS/CTS is enabled. However, interestingly, the RTS/CTS mechanism cannot solve this kind of hidden node problem,as shown in Fig. 5.

The figure shows that the normalized throughput of STA-1 is very low, and even worse than without RTS/CTS applied.Second, we examine voice traffic under the external hidden node problem.We assign bidirectional 64 kbps PCM voice trafficto STA-1 and then vary the offered FTP bandwidth at STA-2 from 1 to 6 Mbps. Fig. 6 illustrates the MOS of the voice traffic.

Note that the upstream voice traffic (from STA-1 to AP-1) has a very low delay (approximately 41 ms), resulting ina high MOS value (about 4.02) for all interfering traffic from external hidden nodes. On the other hand, the quality of

830 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

Fig. 5. Normalized throughput of STA-1 and STA-2 under the external hidden node scenario when RTS/CTS is ON (with RTS) and OFF (without RTS).

Fig. 6. Mean opinion score of voice traffic at STA-1 under the external hidden node scenario.

downstream voice (fromAP-1 to STA-1) is sensitive to interfering traffic from external hidden nodes. Only 1Mbps aggregatebandwidth fromexternal hidden nodes can be tolerated in order to provide good quality voice. As discussed previously, voicecommunication is bidirectional, so we conclude that the aggregate bandwidth from external hidden nodes which is greaterthan 1 Mbps can prevent the STA from providing good-quality voice in this scenario.

Again, this provides evidence that the RTS/CTS mechanism cannot boost the system performance in both best-effort anddelay-sensitive applications under the external hidden node scenario. In the next subsection, we conduct an experimentwith the external hidden node scenario on our wireless test-bed to solidify our findings.

4.2. Experimental study on the performance of a WLAN under the hidden node problem

To reinforce the simulation results that the RTS/CTSmechanismcannot increase the systemperformance in infrastructureWLANs, an experimental study was set up as follows.

Nodeswere placed along a straight line as follows, AP-1–STA-1–AP-2–STA-2. A transmission power of 18 dBm (measuredby theMadWifi driver [38]) is used during the test, instead of decreasing the transmission power in order to reduce the effectof ambience noise. In addition, a spectrum analyzer was used to eliminate conflicting transmissions from external sources.With the Iperf [39] performance measurement software installed on all nodes, TCP traffic from AP-1 to STA-1 starts at timet = 0 and is maintained to the end of the test at time t = 600 s, and TPC traffic from AP-2 to STA-2 starts at time t = 120 sand is maintained to time t = 600 s. The RTS/CTS mechanism is enabled on all nodes. Fig. 7 displays the instantaneousthroughput of STA-1 and STA-2.

As expected, STA-1 canmanage nearly saturated throughput (approximately 6 Mbps) only when there is no interferencefrom AP-2. When TCP traffic from AP-2 to STA-2 starts, the throughput of STA-1 drops to almost zero. Table 1 shows theresults of experiments with the TCP retransmission counts of AP-1 and AP-2. It was found that the TCP retransmission countof AP-1 with RTS/CTS enabled decreased only 1.85% from the basic operation case while at the same time the retransmissioncount of AP-2 increased by approximately 20%. In addition, the average number of packets in the buffer of the WLAN

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 831

Fig. 7. Instantaneous throughput of STA-1 and STA-2 with RTS/CTS, captured by Iperf on the test-bed.

Table 1Results under the external hidden node scenario.

Statistic With RTS Without RTS

TCP retransmission count at AP-1 16,267 16,574TCP retransmission count at AP-2 10,072 7915Average packets in buffer at AP-1 22.49 19.36Average packets in buffer at AP-2 5.36 4.63

interface of AP-1 were approximately 4.2 times larger than those of AP-2, regardless of RTS/CTS being enabled or not. Thisdemonstrates the reason why RTS/CTS cannot alleviate the hidden node problem in infrastructureWLANs. The results fromthis experiment therefore verified the simulation results.

From all the above results, we deduct that interference from hidden nodes has a great effect on WLAN performance.Since there is no absolute solution to solve the hidden node problem in infrastructure WLANs, in order to provide the spacedivision and enhance channel reuse in densely deployedWLANs, we therefore conclude that the hidden node problemmustbe addressed.

5. Proposed channel assignment algorithms

Following the investigation of the severity of the hidden node problem in densely deployed WLANs, we move onto proposing algorithms for mitigating the channel assignment problem. In the internal hidden node scenario, channelassignment cannot alleviate the performance problem because the problem depends on the location and bandwidthrequirement of eachwireless stationwithin the same network, using a single channel. However, it is possible to alleviate theeffects from external hidden nodes using our proposed channel assignment algorithm, called the weight of affected nodesby hidden nodes (WANHN) channel assignment algorithm, described below.

5.1. Definition

Given a wireless network topology, a hidden nodes graph G = (V , E) is defined as follows: V is a set of vertices whichrepresent all wireless nodes in the topology; A is a set of APs and S is a set of STAs (i.e. V = A ∪ S). E is a set of edgesexy = vi

xvjy, linking vertex vi

x (wireless node x in bssi) and vertex vjy (wireless node y in bssj), where vi

x = vjy.

