## 8 Routing Preferences in CRSNs  
Although [47] clearly illustrated how the joint route outperforms the discrete route both with spectrum selection in CRSNs, routing in a CRSN comes with its unique challenges:  
* On-demand routing is favored over predetermined routing [48] because the latter is not suitable for the dynamic network scenario created by DSA.  
* The hop count is reduced to prolong network life, which results in new metrics to be considered in the process of designing the routing protocol, such as bandwidth, channel access delay, interference and operating frequency.
* Re-routing is caused by the spectrum handoff.  
From the review above, we come to the conclusion that in any practical solution for routing problems in a CRSN, various issues must be considered. We have attempted to summarily capture these issues in the form of a summarized framework, as shown in Figure 8. Although the issues are not completely generalized, the framework summarily shows the design issues that must be considered.  
Central to this model is the routing manager, which is the main building block and regulates route selection control, route maintenance control, rate control and power control. The routing table is fed with spectrum opportunities of the node and its neighbors. The node battery capacity, the characteristics of each available link and the buffer status of the node are also recorded. Depending on the scheme being implemented, the routing table can be further reduced by setting the criteria for any node that will participate in the transmission. The spectral opportunities are computed and categorized by the neighborhood manager, and only relevant spectrum opportunities that meet the criteria from the application layer are sent after the selection process is optimized _vis-a-vis_ the QoS stipulations. The criteria can be simply formulated into classes of differentiated services that can be categorized based on the delay and link reliability offered by one set of links against that offered by the others. These  
Figure 8: CRSN routing framework.  
classes can be summarized into soft and hard real-time services according to the level required by the network services. Thus, links can be tagged to the various offered services. The learning block is fully aware of all decisions made at specific times for specific conditions of specific services and systematically stores the decisions. The block gradually gains cognition about its environment such that, with time, it makes the necessary decisions without having to engage in a vigorous optimization computation to save energy. Depending on the dynamics of the spectrum environment experienced by the node, it might choose to increase or reduce its learning time space (\(\tau_{\text{learn}}\)). This aspect will, by implication, have a great impact on reducing the quiet periods usually introduced by the sensing activity and will thus give the node more time for data transmission. Hence, this component can bring about enhanced throughput.  
Based on the available information fed to the route manager, it selects a route, manages the route and opportunistically regulates the power of transmission. The performance of each link in terms of transmission interruption due to PU arrival is fed back to the learning block to update the route priority list. Depending on the handoff scheme, whenever a handoff is initiated from a particular link due to PU arrival, it is stored in the memory with other layer information (as explained in Section 3.2.7) readily available to the network layer. All of these instructions are encapsulated in the header and are attached to the packet payload before it is sent to the lower layers.