RESEARCH Open Access

GSM: gateway selection mechanism forstrengthening inter-cluster coordination incognitive radio ad hoc networksNajam ul Hasan, Waleed Ejaz, Kamran Manzoor and Hyung Seok Kim*

Abstract

In the open access spectrum paradigm, the dynamics of the available spectrum heterogeneity due to thegeographical location of various cognitive radio/secondary users (SUs) and primary users (PUs) prevent theassignment of a single common control channel to every SU. Thus, depending on channel availability, SUs areforced to form clusters to solve the problem of control channel assignment. However, the sudden appearance of aPU may lead to loss of intra-cluster or inter-cluster connectivity among SUs. This problem can be resolved byimproving the cluster formation process and selecting suitable gateway nodes for inter-cluster coordination. In thispaper, our goal is to improve inter-cluster connectivity by selecting more appropriate gateway nodes forinter-cluster coordination. We therefore propose an inter-cluster gateway node selection mechanism thatstrengthens inter-cluster coordination. Our scheme shows significant improvement in terms of inter-clusterconnectivity compared with related work.

Keywords: Cognitive radio; Control channel; Gateway selection

1 IntroductionCognitive radio (CR) networks have gained much attentionin the research community in the last decade and haveemerged as a promising approach to solving the issues ofspectrum scarcity [1]. CR is the key technology underlyingthe dynamic spectrum access (DSA) paradigm. In DSA, CRnetworks are overlaid over the licensed networks, alsoknown as primary networks, for opportunistic spectrumaccess [2]. Consequently, in a network that utilizes DSA,users are classified as primary if they are licensed to operatein a particular frequency band; and secondary if otherwise.Secondary users (SUs) can operate in the vacant portion ofthe licensed band only if they do not interfere with theprimary users (PUs) [3]. When a cognitive radio ad hocnetwork (CRAHN) is set up, SUs need to cooperate byexchanging control information to achieve reliability inseeking vacant frequency bands. As a result, the establish-ment of a control channel is essential for exchangingcontrol information [1-3]. A comprehensive survey ofcontrol channels in CR networks is given in [4].

When setting up a CRAHN, a control channel is essentialin the carrying out of functions such as spectrum sensing,sharing, decision, and mobility as all these require extensivecontrol information exchange [1]. Several techniques havebeen proposed in the literature pertaining to the design ofcontrol channels for CRAHNs. Most of the proposed tech-niques can be divided into two categories: (1) dedicatedcommon control channels and (2) group/cluster-based con-trol channels. Using a dedicated common control channelsimplifies the process of coordination among SUs. However,reliance on a dedicated common control channel results inseveral limitations. Firstly, there is a single point of failure;for example, a dedicated common control channel can re-sult in loss of connectivity among SUs in cases of saturationor jamming. Secondly, in the case of CRAHNs, the dynam-ically changing availability of the spectrum in the temporaland spatial domain makes it difficult to maintain a dedi-cated common control channel that is globally available.Two major challenges arise in the assignment of a dedi-

cated control channel for CRAHNs, namely, PU activityand spectrum heterogeneity. With regard to PU activity,in a CRAHN, a PU has higher priority than an SU. If anSU is operating on a particular frequency band and a PU

* Correspondence: hyungkim@sejong.eduDepartment of Information and Communication Engineering, SejongUniversity, Gunja-Dong 98, Gwangjin-gu, Seoul 143-747, Republic of Korea

© 2013 ul Hasan et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproductionin any medium, provided the original work is properly cited.

