Cluster based routing in cognitive radio adhoc networks: Reconnoitering SINR and ETT impact on clustering

Computer Communications 115 (2018) 10–20

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Cluster based routing in cognitive radio adhoc networks: Reconnoitering SINR and ETT impact on clustering Nitul Dutta a, Hiren Kumar Deva Sarma b,∗, Zdzislaw Polkowski c a

Computer Engineering Department, MEF Group of Institutions, Rajkot, Gujarat, India Department of Information Technology, Sikkim Manipal Institute of Technology, Sikkim, India c Jan Wyzykowski University, Skalnikow St.No.6 B-59-101, Polkowice, Poland b

a r t i c l e

i n f o

Article history: Received 22 January 2017 Revised 2 July 2017 Accepted 1 September 2017 Available online 8 September 2017 Keywords: Cognitive radio CRAHN SINR ETT Cluster based routing

a b s t r a c t A novel clustering approach for Cognitive Radio Ad Hoc Networks (CRAHNs) is proposed in this paper. The Signal to Interference and Noise Ratio (SINR) produced by Primary Users (PUs) on collocated Cognitive Users (CUs) along with Expected Transmission Time (ETT) among CUs is taken into account for cluster formation. The primary concern of the proposed work is to find a suitable method of cluster formation for efficient routing in CRAHN. The suggested algorithm is simulated in ns-3. Through the observed results, values of some base parameters for efficient clustering are established. Efficiency of the proposed approach is also compared with three existing cluster based protocols. Results reveal that the proposed algorithm performs better. One cluster maintenance algorithm is also proposed in the paper. Future scope of the work is outlined. © 2017 Published by Elsevier B.V.

1. Introduction Invention of Cognitive Radio (CR) by Joe Mitola in 1999 [1], enables wireless communication to enter into a new paradigm. The CR technology provides a flexible solution for the problem of spectrum scarcity by means of dynamic spectrum allocation for network communication. It allows coexistence of CR capable devices with licensed band users [2] and enables the later to use the licensed spectrum opportunistically. The CR devices can efficiently sense available idle spectrum, reconfigure parameters to access the temporarily unused spectrum, and produce insufferable interference to licensed users, which make them capable of networking among themselves. Taking into consideration the above mentioned features of CR, the idea of Cognitive Radio Networks (CRNs) is conceived. The CRN has proven itself as a network to increase spectrum efficiency through self-organization and dynamic reconfiguration and hence, the Cognitive Radio Ad hoc Network (CRAHN) [3], is coming up as a promising communication technology. But, CR users must abort their communication or lower their transmission power to avoid disturbances to authorized licensed users (called Primary Users (PUs)). Because, PUs are the owners of the channel; and the CR devices (also called Secondary Users (SUs)) opportunistically use the channel when PUs have no data to transmit [4]. That is why ∗

Corresponding author. E-mail address: [email protected] (H.K.D. Sarma).

http://dx.doi.org/10.1016/j.comcom.2017.09.002 0140-3664/© 2017 Published by Elsevier B.V.

unused spectrum (called as spectrum hole) must be used efficiently to make CRN successful. Besides that, if transmissions of CUs do not cause harmful interferences with PU transmissions, then CUs can communicate among themselves. CU and SU essentially mean the same. Throughout the discussion we shall use the term CU, SU and CR interchangeably for a cognitive node. Clustering of nodes is one of the most widely probed solutions for scaling down ad hoc networks. The logical grouping of nodes reduces signaling overhead for network operation while maintaining the network connectivity. The specific objective of such grouping is generally influenced by various network characteristics and application requirements. In a dynamic environment, clusters partition a network into simpler and stable form and hide local changes from being propagated to whole network. It also reduces the cost of routing information update in dynamic environment. In CRN, restricting communication to single hop enables the scope of efficient use of spectrum and other resources like node power. Simultaneously, clustering diminishes the need of triggering network wide update due to the sudden appearance of a primary user or occurrence of node mobility. Another advantage of clustering is the improvement in the efficiency of routing and multicasting by keeping minimum nodes in the backbone network. One common problem with CRN is as mentioned below: sometimes, even if the secondary users (SUs) are within the coverage of each other, node connectivity may not sustain due to the appearance of primary users (PUs). This feature of CRNs may prevent efficient use of spectrum hole through existing clustering schemes which are