LetWI(vix) be the sum weight of edges incident to vertex vi

x:

WI(vix) =

−∀vix∀v

jy∈V

B(vjy) · HI(v

ix, v

jy), (5)

where B(vjy) is the bandwidth of vj

y and HI(vix, v

jy) is a function defined as

HI(vix, v

jy) =

1 if vj

y ∈ Nx

0 if vjy ∈ Nx,

(6)

where Nx is a set of neighboring nodes of vix. Informally, WI(v

ix) is the sum of the bandwidth of the internal hidden nodes

experienced by vix.

832 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

Algorithm 1WANHN1. Randomly choose channel c from a set of available channels and assign it to each bss.2. Calculate FWANHN =

∑∀c∈C Wsum(vi

x, c).

3. P(c)← Wsum(vix,c)FWANHN

.4. PAcc(c)←

∑ci=1 P(c).

5. Randomize a real number n, 0.0 < n < 1.0.6. Choose c ′ such that PAcc(c ′) is the closest value to n and PAcc(c ′) < n.7. Assign c ′ to vi

x.8. Repeat step 2 for all vi

x ∈ V .

Let WE(vix, c) be the weight function of edges from external hidden nodes on G when channel c ∈ C (a set of available

channels {1 . . . k}) is assigned to vix, defined as

WE(vix, c) =

−∀v

jy

B(vjy) · HE(v

ix, v

jy, c) (7)

HE(vix, v

jy, c) =

1 if vj

y ∈ Ix(c)0 if vj

y ∈ Ix(c),(8)

where Ix(c) is a set of nodes interfering with vix when it is assigned channel c , defined as follows:

Ix(c) = {∀vjy ∈ A, i = j|RSS(vi

x, vjy, c) > RSSmin}. (9)

RSS(vix, v

jy, c) is the received signal strength from APy in bssj(v

jy) experienced by STAx in bssi(vi

x) and RSSmin is the lowerlimit of the received signal strength that STAx requires to successfully receive packets. The total hidden node bandwidthWsum(vi

x, c) seen by vix is calculated as

Wsum(vix, c) = WI(v

ix)+WE(v

ix, c). (10)

5.2. Algorithms

Previous work has not considered the effects of the hidden node problem as it has been assumed that the RTS/CTSmechanismwill solve this issue. However, as we have shown in the previous sections, this does not hold true. Therefore, theobjective function of WANHN aims to minimize the collective amount of interfering traffic from hidden nodes Wsum(vi

x, c)of all nodes in G. The procedure of WANHN is shown in Algorithm 1.

Previous work has identified channel occupation as the main optimization criterion for efficient channel allocation(e.g. [14,26]). In order to obtain the best overall performance we construct an algorithm named combined channelassignment (CCA), which combines channel occupation (degree of interference) DOI(vi

x, c) and interfering traffic fromhidden nodes Wsum(vi

x, c) in its objective function. In [26], we showed that the probabilistic approach taken in WACCAoutperforms the WCA greedy approach. Thus, CCA is effectively made up of a combination of WANHN and WACCA.

The following section describes how the CCA objective function is constructed. Taken from [26], DOI(vix, c) is defined as

follows:

DOI(vix, c) =

−∀v

jy∈V∧i=j

ILF(vix, v

jy) · IL(v

ix, v

jy, c), (11)

where ILF(vix, v

jy) is the interference loading factor and IL(vi

x, vjy, c) is the interference level between vi

x and vjy. ILF(vi

x, vjy) is

a weighted function of the bandwidth of vjy which is interfering with node vi

x, defined as

ILF(vix, v

jy) =

TC−t=1

Lyt · wt , (12)

where Lyt is the current load of access category t of node vjy and wt is the weight of access category t .

IL(vix, v

jy, c) is the normalization function of the received signal strength (RSS) on channel c from node v

jy to node vi

x withrespect to the minimum received signal strength from all members in bssi.

IL(vix, v

jy, c) =

RSS(vix, v

jy, c)

min[{RSS(vix, v

iz, c)}]

, (13)

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 833

Algorithm 2 CCA1. Randomly choose channel c from a set of available channels and assign it to each bss.2. Calculate FCCA,sum =

∑∀c∈C WCCA(v

ix, c).

3. P(c)← WCCA(vix,c)FCCA,sum(vix)

.

4. PAcc(c)←∑c

i=1 P(c).5. Randomize a real number n, 0.0 < n < 1.0.6. Choose c ′ such that PAcc(c ′) is the closest value to n and PAcc(c ′) < n.7. Assign c ′ to vi

x.8. Repeat step 2 for all vi

x ∈ V .

Table 2Scenario settings.

Scenario Network density NSTA

1 1.25× 10−4 2.02 1.25× 10−4 3.53 1.25× 10−4 5.04 2.50× 10−4 2.05 2.50× 10−4 3.56 2.50× 10−4 5.07 3.75× 10−4 2.08 3.75× 10−4 3.59 3.75× 10−4 5.0

10 5.00× 10−4 2.011 5.00× 10−4 3.512 5.00× 10−4 5.0

where viz ∈ bssi. We refer readers to [26] for complete details. The objective function of CCA is then defined as

WCCA(vix, c) = α ·Wsum(vi

x, c)+ β · DOI(vix, c), (14)

where α and β are weights for fine-tuning the performance of the algorithm and α + β = 1. When α = 1 and β = 0, CCAbecomes WANHN, and when α = 0 and β = 1, CCA behaves like WACCA. The procedure of CCA is shown in Algorithm 2.

