## 1 Introduction
This means that CR-WSN inevitably consumes much more energy than conventional WSN due to the cognitive capability. Generally, cognitive radio sensor nodes are characterized by limited energy, constrained storage and processing resources, which are inherited from conventional wireless sensor nodes. Therefore, the main challenges in CR-WSNs are energy efficient communications to extend the lifetime of the network and PU protection from unlawful interference. Network clustering involves partitioning the network into logical groups of nodes that form clusters, each cluster comprises of a clusterhead (CH) while the none clusterhead nodes are referred to as Member Nodes (MNs). The CH may serve as a central point to all nodes in the cluster, and it performs various tasks such as data aggregation and spectrum sensing coordination. In addition, it also provides inter-cluster communications by communicating with neighboring CHs and a Base Station (BS). The MN detects events and communicates its data to the associated CH through intra-cluster communications through either single-hop or multi-hop routing. The network clustering process generally involves three phases: initialization, setup and maintenance phases, which gives the main distinction among the various clustering algorithms. The initialization phase can either be centralized or distributed. The setup phase involves emergence of CHs based on either pre-defined metric functions or random selection, it also involves formation of MNs in the cluster where each MN joins its respective cluster either by default or based on some metric function. Finally, the maintenance phase deals with rotation of nodes' roles and re-clustering of the network when a pre-defined condition is reached or at the beginning of every round. Clustering of a network has several benefits and it has been widely explored in conventional wireless sensor networks; they are firstly to achieve network scalability [9], and at the same time prolong the lifetime of a network [8]. However, its application in CR-WSN to enhance PU protection has not been fully explored [10]. Existing clustering algorithms mainly focus on routing [11] and energy consumption issues in conventional WSNs [12], and only a few have attempted to address both energy consumption and spectrum sensing performance issues in CR-WSN. Therefore, conventional clustering algorithms for WSNs or mobile _ad hoc_ networks may not be suitable for CR-WSN due to the dynamic nature of the channels. This necessitates the need for a novel clustering algorithm that will address both energy issues and spectrum holes detection issues in CR-WSN. Network clustering to support many cognitive radio tasks such as dynamic channels access, cooperative sensing and routing has been extensively discussed in [13]. Cognitive radio-based network clustering requires additional conditions for grouping of nodes based on common vacant channels detected in a temporal and spatial neighborhood [14]. In such a network, sensor nodes within a cluster are require to have at least one common vacant channel between the transmitter and the receiver for communication. The dynamic nature of the environment which is influenced by the PU activities necessitates the need for spectrum aware clustering schemes. Spectrum-aware clustering schemes in cognitive radio networks has received considerable attention in recent times. Network scalability and heterogeneity challenges have been well investigated and addressed such as in [15]. The scheme is based on distributed coordination approach where SUs construct groups in accordance with common vacant channels locally detected. A spectrum-aware routing solution for cognitive radio described in [16] selects routes that offer the highest spectrum availability and computes its long-term routing metrics to balance between short-term route performance and long term route satiiability. The scheme mainly addresses channelization and dynamic variation issues in cognitive radio routing protocol to effectively utilize unused licensed channels.