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considered suitable for conventional ad hoc wireless networks. So, special attention is needed to implement clustering in CRN. Although, SU is normally not allowed to communicate using a channel currently used by the owner PU, in reality if the SU node is located outside the interference range of PU then there is no harm communicating by the SU using the same channel. In that case, the SU should control the transmission power to avoid interference with owner PU. Through clustering of nodes, we can exploit the concurrent communication since the member of the cluster can communicate to the cluster head (CH) with low power signal that will not interfere with the PU. But, we need special care to form clusters. An efficient transmission with minimum signal power may be achieved through clustering where CUs are grouped based on some predefined criteria and transmission is carried out through designated group leaders called Cluster Heads (CHs). All the CUs in a cluster communicate with it’s CH which is very close to it and hence produce low power signal that will not interfere the nearby PU. In this paper, a novel method of clustering is discussed which is based on the Signal to Interference and Noise Ratio (SINR) of PUs on cognitive node and interference among CUs along with Expected Transmission Time (ETT) among CUs. A preliminary version of the work is available in [5]. However this paper extends [5] through some more experimental evaluations. In this work, additional comparisons are made with other relevant state of the art protocols. Rest of the paper is organized as follows. A set of related research is discussed in Section 2. Motivations and significances of this research work are stated in Section 3. The proposed protocol is presented in Section 4. Section 5 reports experimental results and the work is concluded in Section 6. 2. Existing routing protocols for CRN Extensive research has been conducted on routing via clustering in traditional ad hoc networks [6]. However, the dynamic unreliable spectrum availability in cognitive radio networks introduces new challenges for applying clustering in CRNs. In this section, we review some of the existing works on clustering in CRAHNs. A cluster based protocol for establishing common control channels within single hop is proposed in [7]. As suggested by the authors, the protocol is designed mainly for static or quasistationary cognitive based networks. The protocol uses channel availability, signal strength and channel quality between two nodes as the metrics for cluster formation. Although, the protocol in [7] is suitable for static network, it cannot perform well in highly mobile network. Moreover, it has to perform two rounds of information exchanges for determining the rank and reserved values and hence incur additional overheads. Authors have used weighted factors for computation of reserved values [7], however selecting optimal values of such parameters are difficult. The protocol does not address any issue related to data packet forwarding among cluster heads. Another similar work as described in [7] is also found in [8]. As in [7], this protocol also uses channel availability along with node degree level in order to form clusters in a single hop cognitive radio ad-hoc networks. The common channel of a node to all its neighbors is called local connectivity degree of that node. But, the protocol has certain flaws. If spectrum connectivity and the local connectivity of two nodes are same then the algorithm selects the node with lower node id as the cluster head. This may lead to unstable cluster head selection. This algorithm suggests to select the CH first and then the cluster members. It may lead to ambiguity if one node becomes the member of multiple clusters. Nodes with no common channels with the neighbors, are left as CH which increases number of clusters. Data may need to forward through a node which is a member of two clusters and this leads to generation of additional overheads. The algorithm takes steps

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equal to the number of CR nodes to converge in worst case, hence may be an issue with large number of CR nodes. The approach suggested in [9] is another similar work to [8] that uses local connectivity degree parameter for cluster formation. Unlike [8], the work of [9] is targeted at forming clusters among multi-hop connection. Along with channel availability, the node degree level is also used as parameter for cluster formation. However, as the protocol emphasizes on k-hop neighbors, there are possibilities of having less number of common channels in a cluster and hence less effective clusters are formed. Further, due to lack of common channels, switching of channels may be required within a cluster and hence incurs more overheads. Since the number of intra-cluster common channels is preferred in the optimization, the number of generated clusters by the proposed protocol may not be optimal. In [10] a cluster formation procedure is proposed with an objective to enhance cluster stability in multi-hop clusters with moderately mobile environment. The algorithm uses channel availability and node degree as metrics for cluster formation. However, the protocol is designed for highly mobile airborne nodes that moves to a fixed target with a stable speed and hence, not suitable for mobile users for day-to-day operations. Furthermore, the node’s computing capacity is taken as one of the metrics for cluster formation. Therefore, for a network with homogeneous nodes, the node capacity cannot contribute well to the clustering decision. Like other multi hop clustering algorithms, this algorithm also diminishes the chances of having common channels in a cluster and consequently, overheads due to channel switching are introduced. A new concept of clustering is presented in [11] and in [17]. These proposals are based on the most promising concept of clustering in data mining, called Affinity Propagation (AP) [12]. The paper in [11] works on single hop and the paper in [17] works on two-hop neighbors. The affinity concept uses a small managerial packet called Affinity in order to pass managerial information among agents. The various clustering metrics used in the approach are channel availability and node degree level. Conversely, AP based algorithms are good for data but may not be suitable to model the behavior of CR nodes with dynamic nature. Due to several iterations of the algorithm and subsequent message exchanges, the complexity of the algorithm increases and it wastes bandwidth which is not affordable in CRN. AF based methods take more time to converge during cluster formation. The centralized version of [11] possesses single point of failure and bottle neck due to existence of central coordinator. The algorithm provides a heuristic solution than optimized one. In [13,14] a cluster based framework with a topology management scheme for CR network is proposed. A node selects the channel which is available with maximum number of its neighbors and selects it as a Common Control Channel (CCC) and all the nodes construct cluster among them. The algorithm further executes cluster merging algorithm to reduce total number of clusters. This algorithm emphasizes on the existence of at least one CCC within a cluster and optimizes the cluster size. However, the drawback of this algorithm is its tendency to change the cluster structure along with the variations in the spectrum availability. This needs reconstruction of clusters and thereby resulting in more control overheads. Furthermore, the algorithm produces large number of clusters in the initial stage of cluster formation. It also does not pay attention to set of common channels and size of cluster. The proposal stated in [15] suggests a mechanism of computing degree of correlation of available channels with neighbors. The algorithm does not emphasize on having a local CCC in a cluster. It sets a threshold value for the computed degree of correlation. The algorithm exchanges the threshold value with neighbors if the degree is greater than the pre-specified threshold. However, selecting an appropriate threshold itself is a challenge for the protocol.