6. Simulations

Weevaluated the performance of the proposed algorithms using theOPNETModeler [35].We compared the performanceof our new combined approachwith the existingWCA approach using the simulation set-up detailed below.We also studiedthe performance of WANHN separately to gain better understanding of the effects of optimizing for hidden node trafficcompared with optimization for degree of interference.

6.1. Simulation scenario

Three channel assignment algorithms are compared in this study.

1. WCA: based on Hminmax defined by Mishra et al. [14].2. WANHN: our first proposed algorithm explained in the previous section.3. CCA: our second algorithm with empirically determined values of α = 0.7 and β = 0.3, which yielded the best results.

All three algorithms were tested in 12 scenarios with a different value of network density (DN ) and the average numberof STAs per AP (NSTA) as listed in Table 2.

These tested scenarios represent low-density networks (scenarios 1–3), medium-density networks (scenarios 4–9) andhigh-density networks (scenarios 10–12). The average number of STAs (NSTA) was set to 2.0, 3.5 and 5.0 to represent low,medium and high bandwidth requirement.

6.2. Simulation parameters

APs were uniformly distributed throughout the network area. All wireless nodes had 100 m transmission range and allSTAswere uniformly distributedwithin the communication range of their associated APs. 802.11bwas used as the PHY layerwith transmission rate 11 Mbps, RTS/CTS disabled and the MAC buffer set to 1024,000 bits. Furthermore, the data rate ofFTP traffic data rate was set to 100 kbps and the bidirectional PCM voice traffic to 64 kbps.

834 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

6.3. Performance metrics

We compared the performance of our new approach with the existing algorithms using the following metrics.

6.3.1. Percentage of nodes affected by external hidden nodesIn Section 3 we showed that the hidden node problem can commonly occur. Here we follow our investigations by

discussing the effects this may have in densely deployed and unplanned infrastructure WLANs. Since delay-sensitiveapplications (e.g. VoIP) can be highly affected by the hidden nodes, the percentage of nodes affected by external hiddennodes (Ph) gives us a strong indication as to why some networks perform poorly. The higher the percentage of affectednodes, the more nodes are likely to suffer from bandwidth starvation.

6.3.2. Average degree of interferenceIn order to measure the density of WLANs, we introduce a metric called the average degree of interference (DOIavg ),

which is defined as follows.An interference graph Gi can be formed as Gi = (V , E). Let the APs be members in a set of vertices V . A set of edges E is

defined as the set of links from vertex Vj to vertex Vk (or APj and APk) if the received signal from APj to APk is greater than aninterference threshold.

From the above definition, the average degree of interference is defined as

DOIavg =

∑∀APk∈V

DOIk

|V |, (15)

whereDOIk is the total number of edges incident toVk in interference graphGi.DOIavg provides uswith the average number ofneighbors ofwireless networks that are using the same channel. HigherDOIavg translates toAPs being expected to experiencemore interference.

6.3.3. Normalized aggregate throughputThe normalized aggregate throughput (NT ) is the sum of throughput T (bssi) divided by the sum of offered bandwidth

B(bssi) of network bssi for all n networks in the topology:

NT =

n∑i=0

T (bssi)

n∑i=0

B(bssi). (16)

The normalized aggregate throughput (NT ) values of all tested scenarios are verified under IEEE 802.11 DCF basicoperation and with RTS/CTS enabled. It is a good indicator to show the performance of DCF basic operation against theperformance with RTS/CTS enabled.

6.3.4. Normalized number of VoIP connectionsVoice is a good representative of delay-sensitive traffic. Although the required bandwidth of voice traffic is very low

(e.g. 64 kbps PCM), the application itself demands the network to provide a strictly bounded delay.We define the normalizednumber of VoIP connections NV as

NV =nG

nV, (17)

where nG is the number of voice connections whoseMOS values are greater than the good-quality voice threshold of 3.6 andnV is the total number of offered voice connections. The higher the value of NV , the better the network can support voicetraffic.

Similarly, the performance of channel assignment algorithms is measured both with and without RTS/CTS (basicoperation) enabled.

7. Performance evaluation

From Fig. 8, it is clear that the probability of nodes affected by external hidden nodes (Ph) becomes greater when thenode density increases. Since WANHN uses the weight of hidden nodes as its objective function, it can greatly outperformWCA and CCA. CCA inherits the ability to minimize Ph from WANHN, so it improves on the WCA results by at least 45% inthe tested scenarios.

Fig. 9 show DOIavg of the tested scenarios. We can notice that DOIavg depends on the network density. The higher thenetwork density of the tested scenario, the higher DOIavg becomes with all three algorithms. Since WCA was designed toimplicitly minimize DOIavg , it achieves the best results across all tested scenarios. On the other hand, WANHN aims only to

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 835

Fig. 8. Probability of nodes being affected by at least one external hidden node in the tested scenarios 1–12.

Fig. 9. Average degree of interference (DOIavg ) in the tested scenarios 1–12.