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comes back to that frequency band, the SU has to vacatethat channel in favor of the PU and migrate to some otherunused frequency band. Thus, in environments where PUactivity is not known, it is impossible to guarantee that theparticular frequency band of the designated control channelwill always remain available in the presence of PU activity.With regard to spectrum heterogeneity, every SU observesa different set of available channels depending on its geo-graphical location, each of which is a subset of the set of allthe licensed channels. Due to this spectrum heterogeneity,very rarely can a channel commonly available to all SUs ata particular instance be found. To account for PU dynamicsand spectrum heterogeneity issues, several researchers haveproposed cluster-based approaches for control channel de-sign of CRAHNs [5-8]. In cluster-based control channelschemes for CRAHNs, a neighbor discovery (rendezvous)process is performed to set up an initial network topology.Based on the information exchanged during the rendezvousprocess, the nodes are partitioned into clusters dependingon highly correlating spectrum opportunities. A compre-hensive survey on rendezvous process for cognitive radio ispresented [9]. After clustering, one or more channels thatare common to nodes within each cluster are designated asthe control channels.In [9], Zhao et al. proposed a distributed cluster-based

approach for control channel design in CRAHNs. Thisscheme partitions the nodes into clusters, with the largestpossible number of nodes in each cluster. This method re-duces the number of distinct frequencies used for controlchannels thereby incurring less cluster management over-head. However, the scheme requires frequent re-clusteringin response to PU activity. Another similar approach forclustering CR networks is proposed in [10].To avoid frequent re-clustering in response to PU ac-

tivity and spectrum heterogeneity, the concept of backupcontrol channels has been introduced. Bahl et al. [11]proposed a control channel scheme for centralized CRnetworks in which one main channel and one backupchannel are designated as control channels. The mainand backup channels are then interchanged accordingto the spectrum dynamics incurred due to PU activity.In [12], Liu et al. extended this concept of backup con-trol channel in CRAHNs by proposing a cluster-basedcontrol channel allocation scheme called spectrumopportunity-based clustering (SOC). Instead of makingthe cluster with the largest number of nodes as pro-posed in [13] or with just one backup channel as pro-posed in [11], they proposed that each cluster beformed in such a way that the product of the numberof common channels among cluster members and thenumber of cluster members in a cluster are maximized.In other words, clusters are formed in such a way thata balance is created between the cluster size and thenumber of common channels.

By maximizing the number of common channels amongthe nodes within a cluster, intra-cluster connectivity isstrengthened and frequent re-clustering averted. However,the overall connectivity of a cluster-based CRAHN includesintra-cluster as well as inter-cluster connectivity. Thus,in this study, our focus is on inter-cluster connectivity,and we carry forward and enhance the concept intro-duced by Liu et al. [12] to improve inter-cluster con-nectivity. The inter-cluster connectivity is strengthenedby designating more channels as control informationbetween adjacent clusters.Generally, a clustered ad hoc network consists of three

kinds of nodes: cluster head nodes, gateway nodes, andordinary nodes. Cluster head nodes are vested with theresponsibility of managing the cluster. For example, inthe case of a cluster-based control channel design fora CRAHN, the cluster head node is responsible forallocating a set of control channels out of the set ofchannels available to every member of that cluster. Thecoordination between two adjacent clusters is conductedthrough the gateway nodes. A node of a cluster can actas a gateway only if it has a neighboring node as well asa channel in common with the designated controlchannel of its neighboring cluster. All nodes other thanthe cluster head node and gateway node are ordinarynodes. Both gateway nodes and ordinary nodes are alsoknown as cluster members and are managed by thecluster head node.As mentioned earlier, in a cluster-based CRAHN, the

gateway node is responsible for coordination betweenadjacent clusters. It addition, it is well known thatinter-cluster connectivity becomes stronger the morechannels there are between adjacent clusters in thecase of CRAHNs. There can be nodes in a clusterthat have one or more channels in common with theset of channels common among nodes in the adjacentcluster. Any of these nodes can be a gateway nodecandidate. However, the node with the maximumnumber of channels in common with the control chan-nels in the adjacent cluster is the most suitable optionbecause selecting such a node as a gateway strengthensthe coordination with the adjacent clusters. Therefore,in this paper, we propose a mechanism for selectinggateway nodes and analyze its impact on the connect-ivity of CRAHNs.The remainder of this paper is organized as follows:

section 2 presents the system model used for our simulatedenvironment. Section 3 discusses our proposed frameworkand describes the algorithm proposed for the selectionof the gateway node for inter-cluster coordination inCRAHNs. Section 4 shows the impact of variations inSU and PU node densities on the proposed algorithmby presenting simulation results. Finally, section 5 con-cludes the paper.