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Even the algorithm does not state anything if two nodes have the same threshold value. In [16], a spectrum opportunity based control channel assignment and clustering scheme is proposed as a distributed clustering algorithm to address the issue of dynamic control channel assignment in CRNs. However, the channel hopping phase of the protocol degrades the throughput of the algorithm. This is because the hopping sequence devotes too much spectrum resources on control function. An energy-aware routing protocol [28] uses relative energy metric to select the optimal route. Protocol minimizes the average total power consumption and prolongs the network lifetime. This protocol has been designed to find an energy efficient route for a session using a selected set of nodes, channels, and transmitting power. This protocol proposes a new routing metric, called the energy weight of the link. The energy weight of the link increases along with the decrease of reserved energy of the node. When the weight factor of a link reaches a threshold value, the link can be avoided in order to conserve the energy of the node. This approach prevents the network partitioning problem from occurring. An energy-efficient routing protocol [29] enables efficient data flow coordination and energy conservation in distributed cognitive radio ad hoc networks. The first part of the protocol is an assigning mechanism that contributes towards finding a sequence of the best relay nodes for an optimal routing path. Second part of the protocol is the capacity-aware scheme that implements nonperiodic sleep wake schedules to ensure the selection of energy efficient paths. Here the route discovery and route reply phases are based on AODV routing schemes. The assigning mechanism is responsible for finding which neighboring node for an intermediate node is most suitable to serve in the routing path. This selection is based on an evaluation of the metrics. The geo-location database is used to find the information about the neighboring nodes. The source node is informed by the intermediate node about the selected node via a route reply (RREP) packet. Then a confirmation message is delivered to the new relay node in order to ensure its inclusion in the route. In addition, the protocol makes use of the Backward Traffic Difference (BTD) methodology in order to achieve energy efficiency. This method analyzes incoming traffic patterns. This analysis is needed to assign dissimilar sleep times to the secondary nodes. Secondary nodes do not consume energy during sleep mode. The low-latency and energy-based routing (L2ER) protocol [30] is a multi-metric routing protocol for CRAHNs. This protocol may also be classified as on-demand and reactive one. This protocol tends to avoid tradeoff situations between the route with the shortest end-to-end delay (without considering nodal energies), and the route with a high residual energy but significant end-to-end delay. A new cumulative metric is defined as which accommodate both energy and delay factors and enable an efficient routing decision. The route discovery process is similar to AODV. This process is on-demand and spectrum-aware. The route request packet used in the protocol is new and is termed as L-RREQ. L-RREQ has some additional fields as compared to conventional RREQ packets. These additional fields contain the time at which the L-RREQ packet was originated and the path energy. When an intermediate node receives an L-RREQ packet, it updates its energy status to the path energy field of the L-RREQ packet. This update is carried out provided that the residual energy of the node is greater than the threshold value assigned. The intermediate node selects the appropriate channel from the list of available free channels and then forwards the L-RREQ packet. The MAC layer provides the information about occupied and available channels. The enhanced dual diversity cognitive ad hoc routing protocol (E-D2CARP) [31] is a new approach to energy efficient routing in cognitive radio ad hoc networks. There are three different approaches which need strong coordination amongst them in