Fig. 10. Normalized aggregate throughput (NT ) in the tested scenarios 1–12.

minimize Ph. Therefore it is likely to produce the highest DOIavg . CCA can trade off DOIavg and Ph, and produce a balancedcompromise.

The normalized aggregate throughput NT resulting from channel assignment algorithms is shown in Fig. 10. NT valuesin both IEEE 802.11 DCF basic operation (i.e. WCA, WANHN, and CCA) and DCF with RTS/CTS handshaking (i.e. WCA/RTS,WANHN/RTS, and CCA/RTS) are compared. Obviously, in all cases, DCF basic operation can provide far higher aggregatethroughput than DCF with RTS/CTS. Rahman and Gburzynski [40] pointed out that RTS/CTS can hinder non-interferingparallel transmissions, and Ray [41] suggested that RTS/CTS can induce a congestion in ad hoc networks by false blockingpropagation. Our study shows that this problem also exists in infrastructure WLANs.

It should be noted that these experiments were not carried out at network saturation, which explains the fact thatWCA, WANHN and CCA achieve very similar aggregate throughput, though CCA slightly outperforms WANHN and WCA.At saturation, the network cannot sustain more than one voice connection, and the performance of the three algorithms interms of voice support mixed with data traffic cannot be investigated.

In Fig. 11, the algorithms’ ability to support voice traffic is illustrated.With lownetwork density (e.g. scenarios 1–3),WCA,WANHN and CCA can support all voice connections. However, when the network density becomes higher but the averagenumber of STAs per network (NSTA) is sufficiently low, e.g. scenarios 4, 5 and 7, only CCA can provide support for all good-quality voice connections. WCA andWANHN can only partially provide good-quality voice support in such an environment.In a high network density and with high NSTA, e.g. scenarios 6 and 7, although CCA cannot provide all good-quality voice

836 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

Fig. 11. Normalized number of VoIP connections in the tested scenarios 1–12.

Fig. 12. Normalized loss rate in the tested scenarios 1–12.

connections, it still outperformsWCA. If the network density and NSTA are too high, e.g. scenarios 8, 9 and 11, it is impossibleto offer a large number of good-quality voice connections by any channel assignment algorithm. Other mechanisms may berequired to accommodate the quality of voice connections, e.g. voice application admission control.

It is noteworthy that the RTS/CTS mechanism, again, cannot enhance the network to support more good-quality voiceconnections in densely deployed wireless networks. Previously, we showed that the quality of voice is very sensitive to theaggregate bandwidth of hidden nodes. Together with these results, this implies that, in densely deployed and unplannedwireless networks where interference is not absolutely avoidable, it may be beneficial to assign the same channel toneighboring networks (producing higher DOIavg ) in order to avoid the hidden node problem and support more voiceconnections.

For completeness, we also provide results on an investigation on packet loss using the tested algorithmswith andwithoutRTS/CTS enabled (Fig. 12). The results show that CCA also yields the overall lowest packet loss, and therefore TCP traffic canbe expected to experience higher throughput than is possible with the other schemes. However, a rigorous performanceanalysis of this behavior is left for future work.

8. Distributed channel assignment algorithm

In the previous section, we presented the results from our centralized approach. However, there are many cases whena centralized algorithm cannot be applied. In any case where different access points belong to different administrativedomains, it is typically unlikely to relinquish control over such fundamental configuration such as channel usage to anexternal party. Simple examples of this would be a telco hot spot being configured by a competitor or home networksopened up to neighbors.

For this reason, a distributed algorithm is a preferable solution in many common cases. In this section, we describe ourproposed distributed algorithm, the distributed combined channel assignment (DCCA) algorithm, which is based on thesame graph representation as the centralized CCA algorithm.

Given graph G = (V , E) as defined in the previous section, let WH(vix, c) be the weight function of edges from external

hidden nodes on Gwhen channel c ∈ C (a set of available channels {1 . . . k}) is assigned to vix, defined as

WH(c) =−∀vix∈Vi

HLF(vix, c) · H(vi

x, c), (18)

where HLF(vix, c) is the hidden node level factor and H(vi

x, c) is the hidden node function.

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 837

Algorithm 3 DCCA1. bssi randomly chooses channel c ∈ C .2. Calculate FDCCA =

∑∀c∈C WDCCA(v

ix, c).

3. P(c)← WDCCA(c)FDCCA

.4. PAcc(c)←

∑ci=1 P(c).

5. Randomize a real number n, 0.0 < n < 1.0.6. Choose c ′ such that PAcc(c ′) is the closest value to n and PAcc(c ′) < n.7. Assign c ′ to vi

x.8. Repeat step 2.

HLF(vix, c) is a weighted function of the bandwidth of vi

x, defined as

HLF(vix, c) =

TC−t=1

Lxt · wh(t), (19)

where TC is the number of traffic categories, Lxt is the current load of access category t of node vix, and wh(t) is the weight of

access category t with regards to the hidden node problem.The hidden node function H(vi

x, c) is set (i.e. H(vix, c) = 1) when node vi

x is under the hidden node condition and it is reset(i.e. H(vi

x, c) = 0) when vix is not under the hidden node condition.