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2 Problem statement and system model2.1 Problem statementConsider a geographical region in which a specific bandis shared between PUs and SUs operating in that area. Itis assumed that the spectrum is divided into equallyspaced portions known as channels. The spectrumsharing between PUs and SUs is vertical. Thus, an SUhas to ensure that no PU is transmitting on a particularchannel before accessing it. To achieve this, each SUsenses the spectrum after a specific interval and comesup with a set of channels Ci available for its communi-cation. Initially, each SU discovers its neighbors andexchanges the list of channels with them. Two nodesare neighbors if and only if both of them are withincommunication range of each other and they have atleast one channel in common with each other. This istypically achieved during neighbor discovery phase.During this phase, all SUs obtain a list of idle channelsfrom the local spectrum sensing observation, follow ahopping sequence, and hop-over-idle channels. Twoneighboring nodes discover each other and establish alink by exchanging beacons when they hop to the samechannel. This is how the initial network is formed inthe absence of any predefined control channel. Basedon the received information, a distributed clusteringmechanism proposed by Liu et al. in [12] is employedto partition the SUs into clusters, and a cluster head ischosen for each cluster. Each cluster has a set of channelscommon to all the cluster members, known as the intra-cluster common control channel. To perform inter-clustercontrol information exchange, each cluster head chooses agateway for each neighboring cluster. The common chan-nels between the gateway and the neighboring cluster areknown as inter-cluster common control channels. The goalof this study is to propose a mechanism for gateway se-lection in such a way that the number of inter-clustercommon control channels is maximized.For the sake of clarity, the problem mentioned above

is illustrated in Figure 1 in the form of a graph, whereconnectivity of each neighboring pair is shown with thehelp of a solid line. After the neighbor-discovery process,using a distributed clustering mechanism, the SUs arepartitioned into two clusters, as shown in Figure 1a. NodesA and E are nominated as cluster heads of the cluster, asdepicted in Figure 1b. The clusters are named ΦA and ΦE,with the subscripts denoting the cluster heads. ZA and ZE

are the lists of control channels for intra-cluster coordin-ation in clusters ΦA and ΦE, respectively. CD and CG indi-cate the lists of channels sensed idle by nodes D and G. Inthis example, CD is {1, 2, 3, 5, 7} and CG is {1, 2, 3, 4, 8}.Only nodes D and G of cluster ΦA have neighbors inadjacent cluster ΦE. Therefore, both nodes D and Gare candidates for selection as a gateway node. Thisproblem is referred to as the gateway node selection

problem for inter-cluster coordination in CRAHNs. It isessential that there be a mechanism for selecting the nodewith the maximum number of channels in common withthe set of selected channels for the adjacent cluster.

2.2 System modelAs a system model, we consider a cognitive radio networkthat coexists with one or more primary networks in thesame geographical region. The PUs can operate only in thelicensed spectrum, which is divided into T orthogonal fre-quency channels. For the sake of simplicity, it is assumedthat each channel is characterized by the same attributessuch as bandwidth, bit error rate, and so on. There are nSUs randomly distributed in the region. The transmissionrange of each radio, whether it is SU or PU, is assumed tobe about γ meters. Each SU can listen to other SUs onlywithin this transmission range.Each SU employs a spectrum sensing technique, such

as cyclostationary feature detection or energy detection,to search for the idle channels available for their usage.A comprehensive survey on spectrum sensing techniquesincluding energy detection and cyclostationary is presentedin [14]. After sensing, each ith SU has a list of availablechannels denoted Ci. Two SUs are called neighbors ifthey are within the transmission range of each otherand they have at least one channel in common. Each SUdiscovers its neighboring SUs. The list of the neighborsof the SU is denoted Ni.The notations used in this paper are listed in Table 1.After neighbor discovery, each SU participates in a

