## 4 Media Access Control
### Cooperative Sensing
#### 4.2.4 Summary of Cooperative Sensing  
At the end of this section, some summarizing discussions are provided for the three important aspects of cooperative sensing discussed so far. First, key facts of the two main topological models for cooperation are presented in Table 3, revealing a number of advantages of clustered architecture over its centralized counterpart. The most important benefit of clustering is that the decentralization of decision making to smaller clusters improves the system flexibility and reliability. Despite different techniques involved, clusters are commonly formed by nodes that are geographically close to each other. Therefore, decisions made locally within clustersbetter reflect the specific situations of their physical environments. It is worth noting that, according to findings in [94], the probability of path failure tends to increase significantly under influences of the potential problem of correlated shadowing, which is the fading experienced in multiple paths caused by the same large obstacle such as concrete pillars indoors or outdoor trees [94, 95]. As a consequence, this problem can negatively affect the cooperation among clusters and hence their overall performance. As a result, careful thoughts on node placement are recommended in environments that are prone to correlated shadowing [95]. The clustered structure also reduces the communication overhead. Since reports from cluster members are confined to their groups, the frequency of sending and forwarding operation is restricted. Distant transmissions are also minimized as cluster members are usually only one or a reasonably small number of hops away from their CHs. These factors together enhance the efficiency and power consumptions, extending the network lifetime. Moreover, the necessity of global communication and report channels is eliminated with clustered sensing network [96]. Instead of using a single controlling frequency throughout the entire network, each cluster is granted the autonomy to individually choose their own channels for exchanging control information and reports [72, 97]. This distributed decision-making mechanism can better accommodate the local spectral situation and greatly improves the network flexibility and robustness. Second, Table 4 generalizes the pros and cons associated with both random and synchronous sensing scheduling. The scheduling for cooperative sensing is not entirely independent from techniques used at PHY. For example, energy detection is only capable of sampling the strength of signals without distinguishing their  
characteristics or sources. As a result, network-wide quiet sensing periods are required for the sensing result to be meaningful. Synchronous scheduling such as TDMA greatly helps ensure the validity at the expense of synchronization maintenance. Randomly arranged sensing does not entail such overhead, but options at PHY layer are limited to relatively more complicated techniques such as cyclostationary feature detection. The choice of the scheduling mechanism also influences the effectiveness of adaptation and ultimately depends on specific applications. For example, devices such as wireless sensors usually conduct synchronous spectrum sensing because their limited computational power can only afford energy detection at PHY. Finally, facts of soft fusion and hard combination are summed up in Table 5. Soft fusion centralizes the power of decision making to CHs by requiring raw or slightly processed (e.g., quantized) sensing data from cooperative nodes. The resultant comprehensiveness of information contributes to the possibility of a finer granularity of control. But the overhead in terms of transmission, storage and computation is inevitably high. Hard combination distributes the workload of formulating spectrum decision to all cooperation participants. Nodes individually compute their local yes-or-no decisions and report them to CHs where an overall judgment is reached.