order to select an optimized path. These approaches are expected path delay (which is a routing metric used by the protocol), PU region avoidance (which is used to keep the level of interference low), and joint path and spectrum diversity (which is used to have multi-path and multi-channel routes and therefore, can handle route failure). The any path routing protocol [32] strives to deliver the data packets successfully to the destination with the minimum number of transmissions. Here, a node shall multicast intended data packet to multiple intermediate nodes in order to form forwarding set. Any node in the forwarding set can forward the packet. However, it is in accordance with the priorities they are assigned. There is an order of priority maintained and this required to avoid redundant transmissions. A node in the forwarding set can forward the packet subjected to the condition that no nodes with a higher priority have done so. This approach forms a solid basis for solving the PU occupancy problem because it opens the door for opportunistic routing. In addition, any path routing offers the advantage of low packet loss. This is because at least one node in the forwarding set can successfully transmit during every opportunity. The major challenge in implementing the any path routing protocol successfully is dependent on how well the multi-channel rendezvous problem is handled. The energy and interference aware cooperative routing protocol [33] utilizes two algorithms to minimize power consumption and to avoid interference. The first algorithm works to minimize the transmission power. This is done by including a relay node in the shortest path found. The second algorithm focuses on improving the primary user receiver protection by allowing a cooperative relay with a minimum overlapping area. This minimum overlapping area is introduced into the route. The minimum channel switch routing (MCSR) protocol [34] finds the routing paths that have the minimum number of channel switches. Channel switching becomes necessary when the preferred PU channels for upstream and downstream nodes are different during data transmission. Each link is weighted. Here, the SU–SU edge based on the preferred PU channel is designated by the SU nodes and that forms the edge. The weight of the edge is assigned 0 when the preferred PU channel for both of the end nodes is the same; otherwise, it is assigned 1. The MCSR protocol sums the weight of each link in order to calculate the total weight of each routing path. The path with the smallest sum is selected. The routing process in MCSR is based on AODV routing with modified RREQ packets. The RREQ packet has two distinct fields: (i) a route vector field to record the IDs of the source, destination, and relay nodes, and (ii) a preferred PU channel vector field to record information about the occupied PU channel at that instant. This protocol minimizes the number of channel switches in a routing path which is of great importance in CRAHNs. All the protocols discussed above emphasize on opportunistic transmission. Formation of clusters are taking mostly the node degree, available common channels, link quality and stability into account. However, in clustering environment, concurrent communication may be possible if the CU transmission power is controlled. This improves utilization of spectrum by enabling concurrent transmission of CR nodes with primary nodes. To fill up this gap in clustering for CRN, and to facilitate concurrent communications, a clustering mechanism is proposed in this paper. The proposed technique enables simultaneous transmission of CR nodes (also termed as SUs) and primary nodes (PUs). 3. Motivation and significance From CU perspective, PU transmissions appear as noises. The Signal to Interference and Noise Ratio (SINR) gives a better representation of such unwanted signals. So, SINR is an important

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parameter that may be considered as a metric for cluster formation. We assume that at a certain distance from PU transmitter, the strength of the signal is weak enough for the PU receiver. That region may be used by CU devices for concurrent communications. Our aim is to form a cluster of all the nodes that experience similar SINR, limited by a threshold. The concept of SINR in clustering of CR nodes are used in [18] (adopted from [19]). The authors have considered the impact of SINR imposed by collocated CR users on channel quality. Yet, the work has not considered the impact of PU on the CU for SINR calculation or cluster formation. Further, in [18], every CU forms a cluster at the initial stage and later they merge to reduce number of clusters. It may be a drawback as cluster merging incurs additional overhead. In our proposal, we group as many CUs as possible in a cluster in initial phase of cluster formation. We also include Expected Transmission Time (ETT) as another metric for cluster formation in combination with SINR. During PU communication, either the CU node has to stop communication or they may lower down the transmission power so that it does not harm the PU transmissions. SINR produced by PUs on a CU node may be used to measure the impact of CU transmissions over PU nodes. A low SINR implies that at certain point power of interference is higher than the signal power. At that point, the power of transmitted signal is of no use or cannot take over the interference power. If we observe the same activity from the PU perspective, then the signal of CU transmission is low enough to harm the PU transmissions. Inversely, if the CU restricts the power of transmissions to less than a given threshold, transmissions of CU cannot harm the PU operation. So, the nodes at a distance of certain threshold (determined by PU interference) may communicate among themselves without harming the communications of PU. Hence all such nodes may be inside the same cluster. Keeping this in mind, the SINR of PUs on certain CU is used as a parameter for cluster formation. The significance of this work may be summarized as follows: • Signal to Noise and Interference Ratio (SINR) is considered for cluster formation. • Expected Transmission Time (ETT) is one of the parameters selected for cluster formation as well as merging in order to produce optimal clusters. • CR nodes under coverage of more than one PU are forced to produce less number of control packets and act as outliers initially. 4. Proposed algorithm 4.1. System model The network topology considered in this work assumes coexistence of randomly distributed static PUs and mobile CUs. There are M non-overlapping channels denoted by M = {1, 2,…,M}. Each PU is assigned a fixed channel out of the set M. The coexisting CUs are equipped with a half-duplex cognitive transceiver and can tune themselves to different channels for communication. In order to form clusters, initially all CUs detect available spectrum and acquire neighbor information. Both of the activities are challenging issues in CRAHNs, details of which are out of the scope of this paper. Many methods of spectrum detection are described in [27], however we have adopted the methodology described in [16,23] for spectrum detection and [20–22] for neighbor discovery. CU can access only one channel out of M at a time and can no way harm the PU conversations. No fixed common control channel is used for managerial packet transmissions among CUs. Any one of the existing and available channels is used for the purpose. Fig. 1 demonstrates a typical architecture reflected in the proposed system topology. The diagram shows three primary users

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Fig. 1. Sample network topology.