The hidden node condition can be identified by special mechanisms, such as passive and active hidden terminaldetection [42].

LetWD(vix, c) be the weight function of edges from the interference graph, as follows:

WD(c) =−∀vix∈Vi

ILFd(vix) · ILd(v

ix, c), (20)

where ILFd(vix) is the distributed interference loading factor and ILd(vi

x, c) is the distributed interference level.ILFd(vi

x) is the sum of the weighted function of the bandwidth of vix in bssi, defined as

ILFd(vix) =

TC−t=1

Lxt · wi(t), (21)

where Lxt is the current load of access category t of node vix and wi(t) is the weight of access category t with regards to

interference.Given vi

x ∈ A and vjy ∈ Nx, where Nx is a set of neighboring nodes of vi

x, ILd(vix, c) is the normalization function of received

signal strength (RSS) on channel c from node vjy (neighbors of vi

x) to node vix with respect to the minimum received signal

strength from all members viz ∈ Si, where Si is a set of STAs in bssi.

ILd(vix, c) =

−∀v

jy∈Nx

RSS(vi

x, vjy, c)

min[{RSS(vix, v

iz, c)}]

. (22)

The objective function of DCCA is then defined asWDCCA(c) = α ·WH(c)+ β ·WD(c), (23)

where α and β are weights for fine-tuning the performance of the algorithm and α + β = 1. The procedure of DCCA isshown in Algorithm 3.

8.1. Simulation study on DCCA

We evaluated the performance of the proposed algorithms using the OPNETModeler [35]. We compare the performanceof our distributed algorithm with the existing WCA and centralized approach using the simulation set-up detailed below.We also studied the performance of the proposed algorithm when it was deployed on 25%, 50%, and 75% of the wirelessnodes to provide further understanding of the algorithm sensitivity and rollout characteristics.

8.2. Simulation scenario

The effects of different levels of penetration of the distributed channel assignment algorithm are compared in this studyas follows.1. DCCA: our proposal of distributed channel assignment algorithm is applied to all wireless nodes.2. DCCA-X: DCCA is applied to X% (where X is 75, 50, and 25) of the wireless nodes and the remaining nodes are assigned

a channel randomly. We study the behavior for DCCA-75, DCCA-50 and DCCA-25.

838 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

Table 3Scenario settings for DCCA experiments.

Scenario Network density NSTA

1 1.25× 10−4 2.02 2.50× 10−4 2.03 3.75× 10−4 2.04 5.00× 10−4 2.05 1.25× 10−4 5.06 2.50× 10−4 5.07 3.75× 10−4 5.08 5.00× 10−4 5.0

Fig. 13. Normalized throughput in the tested scenarios.

1.0

0.8

0.6

0.4

0.2

0.0

Nor

mal

ized

Num

ber

of V

oIP

Con

nect

ions

Fig. 14. Normalized number of VoIP connections in the tested scenarios.

DCCA is tested in eight scenarios spanning low-density to high-density networks according to Table 3.

8.3. Discussion of DCCA

Fig. 13 shows the normalized aggregate throughputNT resulting from the studied channel assignment algorithms. Underlow load (scenarios 1–3), DCCA, DCCA-25, DCCA-50, and DCCA-75 provide similar results. Under high load (scenarios 5–7),it is possible for DCCA to improve on WCA somewhat (by approximately 5% in our tests). It should be noted that WCA iscentralized in these tests, and therefore it has an advantage over DCCA in finding the optimal solution.

It is also important to note that although DCCA cannot outperformWCA under low-load scenarios, it can provide a bettervoice support under such conditions as illustrated in Fig. 14.

9. Conclusions and future work

In this paper, we have investigated the effects on network performance from hidden nodes in densely deployed WLANnetworks. We have carried out extensive simulation studies and experiments and verified that the RTS/CTS mechanismis unable to solve the performance issues this brings. We have further shown that the hidden node problem should beconsidered by channel assignment algorithms for WLANs since delay-sensitive applications may suffer otherwise. Finally,we have proposed two algorithms for channel allocation, building on a combination of channel occupancy and hidden node

S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840 839

traffic, and have shown that our solution can yield amuch better support for voice applications in densely deployedwirelessnetworks without sacrificing the aggregate throughput.

In the futurewe intend to address the following issues. Since the algorithmshave to determine theweight of the aggregatebandwidth of the hidden nodes, we need a suitable mechanism in order to detect the hidden node conditions in wirelessnodes, e.g. passive and active hidden terminal detection as proposed by Frank et al. [42]. It is not clear how best to includesuch support in an overall system.

In addition, the results in this paper were obtained in a static and steady-state wireless topology. Wireless nodemobilityhas not been considered. However, the algorithms are adaptive to topology changes due to their repetitive behavior at theexpense of convergence time. Additionally, some other performance metrics may be required to accommodate mobilitysupport, e.g. hand-over delay, channel switching delay, etc. Our next step is to incorporate mobility support in this workand to study the effects on delay-sensitive and bulk data transfer applications taking this dynamic environment into account.

Acknowledgement

SuparerkManitpornsutwould like to thankDepartment of Computer Engineering andMultimedia, School of Engineering,the University of the Thai Chamber of Commerce, Thailand, for its support. This work was sponsored by the AustralianResearch Council under ARC grant DP0987782. Special thanks also go to National ICT Australia, Sydney, Australia, forproviding research facilities.