process of cluster formation and inter-cluster connec-tion based on the common channels among the nodes.The cluster formation and inter-cluster connection areperformed in a distributed manner, based on neighborinformation. A cluster formation problem in a CR net-work is formally related with four items: cluster head,cluster members, common channels between the clustermembers, and the cluster gateway used for inter-clustercoordination.This paper addresses the problem of cluster gateway

selection for inter-cluster coordination. Any member ofa cluster that has a single neighbor node and channel incommon with the set of common channels of any otheradjacent cluster can be a candidate for selection as agateway node. In this paper, our aim is to devise amechanism for selecting a more suitable gateway nodesuch that inter-cluster connectivity is stronger againstPU interference. PU activity prediction is quite difficultin real time, so the goal of selecting the gateway nodeis to establish as many common channels betweenneighboring clusters as possible. If the chosen gatewayhas a large number of channels in common with thedesignated control channels for the neighboring cluster,in case a channel used for inter-cluster communication

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gets occupied by a PU, the inter-cluster link can stilluse some other backup channels on the same gateway.

3 Proposed frameworkTo account for the space and time availability of thespectrum for control information exchange, the CRAHN ispartitioned into clusters. To ensure availability of connect-ivity in the CRAHN, adjacent clusters must be connectedto each other. To strengthen the connectivity among theclusters, we propose a gateway node selection mechanismfor CRAHNs. The entire process of establishing controlchannels for a CRAHN can be broken down into threephases: (1) neighbor discovery, (2) cluster formation, and(3) gateway node selection. In the neighbor discovery phase,each SU discovers its neighbors by listening to differentchannels; however, the details of the neighbor discoveryprocess are beyond the scope of this paper.

3.1 Cluster formationTo ensure that a large set of common idle channels is ineach cluster and provide a graceful trade-off betweenthe cluster size and cluster-based common idle channels,Liu et al. [12] formulated the clustering problem using abipartite graph. In this study, we incorporate the samemethodology for cluster formation. Despite of employingthe same clustering as [12], proposed gateway selectionmechanism (GSM) can work independent of underlyingclustering scheme. However, the performance of GSM isstill dependent on the underlying clustering technique.SUs are partitioned into clusters based on the set of idlechannels that are common to all cluster members. Themain goal of this cluster formation methodology is tocluster SUs in such a way that there is a large set ofcommon idle channels between the cluster members.The benefit associated with this sort of clustering is that

Figure 1 Clustering of connected graphs of seven SU nodes, cluster head nomination, and gateway node election. (a) An example ofclustering of connected graphs of seven SU nodes in a CRAHN; (b) Cluster head nomination for each cluster; and (c) Gateway node election foreach cluster.

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a cluster has more options for choosing a control channel.As a result, the control channel can migrate to anotherchannel without performing clustering again, even if thecurrent one becomes occupied by a PU.Following neighbor discovery and the initial exchange

of the list of idle channels C, each SUi becomes aware ofits list of one-hop neighbors, denoted Ni, and their channellist Cj, where SUj ∊ Ni. The cluster formation procedureconsists of the following steps:

3.1.1 Procedure: cluster formation mechanism

Step 1: Every ith SU broadcasts its set of idle channels Ci.Step 2: Every ith SU constructs a bipartite graph based

on Ni, Ci, and Cj the list of channels of neighbors SUj.Step 3: Every ith SU extracts the maximum edge

biclique graph Q(Xi,Yi), as will be explained later inthis section. The product of the number of nodesand channels in the chosen biclique graphrepresents the weight wi of the ith SU.

Step 4: Every ith SU broadcasts wi, Xi, and Yi to itsneighboring SUs.

Step 5: If wi > wj, ∀j ∊ Ni, the ith SU is declared acluster head; otherwise, it joins the cluster head withthe greatest weight wj among its neighbors Ni.