PU1 , PU2 , PU3 occupying channel 1, 2 and 3 respectively. Various secondary users are also shown within their coverage area. Secondary devices when get into the CRN environment sense the environment [16,23] and collect the information about available and under use PU channels. It also gathers the information of its neighbors using packets as described in [20–22]. Every SU node u maintains a set N (u ) which contains the ids of its neighbor SUs. Once it acquires the neighbor information and channel information it computes SINR and ETT using the methods described in next subsection (Section 4.2). Later, these information are exchanged among neighbors to form clusters. To minimize transmission of managerial packets, SUs under the coverage of multiple PUs refrain themselves from transmitting information exchanging packets for certain time interval till some clusters are formed by nodes under coverage of single PUs. Once few clusters are formed, remaining SUs may join any nearby clusters based on ETT values. For example, SU12 is under coverage of PU1 and PU2 (at a distance of d1 and d2 respectively), SU23 is under the coverage of PU2 and PU3 . In such cases the said ETT value is considered to decide which cluster the CU will be a member of, or it is an outlier that can act as a gateway (GW). The gateway is actually a CH without members and forwards packets for other CH or GWs. In the following sub-section procedures for calculating SINR and ETT are outlined. 4.2. SINR and ETT computation In this proposal, the calculated SINR prediction is used to form clusters among CUs. Further, two CUs located at equal distance (may be in opposite side of the same PU) may have same SINR value, but combining them to a single group shall lead to introduction of additional transmission cost. So, in such cases, the Expected Transmission Time (ETT) among CUs are taken into account for clustering. Using this mechanism, two CUs will be in the same cluster only if both the CUs have closer predicted SINR value within the same threshold (TSINR ) and the ETT among CUs are within the ETT threshold (TETT ). The SINR and ETT computation procedures are described below. For SINR computation let us assume that signal power of node PUi at node SUj is Ei→ j ( p). Similarly, the signal power of neighbor SUk on the SUj is denoted by Ek→ j (s ). Considering environmental noise as N, the signal to Noise Ratio (SNR) at SU node j for PU node i is computed as given below [19,24].



SNR =

Ei→ j ( p)α Pi→ j N



(1)

where, α (Pi→ j ) is the factor that shows the loss of PU signal at SUj . The value of this loss factor is in between 0 and 1 assuming Random Loss model as discussed in [24]. It is dependent and

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decreasing function of the Euclidean distance between the said PU and the SU. Higher the distance from the PU, lower is the SNR value. The above equation however does not include the interference produced at the SU node. In order to include the interference with the SNR, the following equation is used.

l







Ei→ j ( p)α Pi→ j  N+γ m k=1 Ek→ j (s ) i=1

SINR =

(2)

l is the number of PUs that covers SUj and m is the neighbors of SUj . γ is the constant that determines the characteristics of the gradient of the transmission environment. Eq. (2) shows the impact of PU signals as well as neighbors CUs on a particular secondary user. Expected Transmission Time (ETT) is a good measure of probable time required to transmit a packet between two points. It reasonably predicts the link quality. Lower value of ETT is a desirable criteria for better throughput. Without considering interference into account, the ETT of a link l can be computed by calculating the expected number of transmissions (ET X (l ) ) required for successful delivery of the packet as stated in Eq. (3) as reported in [19].

ET X ( l ) =



1

1 − p (l )



1 − p (l )

=

1 1 − p (l )

2

(3)

Fig. 2. Cluster formation.

p(l ) is the packet loss probability in a channel in both forward and reverse direction. The expected transmission time (ET T(l ) ) for the link l may be computed as given in Eq. (4).

ET T T ( l ) = ET X ( l ) ×

SF xd SLrg/(Ts − Tl )

(4)

SF xd is the size of a fixed sized packet, SLrg size of large sized packet, (Ts − Tl ) is the time between arrival of the two packets. 4.3. Cluster formation The CU that detects activity of only single PU prepares a message containing its identity, SINR etc. and exchanges with its neighbors. However, CU nodes under the coverage of multiple PUs do not immediately transmit their information to their neighbors. Such CUs wait for a period determined by the KEEP_SILENT timer. After expiry of the timer, such CU nodes, if they listen to any CH advertisement, they join the appropriate cluster. Otherwise, declare themselves as outliers. At the end, each CU constructs a table of its neighbors with SINR and computes ETT. Fig. 2 shows a network topology with CUs and PUs. Tables created by cognitive nodes under the coverage of PU1 are shown in Fig. 3. The highlighted row in a table indicates node’s own information. To form the cluster, all nodes compute difference in SINR with its neighbor from the stored information. All nodes with SINR difference within TSINR and ETT within TETT are placed in the same cluster. Within a cluster, the node with lowest SINR is selected as cluster head. Any node having more SINR difference than TSINR or TETT will not join any cluster, and is marked as an outlier. All nodes that are covered by multiple PUs refrain themselves for certain time interval (determined by the KEEP_SILENT timer) from participating in cluster formation. After CH selection, CH broadcasts its identity within his coverage range through message. The node under multiple PU coverage hears CH declaration message from multiple CHs. Such SU then compares the difference of the SINR (TSINR ) and ETT (TETT ) with CH and joins the cluster with smallest value. If SINR of the SU is not within the threshold value then it declares itself as gateway (GW). Fig. 2 also shows the final cluster formation by CUs. It shows six clusters including three outliers. Outlier 1 has same SINR difference with that of members in cluster 2, but the ETT to CH of

Fig. 3. Information gathering by CU nodes.

cluster 2 from the outlier is more than the TETT , so left as outlier. Similarly, outlier 2 and 3 are in the coverage of three and two PUs, respectively. After construction of the initial clusters, outlier 2 will join cluster 2 as its ETT to CH (of cluster 2) is within the threshold. But outlier 1 and 3 will work as a GW as their ETT is more than the threshold. Various procedures used for cluster formation are discussed next.

