## 4 Media Access Control
### Cooperative Sensing
#### 4.2.1 Cooperation Architectures  
The objective of cooperative spectrum sensing is to combat individual uncertainty or inaccuracy with collective knowledge [52]. Therefore, how the spectrum sensing results of individual devices are shared bears considerable implication on the performance. There are generally two types of architecture for cooperation: centralized and clustered [20]. The centralized model is built around a singular network-wide authoritative entity called the fusion center (FC) [53, 54, 55, 56, 57, 58, 59, 60]. Nodes under this paradigm carry out spectrum-sensing routines independently and send their individual local results to the FC through a common report channel. Global decisions are formulated via certain decision-making algorithms at FC, which subsequently instructs participating nodes on further actions. Despite the simplicity of this scheme, it suffers from a range of potential shortcomings, including scalability issues and controlling overhead and excessive energy consumption of the FC, particularly for large-scale wireless networks. The clustered approach is considered a more sustainable option [20] and has already been widely adopted for the organization of WSNs because of its advantages over non-clustered, centralized schemes in terms of reliability, robustness, scalability, network lifetime, and so forth [61, 62, 63, 64, 65, 66]. With clustering, the network is segregated into several clusters that consist of subsets of nodes, establishing a hierarchical structure. In each of the clusters, one of the nodes is elected as the cluster-head (CH). Although the technique was introduced to mainly improve the routing of data, it can be easily extended to facilitate sensing cooperation by making the CHs collect sensing data from other members of their cluster and generate local channel decisions in a similar manner to the FC of the centralized model. In the next section, a number of common clustering algorithms are reviewed. Two basic criteria can be employed for network division and CH election, namely node identifiers (IDs) and locations. The ID-based clustering is arguably the most straightforward approach. It simply determines the role of nodes according to their unique identifiers [67], which, in practice, can be extracted from MAC address or regulated manually by administrators. Based on specific settings, the node with the greatest ID value within the communication range is elected the cluster-head. Despite the notable simplicity, the method has some apparent drawbacks in that it does not take into account of the prospective CH's location in the cluster. For example, the node with the highest ID may reside at the edge of the cluster and does not have very good connectivity with nodes at the other end of the area. In that case, the ideal CH would have been those in the center of the cluster. Another potential issue is that the scheme tends to overload a particular set of nodes, since the system is inclined to select those with IDs favored by the election mechanism. Although such a problem can be mitigated by ID rotation, the associated overhead can be an issue [68]. The location-based clustering algorithms, in contrast, elect the nodes with the most neighbors to be CHs [69, 70]. Although the selected CHs are arguably the best connected in the cluster, this approach can still cause premature power drainage if some clusters have considerably more members as an imbalanced network load could quickly exhaust the power of busy CHs and affect the network lifetime. This approach is also vulnerable to the possible scenario of highly mobile network devices, where frequent updates on cluster-head election are necessary since distances between devices are constantly changing. A number of more sophisticated tactics have been designed to address the power consumption problem. The approach in [71] constantly updates the optimal transmission radius of CHs, _i.e._, cluster sizes, using the _Ad Hoc_ network design algorithm (ANDA).