3.2 Construction of bipartite graph and extraction ofmaximum edge biclique graphAfter exchanging information between neighboring SUs,this information can be represented in the form of abipartite graph. A graph G(V,E) is called bipartite if theset of vertices V can be partitioned into two disjoint setsS1 and S2 with S1 ∪ S2 = V such that every edge E con-nects a vertex in S1 to a vertex in S2. Gi(S1,S2,E) denotesthe bipartite graph for the ith node, where the set S1consists of SUi and its neighbors Ni and the set S2 con-sists of the list of idle channels Ci. An edge (x,y) existsbetween vertices such that x∊S1 and y ∊ S2 if y ∊ Cx, i.e.,channel y is in list Cx. Figure 2a depicts the bipartitegraph constructed for node A of the graph shown inFigure 1a. A bipartite graph Q(V = X ∪ Y, E) is termedbiclique if for each x ∊ X and y ∊ Y, there exists an edgebetween x and y. In this study, we are interested inextracting the biclique graph that has the maximumproduct of cluster size and the set of channels commonamong cluster members. Figure 2b depicts the max-imum edge biclique graph for node A.The basic aim of our clustering scheme is to ensure

that a large set of idle channels are available to the clus-ter members. To attain this goal, we need to cluster insuch a way that the product of the number of nodes andnumber of channels common to each member of thecluster is maximized. The sub-graph meeting this criter-ion is called the maximum edge biclique graph. Thealgorithm for extracting this maximum edge bicliquegraph is presented below.Algorithm 1 computes the maximum edge biclique graph

for the ith SU. In each iteration, one SU is examined. Thevector I holds the indices of the SU that have already beenexamined; while Yi stores the channels that are common toSUs in I. Initially, I is empty and Yi contains the list ofchannels for the ith SU on which the algorithm is running.The SU node k to be examined in each iteration is the onewhose channel list Ck has the maximum overlap with Yi,i.e., the number of elements in R = Yi ∩ Ck. Then, the exam-ined SUk is removed from S1 and the above process isrepeated until either S1 becomes empty, meaning that Mgoes to zero or R becomes null. Finally, the biclique graphQ with the highest product of the number of SU nodes andthe number of common control channels is eventuallyobtained. The outcome is a set of nodes Xi with a set ofcommon channels Yi. Yi is obtained by taking the intersec-tion of channels available to every element of Xi. The nodeshaving the same biclique graph form a cluster with the

Table 1 Nomenclature

Notation Description

Φi Cluster with the ith node as the cluster head.

T Total number of channels in the network.

n Number of SUs.

Ci List of idle channels sensed by the ith SU.

Ni List of neighbors of the ith SU.

γ Transmission range of SU or PU.

Gi(S1, S2, E) Undirected bipartite graph at the ith SU.

S1 Set of vertices including the ith SU and its neighborsNi.

S2 Set of vertices including a list of channels Ci availablewith the ith SU.

Xi Nodes in the chosen maximum edge biclique graphof the ith SU.

Yi Channels in the chosen maximum edge bicliquegraph of the ith SU.

Q(Xi, Yi) Sub-graph of graph G, known as a multiple-edgebiclique graph.

Zm Common control channels assigned to the mth cluster

F Number of elements in Xi.

K Number of elements in Yi.

wi Weight assigned to the ith SU based on theproduct of Xi andYi.

dim Degree of connectivity of an ith node to its neighbornode belonging to cluster m, i.e., the intersection ofCi and a set of idle channels chosen for cluster m.

σ PU arrival rate; refers to how often a PU occupies a channel.

α Average number of intra-cluster control channelsper cluster in the entire CRAHN.

β Average number of inter-cluster control channelsper cluster in the entire CRAHN.

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cluster head having the highest weight among all othernodes and Yi is declared as the set of control channels forthat cluster, denoted Z.