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Fig. 4. Pre-processing procedure.

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Fig. 8. Cluster formation algorithm.

Fig. 5. SINR computation procedure. Fig. 9. Cluster merging algorithm.

Fig. 6. Packet processing in a CU node.

Every CU executes Algorithm 1 and communicates a packet after calculating the SINR value using Algorithm 2. On receipt of the packet by a CU, it creates a table of neighbor information as demonstrated in the Fig. 3. The processing of packet is shown in algorithm 3. On receipt of the packet communicated by a CU node from its neighbor, it compares difference of its own SINR and SINR of the CU contained in the packet. If the difference is within the threshold then it computes the ETT to the said node and if ETT is also within the threshold then sends a message to create group to all such neighbors. The procedure is give in the form of algorithm 3. When the create group message is processed (algorithm 4) by nodes, they simply create a table of their neighbors and respective SINR and ETT values. Once the neighbor information are created by all the CU nodes, the cluster head selection process starts. To select the cluster head, the concerned node compares its SINR with the entry in the table and the node with highest value of SINR is selected as the cluster head. The procedure of cluster head selection is discussed in algorithm 5. Every cluster head sends periodic CH advertisement message and any CH that hears such advertisement, executes algorithm 6 to check the probability of merging two clusters. In the initial stage of the process, many clusters are created. The execution of cluster merging algorithm reduces the number of clusters and it is considered to be efficient for cluster based routing. This algorithm is also used in the optimization and maintenance phase as described in the next subsection. 4.4. Optimization and maintenance

Fig. 7. Procedure to create a cluster.

There are two reasons because of which clusters may be merged. In the initial stage of cluster formation, no optimization

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in number of cluster formation is considered and hence many clusters are formed. So, an optimization mechanism for reducing the number of clusters is applied. As a result of optimization, some clusters may merge. Another reason of merging is, CUs random movement may bring two groups closer to each other, thereby leading to the need for cluster merging. In such cases, it may so happen that the two groups coming closer due to mobility may not remain in the same cluster for longer time. The group may be a passing by group that crosses that area. As a result of continuous movement, such merging will lead to unnecessary operation and hence introduces performance degradation. So, we adopt a mechanism not to merge two clusters immediately as they come closer. After completion of CH selection, the CH advertises itself through message. Every CH within the same transmission range on receipt of the advertisement computes ETT to the advertised CH. If the ETT is within a pre-specified threshold (TETT ) then two clusters merge with each other. In this phase, most of the outliers are merged and this generates optimized number of clusters. However, to avoid unnecessary merging, merging is delayed up to some time period specified by MERGE_DELAY timer. On hearing of from others, a CH starts the above mentioned MERGE_DELAY. After the expiry of the timer, if both CHs are still within the same radio range then they calculate ETT of each other. If the ETT is within a threshold TETT , then both clusters merge and the CH with lower SINR takes over as new CH. An algorithm is proposed as stated in Algorithm 3 to perform merging of clusters. Every cluster head hearing the executes the algorithm.

14 SINR (Thr)=1dB SINR (Thr)=3dB SINR (Thr)=5dB

12

No of Clusters

16

10 8 6 4 2 0 20

40

60

80

100

120

Number of CUs Fig. 10. Impact of TSINR on cluster formation.

channels and hence path loss and delay encountered in two ray model are assumed in the simulation environment. The wi-fi MAC standard is set to 802.11 g with a rate of 11 Mbps as stated in [25]. Multiple UDP (User Datagram Protocol) flows are created with a constant data rate of 512 kbps between randomly selected pair of CR nodes during simulation. The PU nodes follow an on-off state with exponential distribution having average duration of 2 s and 10 s, respectively. Moreover, for few of our experiments, we have changed mean on-off duration. Each simulation is carried out for 500 s. A set of 20 simulation runs are performed and average of such results with 95% confidence interval has been considered for analysis.