References

[1] A. Akella, G. Judd, S. Seshan, P. Steenkiste, Self-management in chaotic wireless deployments, in: Proceedings of the 11th Annual InternationalConference on Mobile Computing and Networking, 2005, pp. 185–199.

[2] J. Geier, Interference from cordless phones. Available at: http://www.wi-fiplanet.com/tutorials/article.php/2191241.[3] N. Golmie, N. Chevrollier, O. Rebala, Bluetooth andWLAN coexistence: challenges and solutions, IEEEWireless Communications 10 (6) (2003) 22–29.

1536-1284.[4] A.P. Jin, P. Seung-Keun, K. Dong-Ho, C. Pyung-Dong, C. Kyoung-Rok, Experiments on radio interference between wireless LAN and other radio devices

on a 2.4 GHz ISM band, in: The 57th IEEE Semiannual Vehicular Technology Conference, vol. 3, 2003, pp. 1798–1801.[5] J. Jung-Hyuck, H. Jayant, Performance evaluation of multiple IEEE 802.11b WLAN stations in the presence of Bluetooth radio interference, in:

Communications, 2003. ICC’03. IEEE International Conference on, vol. 2, 2003, pp. 1163–1168.[6] Y. Matsumoto, M. Takeuchi, K. Fujii, A. Sugiura, Y. Yamanaka, Performance analysis of interference problems involving DS-SS WLAN systems and

microwave ovens, IEEE Transactions on Electromagnetic Compatibility 47 (1) (2005) 45–53. 0018-9375.[7] L. Navarro-Garcia, J.I. Alonso, A novel method for interference analysis in IEEE 802.11WLAN’s in coexistence with Bluetooth, in: Wireless Technology,

2005. The European Conference on, 2005, pp. 181–184.[8] K. Premkumar, S.H. Srinivasan, Diversity techniques for interference mitigation between IEEE 802.11 WLANs and Bluetooth, in: Personal, Indoor and

Mobile Radio Communications, 2005. PIMRC 2005. IEEE 16th International Symposium on, vol. 3, 2005, pp. 1468–1472.[9] IEEE standard for information technology – telecommunications and information exchange between systems – local andmetropolitan area networks

– specific requirement. Part 11: wireless LAN medium access control (MAC) and physical layer (PHY) specifications. Amendment 2: higher-speedphysical layer (PHY) extension in the 2.4 GHz band – corrigendum 1, in: IEEE Std 802.11b-1999/Cor 1-2001, 2001.

[10] B.-J.K. Kin, K. Leung, Frequency assignment for IEEE 802.11 wireless networks, in: Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE58th, vol. 3, 2003, pp. 142–1426.

[11] L. Youngseok, K. Kyoungae, C. Yanghee, Optimization of AP placement and channel assignment in wireless LANs, in: Local Computer Networks, 2002.Proceedings. LCN 2002. 27th Annual IEEE Conference on, 2002, pp. 831–836.

[12] M. Iridon, D. Matula, C. Yang, A graph theoretic approach for channel assignment in cellular networks, Wireless Networks 7 (6) (2001).[13] J. Riihijarvi, M. Petrova, P. Mahonen, J. de Almeida Barbosa, Performance evaluation of automatic channel assignment mechanism for IEEE 802.11

based on graph colouring, in: Personal, Indoor and Mobile Radio Communications, 2006 IEEE 17th International Symposium on, 2006, pp. 1–5.[14] A.Mishra, S. Banerjee,W. Arbaugh,Weighted coloring based channel assignment inWLANs, ACM SIGMOBILEMobile Computing and Communications

Review 9 (2005) 19–31. 1559–1662.[15] P.P. Pham, S. Perreau, A. Jayasuriya, Performance analysis of the IEEE 802.11 DCF, in: Communications, 2005 Asia-Pacific Conference on, 2005,

pp. 764–768.[16] G. Bianchi, Performance analysis of the IEEE 802.11 distributed coordination function, Selected Areas in Communications, IEEE Journal on 18 (3) (2000)

535–547.[17] P. Latkoski, Z. Hadzi-Velkov, B. Popovski, Extendedmodel for performance analysis of non-saturated IEEE 802.11 DCF in erroneous channel, in: Mobile

Adhoc and Sensor Systems, MASS, 2006 IEEE International Conference on, 2006, pp. 783–788.[18] C. Jaehyuk, Y. Joon, K. Chong-kwon, A novel performance analysis model for an IEEE 802.11 wireless LAN, Communications Letters, IEEE 10 (5) (2006)

335–337. 1089–7798.[19] X. Wang, Performance modeling of IEEE 802.11 DCF using equilibrium point analysis, in: Advanced Information Networking and Applications, 2006.