3.3 Gateway node selection mechanism (GSM)After clustering, the next step is to choose the gatewaynode for each cluster. In a cluster-based CRAHN, two

clusters can access each other through different nodes andso there arises the problem of selection of the node moresuitable to function as the gateway node between them. Itis the responsibility of the cluster head to choose the gate-way node for the adjacent clusters. The main goal of choos-ing a gateway and designing the control channels for aCRAHN is that the connectivity between the adjacent

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clusters is strengthened. To strengthen the inter-clusterconnectivity, there should be a large set of channels forinter-cluster communication. Therefore, the node withmore channels in common with the adjacent cluster shouldbe selected as the gateway node.In this study, we propose a mechanism for selecting gate-

way nodes on the basis of degree of connectivity dim, wherei denotes a particular node and m is the label of a particularcluster. The degree of connectivity of node i with aparticular cluster m can be mathematically defined as

dim ¼ Ci∩Zm; ð1Þ

where Ci is the channel list of SUi and Zm is the set ofchannels common to the members of the mth cluster. Thefollowing steps are involved in the selection of a gateway/border node at a particular cluster head:

3.3.1 Procedure: gateway/border node selection

Step 1: Each ith SU broadcasts its cluster identifier andthe list of control channels Yi used in its cluster toits neighbors.

Step 2: Each ith SU computes the degree ofconnectivity dim with its neighboring clusters.

Step 3: Each ith SU sends all the computed degrees ofconnectivity to its cluster head.

Step 4: For each neighboring mth cluster, the cluster headnominates node as a gateway node that has the highestdegree of connectivity and lowest id.

In the above procedure, the identifier assigned to thecluster is based on the cluster head identifier. For example,the graph in Figure 1a is clustered into two clusters labeledΦA and ΦE, where the subscripts represent the clusterheads. ZA and ZE are the idle channels common to thecluster members of clusters ΦA and ΦE, respectively. Thenext step is for the cluster head A to choose the gatewaynode from among the members of its cluster. The clustermembers {D, G} of cluster ΦA having neighbors in ΦE

compute their degrees of connectivity dDE and dGE by usingEquation 1 as follows:

dDE ¼ CD∩ZE ¼ 1; 2; 3; 5; 7f g∩ 2; 5; 7f g ¼ 2; 5; 7f gð2Þ

dGE ¼ CG∩ZE ¼ 1; 2; 3; 4; 8f g∩ 2; 5; 7f g ¼ 2f g: ð3Þ

After computing the degrees of connectivity, both nodesD and G send their degrees of connectivity dDE and dGE totheir cluster head A. Node A then determines the node iwith the maximum degree of connectivity, argmaxi[dim].In the above example, node D has a higher degree of con-nectivity than G because it has three channels in commonwith the common channels of ΦE; thus, D is chosen as agateway node for inter-cluster coordination between ΦA

Figure 2 Bipartite graph for node A (a) and maximum edge biclique graph for node A (b).

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and ΦE, as shown in Figure 1c. The algorithm for selectingthe gateway node is as follows:When a cluster head nominates the gateway for the

mth cluster, two possible cases can be observed:

Case 1. There exists a valid gateway node: in cases wherethere are one or more nodes that have one or morenumber channel in common with the control channelnominated for the mth cluster, one or more validcandidates can be nominated as a gateway node. Insuch a case, after receiving the number of channels incommon between the valid gateway candidate andcontrol channel nominated for the mth cluster (alsomentioned earlier as the degree of the node), thecluster head nominates the node with the greatestdegree as the gateway for the mth cluster.

Case 2. There exists no valid gateway node: in case thatthere exists no node that has any channel incommon with the control channel nominated forthe mth cluster, the mth cluster will not beconsidered a neighboring cluster; hence, no validgateway exists in this scenario. Instead, the controlmessages between these clusters will be exchangedvia another cluster. The number of hops betweentwo clusters depends on the number of clusters. Ifthe number of clusters is less, less number of hopswill be required and vice versa. The number ofclusters depends on the size of clustering, which isbased on the adopted clustering mechanism.However, in case of an isolated cluster where nointer-cluster link of a cluster exists with any othercluster, the proposed GSM will not be helpful.

