## 1 Introduction  
Technological advances in microelectronics have led to the widespread applications of wireless sensor networks (WSNs) in a variety of application areas. In general, wireless sensor nodes and many other wireless devices based on Wi-Fi, Zigbee and Bluetooth standards operate in unlicensed spectrum bands such as the Industrial Scientific and Medical (ISM) band which lack tight regulations. This leads to severe congestion in the useable unlicensed spectrum bands and causes harmful interference between the various wireless devices. On the other hand, licensed spectrum bands which are assigned to licensed users known as Primary Users tend to become underutilized due to their fixed spectrum band allocation, as reported in the Federal Communications Commission (FCC) report [1]. This necessitates the need for a paradigm shift from the conventional inefficient spectrum allocation policy to a dynamic and more flexible spectrum access management. Cognitive Radio (CR) is a new paradigm that has the potential to efficiently utilize the unused licensed spectrum bands, also known as spectrum holes, by dynamically allocating the spectrum holes to unlicensed users referred to as Secondary Users (SUs) without any harmful interference with PUs' transmissions. Therefore, the main motivation for CR is dynamic access to temporal and spatial spectrum holes [2]. Spectrum sensing is the main fundamental function of CR for spectrum band exploration to identify spectrum holes and to protect PUs from harmful interference. Two performance metrics namely, probability of detection \(P_{d}\) and probability of false alarm \(P_{f}\) are used to measure the reliability of spectrum sensing techniques for discovering the availability or otherwise of spectrum holes. However, wireless propagation impairments such as multi-path fading, receiver uncertainty, shadowing and interference in wireless channels degrade the performance of PU detection techniques [3]. Cooperative spectrum sensing is a promising approach to overcome these problems [4]. The approach involves coordinating multiple CRs to share their local sensing results and make a collective decision about spectrum hole availability. It also improves the probability of PU detection through exploration of multi-users' sensing diversity. A Fusion Centre (FC) performs decision fusion on the sensing results obtained and makes a global decision on the spectrum holes' status. Although cooperative spectrum sensing yields better sensing performance, it also increases communications overhead, incurs in high energy consumption as well as extra sensing and reporting delays, particularly in large-scale networks such as CR-WSN. These problems can be minimized by logical grouping of multiple SUs to form a cluster. CR-WSN is a network of dispersed wireless sensor nodes embedded with cognitive radio capability which enable them to dynamically access unused licensed spectrum bands for data transmission while performing conventional wireless sensor nodes' tasks [5]. CR-WSN offers several potential benefits to a wide range of applications domains and has been proposed as one of the most promising technologies to address spectrum access and utilization challenges in WSN [6, 7]. For instance, when multiple conventional sensor nodes attempt to simultaneously transmit data through the overcrowded unlicensed spectrum bands, the transmitted packets may not get to the destination due to packet collisions. This not only leads to excessive network power consumption as a result of packet retransmissions, but also increases the probability of packet collisions which significantly affect the communication reliability of the network [8]. Although, cognitive radio sensor nodes can dynamically access multiple unused licensed channels for data transmission in order to mitigate this challenge, the additional task of opportunistic access to unused licensed channels through spectrum sensing incurs a significant energy cost.