AINA 2006. 20th International Conference on, vol. 1, 2006, p. 6 pp.[20] A. Samhat, Z. Altman, B. Fourestie, Performance analysis of the IEEE 802.11 DCF with imperfect radio conditions, in: Wireless and Mobile

Communications, 2006. ICWMC’06. International Conference on, 2006, pp. 27–27.[21] G.A. Safdar, W.G. Scanlon, Performance analysis of an improved power-saving medium access protocol for IEEE 802.11 point coordination function

WLAN, Communications, IEE Proceedings 153 (5) (2006) 697–704. 1350–2425.[22] Q. Wan, H. Du Ming, Improving the performance of WLAN to support VoIP application, in: Mobile Technology, Applications and Systems, 2005 2nd

International Conference on, 2005, p. 5 pp.[23] K. Taekon, K. ChangYeul, Y. Chil-Youl, Performance analysis of polling schemes with IEEE 802.11a WLAN, in: Vehicular Technology Conference, 2004.

VTC 2004-Spring. 2004 IEEE 59th, vol. 3, 2004, pp. 1564–1568.[24] S. Manitpornsut, B. Landfeldt, On the performance of IEEE 802.11 QoS mechanisms under spectrum competition, in: ACM IWCMC’06: Proceedings of

the 2006 International Conference on Wireless Communications and Mobile Computing, 2006, pp. 719–724.[25] J. Riihijarvi, M. Petrova, P. Mahonen, Frequency allocation for WLANs using graph colouring techniques, in: Proceedings of the Second Annual

Conference on Wireless On-demand Network Systems and Services, 2005, pp. 216–222.[26] S. Manitpornsut, B. Landfeldt, Weighted channel allocation and power control for self-configurable infrastructure WLANs, Journal of Interconnection

Networks 9 (3) (2008) 299–316.[27] S. Ray, D. Starobinski, J.B. Carruthers, Performance of wireless networks with hidden nodes: a queuing-theoretic analysis, Journal of Computer

Communications 28 (2005) 1179–1192.

840 S. Manitpornsut et al. / Performance Evaluation 68 (2011) 825–840

[28] C.K. Ho, Jean-Paul M.G. Linnartz, Analysis of the RTS/CTS multiple access scheme with capture effect, in: Personal, Indoor and Mobile RadioCommunications, 2006 IEEE 17th International Symposium on, 2006, pp. 1–5.

[29] S. Papanastasiou, M. Ould-Khaoua, V. Charissis, The effect of the RTS/CTS handshake on TCP, in: Advanced Information Networking and ApplicationsWorkshops, 2007, AINAW’07. 21st International Conference on, vol. 2, 2007, pp. 940–946.

[30] H. Jasani, N. Alaraje, Evaluating the Performance of IEEE 802.11 Network using RTS/CTS Mechanism, in: Electro/Information Technology, 2007 IEEEInternational Conference on, 2007, pp. 616–621.

[31] A. Tsertou, D.I. Laurenson, Revisiting the hidden terminal problem in a CSMA/CA wireless network, Mobile Computing, IEEE Transactions on 7 (7)(2008) 817–831. 1536-1233.

[32] J.L. Sobrinho, R. de Haan, J.M. Brazio, Why RTS-CTS is not your ideal wireless LAN multiple access protocol, in: Wireless Communications andNetworking Conference, 2005 IEEE, vol. 1, 2005, pp. 81–87.

[33] W. Chien-Min, H. Ting-Chao, The impact of RTS/CTS on performance of wireless multihop Ad Hoc networks using IEEE 802.11 protocol, in: Systems,Man and Cybernetics, 2005 IEEE International Conference on, vol. 4, 2005, pp. 3558–3562.

[34] P.C. Ng, S.C. Liew, K.C. Sha, W.T. To, Experimental study of hidden-node problem in IEEE 802.11 wireless networks, in: ACM SIGCOMM, 2005.[35] OPNET Modeler. [Online]. Available: http://www.opnet.com/support/.[36] Sandvine, Study: 44% of internet traffic is peer-to-peer, 2008. [Online]. Available: http://www.sandvine.com/.[37] ITU-T recommendation G.107: the E-model, a computational model for use in transmission planning, 2005.[38] MadWifi. [Online]. Available: http://www.madwifi.org/.[39] Iperf. [Online]. Available: http://dast.nlanr.net/Projects/Iperf/.[40] A. Rahman, P. Gburzynski, Hidden problems with the hidden node problem, in: Communications 23rd Biennial Symposium on, 2006, pp. 270–273.[41] S. Ray, J.B. Carruthers, D. Starobinski, RTS/CTS-induced congestion in ad hoc wireless LANs, in: WCMC, 2003.[42] A.K. Frank, Y. Li, P. Engelstad, Passive and active hidden terminal detection in 802.11-based Ad-Hoc networks, in: INFOCOM, 2006.

Suparerk Manitpornsut received his Bachelor degree in Electrical Engineering from King Mongkut’s Institute of TechnologyLadkrabang (KMITL), Thailand and finished his Master degree in Electrical Engineering from University of Southern California(USC), USA. He earned his Ph.D. from School of Information Technologies, University of Sydney, Australia. Currently,Dr. Manitpornsut is a lecturer in the Department of Computer Engineering and Multimedia, University of the Thai Chamberof Commerce, Thailand. His research interests include wireless networks, vehicular networks, traffic modeling and simulation,autonomous mobile multi-agents, and machine learning.

Björn Landfeldt started his studies at the Royal Institute of Technology in Sweden. After receiving a B.Sc. equiv, he continuedstudying at The University of New South Wales where he received his Ph.D. in year 2000.