4 Performance evaluationTo evaluate the performance of proposed GSM scheme,we did a comparison with the existing SOC approachand a modified version of GSM named as GSM′. InGSM′, the clustering is done using the concept intro-duced by [11]. In this clustering, there are one main andone backup channel in each cluster, and all the nodeswhich are in the communication range and have twocommon channels should be part of one cluster. Themetrics used for the comparison were (1) average num-ber of intra-cluster channels per cluster, denoted α,and (2) average number of inter-cluster channelsbetween adjacent clusters, denoted β. The intra-clusterchannels are the channels available for use as controlchannels for local coordination within a cluster. Inter-cluster channels are channels available for use by a gate-way for coordination with adjacent clusters. We examinedthe effect of varying the number of SUs and PUs in asimulation.Let the available spectrum be divided into T = 30

channels. It is assumed that each radio can listen toother radios within a transmission range of x = 40 mand the total area over which this CRAHN is set up is100 × 100 m. The entire area is divided into a gridconsisting of 100 equal elements. The SUs and PUsavailable in the network are randomly distributed in theregion. Any two SUs lying in the same or adjacent gridelements have at least 70% of the available channels incommon. The parameter σ is used to denote the PUarrival rate, which indicates how often a PU occupies achannel. The value of σ is taken to be 0.1 unless other-wise specified.

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4.1 Effect of variation in SU densityFigure 3, graphically illustrates the impact of the numberof SUs on the average number of clusters. As mentionedearlier in section 2, the same approach is used for clusteringas used in SOC. Therefore, the same average number ofclusters is formed in both the proposed GSM approach andthe SOC, as depicted in Figure 3. However, in GSM′, thenumber of clusters is less than GSM or SOC. In SOC, theobjective is to maximize the product of the number ofnodes and the number of common channels in eachcluster. On the other hand, in GSM′, the objective is tomaximize the number of nodes, having just two channelsin common with each other. Hence, in GSM′, relativelylarge clusters will be formed than SOC. This will lead to re-ducing average number of formed clusters in GSM′. WhenSU density is low, most of the SUs may be disconnectedfrom each other and more clusters are formed. When moreSUs join the network, however, the average number of clus-ters starts to decrease because more SUs find others thatare close. However, the average number of clusters starts toincrease once again after a certain number of SUs havejoined the network. As shown in Figure 3, this occurs after700 SUs have joined the network.Figure 4 illustrates the impact of the number of SUs on

the average number of control channels per cluster,denoted by α. The performance of both GSM and SOC isthe same in terms of the value of α. However, the value forGSM′ is quite less than that for GSM because GSM′ triesto maximize nodes in the cluster rather than commonchannels, therefore, all the nodes within communicationrange having at least two channels in common are includedin one cluster. Therefore, the value of common channelsfor GSM′ is greater than 2 but not comparable with GSM.The value of α decreases as the number of SUs increases.Figure 5 shows the effect of increasing the number of

SUs on the average number of channels between theadjacent clusters, denoted by β. The value of β rises as thenumber of SUs in the network increases. The reason ofincrease in β is that more SUs joining the network lead to

an increase in candidate nodes that can act as gatewaynodes between the adjacent clusters. Therefore, choosing agateway node from a large number of candidates increasesthe probability of finding a node with a large number ofcommon channels with the control channels of theadjacent clusters. It can be seen that the proposed GSMapproach outperforms SOC in terms of β. However, GSM′performance is comparable with SOC, but lower thanGSM because it has less number of common channelsdesignated for intra-cluster communication, which alsoresults in the reduction of degree of the candidate gatewaynodes, which is based on the number of channels which anode can have common with the common channel desig-nated in the neighboring cluster. However, the perform-ance of GSM′ is comparable with SOC because instead ofchoosing a gateway node randomly, it selects the node withmaximum value of degree and secondly, as it results inlarge cluster size; hence, more possible candidates for gate-way selection are there.

4.2 Effect of variation in PU densityFigure 6 shows the impact of the number of PUs on α.In this case, the number of PUs is varied from 10 to 100.

Figure 3 Impact of SU density on the average number of clusters.