5. Experimental results 5.2. Evaluation of the proposed protocol An experimental setup in ns-3 [25] is developed to evaluate the performance of proposed mechanism of cluster based routing. The primary focus of this simulation study is to carry out investigation on various parameters that contribute to cluster formation and to compare the performance of the proposed mechanism with state of the art algorithms [9,17,18]. A brief description of the simulation test bed is presented in the next subsection. An analysis of the simulation results is also presented. 5.1. Simulation setup A simulation environment of 11 PUs (with 1 dedicated channel per PU) and SUs (or CR devices) ranging from 20 to 200 in number is created in ns-3. The ns-3 network simulator has been installed on Fedora core Linux with kernel version 3.13. The CPU is an Intel Core i5 3210M clocked at 2.50 GHz having RAM of 8GB. All simulations were performed in a single thread per core. All PUs are static and CUs are distributed uniformly in an area of 10 0 0 × 10 0 0 m2 . The CU nodes are allowed to move according to the random walk mobility model [26]. The CRN module of the ns3 environment [25] facilitates to define any number of cognitive interfaces for a CR device. Each interface constitutes of three separate MAC-PHY layers for control packet transmission, data packet transmission and reception. However, we have not used the control interface as no common control channel is implemented in our model. The CUs can transmit effectively within a circular range of 50 m and can receive effectively in area of 70 m. The interference range of a PU is set to 70 m. For transmission, assumed current is 330 mA and for reception it is 230 mA with a voltage of 5 V. Based on these values different energy amount consumed are as mentioned below: idle 1.0 Joule/s, receiving 1.1 Joules/s and transmitting 1.2 Joule/s. Each CR node contains an initial energy of 10 0 0 Joule. The PHY layer defined in ns-3 can switch between any numbers of defined channels; however, we have implemented 11 channels. Although each of the channels can have different propagation model, we have assumed only two ray ground propagation model [25] for all the

A clustering algorithm is considered efficient if it generates as minimum clusters as possible at the initial iteration of the algorithm. So, the number of clusters formed in the network is a major concern for most of the cluster based routing protocols. In this sub-section, an observation is made regarding the initial cluster formation by the proposed algorithm with respect to the input parameters like SINR, threshold TSINR , ETT, threshold TETT etc. The TSINR , which is the difference in predicted SINR value of a CU with its neighbor CU (as discussed in Section 4.3), plays a major role in cluster formation in the proposed algorithm. The selection of TSINR is very critical for better result of the algorithm. So, in this section, some experimental results are presented to observe the impact of these parameters on cluster formation. Taking these observed values as basis, performance of the algorithm in terms of total number of clusters and outlier formation is also examined. Fig. 10 shows the impact of TSINR (the threshold of SINR) on initial cluster formation. Although actual values of SINR are negative, we are considering the difference in SINR value for TSINR . The ETT threshold (TETT ) is considered as 10 ms. Presented graph depicts that increasing SINR threshold, drops the number of cluster formation. As the threshold is more, large number of CUs can be grouped into single cluster. By this graph, it is tried to figure out a suitable value of TSINR which could be used in future analysis of the proposed algorithm. With TSINR equals to 3 dB the number of cluster formation is almost stable in presence of more than 80 CUs. Although not necessarily optimum, 3 dB is selected for observation of other results in this paper with network size of 120–150 CUs. For selection of a suitable value of ETT threshold (TETT ), similar experiments are carried out and observations are presented in Fig. 11. The figure shows that the increase in ETT threshold value leads to increase in number of initial cluster formation. So a suitable value of ETT seems to be in between 10 to 15 ms, as with that value initial cluster formation is comparatively stable. As stated earlier, the initial execution of the proposed algorithm forms many clusters. Then, the algorithm executes the maintenance procedure

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Fig. 11. Impact of TETT on cluster. Fig. 13. Impact of TSINR on outlier.

Fig. 12. Cluster Stabilization over time.

to minimize the number of clusters by merging few of them based on some predefined criteria. After having suitable values for TSINR and TETT , effort has been made to find out the stable cluster formation, over time. Fig. 12 shows a graph of a simulation observed for 500 s and recorded stability in total cluster formation. It comes to a stable state after 40% of time elapsed and that the shrinking of clusters is almost 50–60%. It is because initially clusters are formed quickly to minimize the broadcast message for information exchange. Later the nearby clusters are merged when two CHs hear each other. The clustering algorithm detects some node in the topology that does not belong to any cluster. Such nodes are called as outliers. Outliers are not considered good for carrying out routing. They are the clusters without members. More outliers lead to the degradation of the performance as they cannot get the benefits of clustering. Figs. 13 and 14 show our observation regarding the total outliers formed for varying TSINR and TETT , respectively. It is comprehended that increasing TSINR and TETT , reduces outliers. Again, more are the number of CUs less are the number of outliers, as in this case more CUs will have similar SINR characteristics. The measure of outliers is taken in the initial phase before executing the merging process. However, during merging phase, most of the outliers are merged and very few of them work as gateways. The proposed algorithm suggests to merge two clusters when they come closer. However, if after merging, splitting of clusters are required then it increases cluster formation overhead. So, a delay period is introduced before merging clusters to avoid unpro-

Fig. 14. Impact of TETT on outlier.

ductive merging of clusters (Section 3.2). In Fig. 15, an observation is made to see how the delay_timer influences the cluster merging process. The graph shows that increased delay_timer reduces cluster merging. However, after certain range of the timer value, the merging process gets stabilized. For shorter delay few clusters are merged although they are moving away from each other. However, if delay is more, moving clusters come to a stable state by that time. In that case only those clusters coming really closer to each other get merged.