In parallel with his studies in Sweden he was running a mobile computing consultancy company and after his studies hejoined Ericsson Research in Stockholm as a Senior Researcher where he worked onmobility management and QoS issues. In 2001,Dr. Landfeldt took up a position as a CISCO Senior lecturer in Internet Technologies at the University of Sydney with the Schoolof Electrical and Information Engineering and the School of Information Technologies. Currently, he is an Associate Professor inSchool of Information Technologies at University of Sydney.

Prof. Landfeldt has been awarded over 10 patents in the US and globally. He has published more than 90 publications ininternational books, journals and conferences and been awardedmany competitive grants. He is serving on the editorial boards ofinternational journals and as a program committeemember ofmany international conferences and is currently supervising 5 Ph.D.students. He is further active in conference organisation and is chairing many program committees of conferences and symposia

such as ACMMSWIM, MOBIWAC and IEEE ICCCN. He is also operations chair of IEEE ICC 2014.Dr. Landfeldt’s research interests include; wireless systems, systems modeling, mobility management, QoS and service provisioning.

Azzedine Boukerche is a Full Professor and holds a Canada Research Chair position at the University of Ottawa (uOttawa). Heis a Fellow of the Canadian Academy of Engineering and the founding director of the PARADISE Research Laboratory, Schoolof Information Technology and Engineering (SITE), Ottawa. Prior to this, he held a faculty position at the University of NorthTexas, and he was a senior scientist at the Simulation Sciences Division, Metron Corp., San Diego. He was also employed asa faculty member in the School of Computer Science, McGill University, and taught at the Polytechnic of Montreal. He spenta year at the JPL/NASA-California Institute of Technology, where he contributed to a project centered about the specificationand verification of the software used to control interplanetary spacecraft operated by JPL/NASA Laboratory. His current researchinterests include wireless ad hoc and sensor networks, wireless networks, mobile and pervasive computing, wireless multimedia,QoS service provisioning, performance evaluation and modeling of large-scale distributed systems, distributed computing, large-scale distributed interactive simulation, and parallel discrete-event simulation. He has published several research papers in theseareas. He served as a guest editor for the Journal of Parallel and Distributed Computing (special issue for routing for mobile ad

hoc, special issue for wireless communication and mobile computing, and special issue for mobile ad hoc networking and computing), ACM/ KluwerWireless Networks, ACM/Kluwer Mobile Networks Applications, and Journal of Wireless Communication and Mobile Computing. He serves/served as anAssociate Editor of IEEE Transactions on Parallel and Distributed systems, IEEE Transactions on Vehicular Technology, Elsevier Ad Hoc Networks, WileyInternational Journal of Wireless Communication and Mobile Computing, Wiley’s Security and Communication Network Journal, Elsevier Pervasive andMobile Computing Journal, IEEE Wireless Communication Magazine, Elsevier’s Journal of Parallel and Distributed Computing, and SCS Transactions onSimulation. He was the recipient of the Best Research Paper Award at IEEE/ ACM PADS 1997, ACM MobiWac 2006, ICC 2008, ICC 2009 and IWCMC 2009,and the recipient of the Third National Award for Telecommunication Software in 1999 for his work on a distributed security systems on mobile phoneoperations. He has been nominated for the Best Paper Award at the IEEE/ACM PADS 1999 and ACM MSWiM 2001. He is a recipient of an Ontario EarlyResearch Excellence Award (previously known as Premier of Ontario Research Excellence Award), Ontario Distinguished Researcher Award, and GlinskiResearch Excellence Award. He is a cofounder of the QShine International Conference on Quality of Service for Wireless/Wired Heterogeneous Networks(QShine 2004). He served as the general chair for the Eighth ACM/IEEE Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems,and the Ninth ACM/IEEE Symposium on Distributed Simulation and Real-Time Application (DS-RT), the program chair for the ACM Workshop on QoS andSecurity for Wireless and Mobile Networks, ACM/IFIPS Europar 2002 Conference, IEEE/SCS Annual Simulation Symposium (ANNS 2002), ACMWWW2002,IEEE MWCN 2002, IEEE/ACMMASCOTS 2002, IEEEWireless Local NetworksWLN03-04; IEEEWMAN 04-05, and ACMMSWiM 98-99, and a TPCmember ofnumerous IEEE and ACM sponsored conferences. He served as the vice general chair for the Third IEEE Distributed Computing for Sensor Networks (DCOSS)Conference in 2007, as the program cochair for GLOBECOM 2007–2008 Symposium onWireless Ad Hoc and Sensor Networks, iCC 2011 Ad Hoc Symposiumon Ad Hoc, Sensor and Mesh Networking Symposium, for the 14th IEEE ISCC 2009 Symposium on Computer and Commmunication Symposium, and asthe finance chair for ACM Multimedia 2008. He also serves as Tutorial Co-Chair for the ICC 2012, Panel Chair for MobiCom and a Vice Chair for the IEEETechnical Committee for Ad hoc and Sensor Networks, and a Steering Committee chair for the ACM Modeling, Analysis and Simulation for Wireless andMobile Systems Conference, the ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, and IEEE/ACM DS-RT.