Figure 4 Impact of number of SUs on average number ofintra-cluster control channels per cluster (α).

Figure 5 Impact of number of SUs on average number ofinter-cluster control channels (β).

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In addition, the simulation is conducted for PU arrivalrates from ranges 0.1 to 0.5. The PU arrival rate σ isdefined as the frequency with which the PU occupiesan available channel. Both GSM and SOC gave thesame results. However, GSM′ has lower α because ittries to maximize the number of nodes with at least

two common channels in a cluster. The value of α de-creased as the number of PUs, as well as the PU arrivalrate, increased. For example, for GSM, it can be seenthat for a PU arrival rate of 0.1, α is near 15 when thereare 10 PUs, decreases as the number of PUs increases,and finally, reaches a value of 11 when the number ofPU increases to 100.Figure 7 shows the impact of the number of PUs from

10 to 100 on β. The simulation is conducted for multiplevalues of PU arrival rate varying from 0.1 to 0.9. Thevalue of β decreases as the number of PUs or PU arrivalrate increases. It can be seen that GSM outperforms theSOC in terms of β. GSM′ performance is lower thanGSM because it has less number of common channelsdesignated for each cluster than GSM but GSM′ hashigher or similar performance to SOC because SOCemploys a random gateway node selection mechanismwhereas GSM selects a node with the highest degreeamong the possible gateway candidate nodes in a cluster.For example, in Figure 7a, for a PU arrival rate of σ = 0.1and 10 PUs, our proposed scheme has the value of β as2.5 whereas SOC has a value of 1.5. Hence, our techniqueis more robust and reliable for inter-cluster coordinationthan SOC.

5 ConclusionIn this study, we examined the control channel estab-lishment problem in CRAHNs. More specifically, welooked at the effect of spectrum heterogeneity and PUdynamic force in the formation of a group-based controlchannel assignment for CRAHNs. To avoid frequent re-clustering, SOC nodes in such a way that more backupchannels are available in each cluster. Whenever a channelgets occupied by a PU, instead of re-clustering, controlinformation exchange migrates from the occupied channelto one of the other backup channels. We extended thisconcept from the intra-cluster level to the inter-clusterlevel. We surmised that inter-cluster coordination can bestrengthened if more channels are between the adjacentclusters and proposed a technique for gateway nodeselection that ensures that more channels are availablefor control channel establishment between adjacentclusters. Our proposed approach provides significantimprovement over SOC in terms of the average numberof inter-cluster control channels.

Competing interestsThe authors declare that they have no competing interests.

AcknowledgementsThis work was supported by the Ministry of Science, ICT and Future Planning(MSIP), Korea, under the Convergence Information Technology ResearchCenter (C-ITRC) support program (NIPA-2013-H0401-13-1003) supervised bythe National IT Industry Promotion Agency (NIPA). It was also supported bythe Seoul R&BD Program (SS110012C0214831) and Special DisasterEmergency R&D Program from National Emergency Management Agencythrough Kyungil University (2012-NEMA10-002-01010001-2012).

Figure 6 Impact of number of PUs and arrival rate on averagenumber of intra-cluster control channels (α). Arrival rate(varying from 0.1 to 0.5). (a)σ = 0.1, (b)σ = 0.3, (c)σ = 0.5.

Figure 7 Impact of number of PUs on average number of inter-cluster control channels with different arrival rates. (a) σ = 0.1,(b) σ = 0.3, (c) σ = 0.5, (d) σ = 0.7, and (e) σ = 0.9.

ul Hasan et al. EURASIP Journal on Wireless Communications and Networking 2013, 2013:171 Page 10 of 11http://jwcn.eurasipjournals.com/content/2013/1/171

Received: 31 March 2012 Accepted: 3 June 2013Published: 21 June 2013

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doi:10.1186/1687-1499-2013-171Cite this article as: ul Hasan et al.: GSM: gateway selection mechanismfor strengthening inter-cluster coordination in cognitive radio ad hocnetworks. EURASIP Journal on Wireless Communications and Networking2013 2013:171.

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