5.3. Comparative evaluation of the proposed protocol The performance of the proposed protocol is compared with that of the algorithms given in [9] (Combo) and [17] (CLCC) and [18] UNITED. Three parameters for evaluating robustness, (e.g. cluster number, cluster size, and cluster survival time) are considered. Other five parameters are considered for measuring efficiency, (e.g., average route construction time, ratio of common channel (CC), successful route construction ratio, throughput and end to end delay) are examined for these three protocols. For observation of the proposed protocol, the best values of SINR and ETT threshold are

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Fig. 17. Cluster size. Fig. 15. Cluster merging.

Fig. 18. Cluster survival. Fig. 16. Cluster formation.

chosen as stated in Figs. 10–12. The input parameters for Combo, CLCC and UNITED are taken as per [9,17,18] respectively. Total numbers of clusters formed by the four protocols are shown in Fig. 16. Proposed algorithm demonstrates lowest number of clusters compared to other three algorithms. As the proposed protocol takes SINR and ETT threshold to group nodes into clusters, hence more nodes are clubbed together. Therefore, less numbers of clusters are formed. However, other three protocols consider common channels and node degree hence more clusters are formed. The average size of clusters is also shown in Fig. 17 and the figure demonstrates that the proposed algorithm includes highest number of nodes inside a single cluster. Fig. 18 shows the cluster survival time over simulation time and the proposed algorithm shows the best behavior among all three. Since we are adopting a new mechanism to merge clusters by delaying the process to certain interval determined by the DELAY_TIMER, hence the proposed algorithm avoids unnecessary merging and splitting. And hence long survived clusters are noticed under the influence of the proposed protocol. Other three algorithms under consideration, merge clusters when they come closer and therefore, splitting is also required subsequently. Cluster survival is improved under the influence of the proposed algorithm. Although, UNITED [18] has the same pattern as the proposed algorithm over shorter simulation time, it has been

observed that for longer duration of simulation, the proposed algorithm outperforms UNITED [18]. Fig. 19 shows the average cluster construction overhead and demonstrates that the overhead is the lowest for the proposed algorithm as compared to the other three. In the proposed algorithm, node under the coverage of multiple primary nodes avoids message broadcasting and hence, comparatively low overhead is noticed. However, as the number of nodes increases, the overhead also increases due to more number of packet exchanges for cluster formation. Moreover, in low density network, the proposed algorithm and UNITED [18] react in similar way, but in densely populated network the proposed algorithm works better. It is primarily because of the avoidance of unnecessary merging of clusters for the passing by CHs, in the proposed algorithm. This was achieved by the implementation of DELAY_TIMMER as stated above (Fig. 18). Fig. 20 shows the ratio of common channels (CC) over available channels. Our algorithm shows the best performance compared to COMBO and CLCC. This is due to the selection of SINR and ETT threshold for cluster formation. Since UNITED uses the CU interference among them to form clusters, therefore, for low density network more common channels are noticed. The proposed algorithm performs better for dense networks because we are considering PU interference on CU transmissions, in the proposed algorithm. Throughputs of various algorithms are plotted in Fig. 21 and end to end delays are plotted in Fig. 22. Throughput is recorded

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Fig. 22. End to end delay. Fig. 19. Cluster construction overhead.

against varying PU activity and end to end delay is recorded against data rate with PU activity of 35%. Results reveal that throughput decreases with the increase in PU activity for all the protocols. It is obvious, as the increasing PU activity reduces the opportunity for CU nodes. But it is observed that throughput of our proposed algorithm is higher than to all other [9,17,18], due to provision of concurrent communication in the proposed algorithm. Although for low PU activity scenario, proposed algorithm and UNITED show similar throughput level, as the PU activity increases, proposed algorithm outperforms UNITED. The UNITED does not support concurrent communication hence it fails to deliver packets under high PU activity situations. For the same reason UNITED shows better performance while end to end delay (Fig. 22) is considered. End to end delay increases under the influence of the proposed algorithm as it suffers from more channel switching and therefore, subsequent delay for delivery of packets. UNITED, on the other hand, performs less switching of channels compared to the proposed algorithm and hence shows lower delay under heavy PU activity scenario. Fig. 20. Ratio of common channels.

6. Conclusions

Fig. 21. Throughput.

This paper describes a novel approach for cluster formation in CRAHN. The algorithm makes use of SINR produced by the PU on CU and ETT among CUs in order to form clusters. During the formation of the clusters and subsequent transmissions of control messages, it is always tried to avoid disturbing the PU’s ongoing transmissions and minimize the broadcasting of messages. Few most vital parameters are examined for finding the acceptable values of inputs for cluster formation algorithm. The performance of the proposed algorithm in terms of cluster and outlier formation has been evaluated. The maintenance procedure is also examined by varying a timer to observe the need of merging two clusters. A comparative performance analysis of the proposed algorithm with Combo [9], CLCC [17] and UNITED [18] is also carried out for observing robustness and efficiency of the proposed protocol. A medium scale network with up to 200 CUs are considered for various experiments. It is comprehended from the observed results that the proposed algorithm out performs the other three approaches. However, designing optimal algorithm of cluster formation and also for routing in such a network could be an interesting work. In future, we shall consider the issue of

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