# An Integrated Cognitive Radio Network for Coastal Smart Cities
## 3 Simulation Results and Discussion  
We simulated our proposed scheme in NS-2 to evaluate the network performance for one vehicular source-destination pair and one maritime source-destination pair. The total number moving in the network (\(VS\)) varied between 6 and 24, each with a communication range of 200 m. Vehicles and ships moved at varying speeds up to a maximum of 15 m/s. The LC for the marine network moved at a speed of 10 m/s close to shore to keep it connected with the SDN controller. We divided the spectrum into \(M=5\) bands where any channel could be occupied by a licensed PU. Two PUs were used in the network, each with a communication range of 500 m. These PUs were fixed nodes which considered an exponential on/off activity pattern with a rate parameter of 0.05. There were two RSUs, each with a communications range of 350 m. The fog cloud was a single fixed node that acted as a central controller for the simulations, with \(\beta=0.7\), \(\gamma=0.8\), \(P_{\rm ss}=0\), \(P_{\rm ss^{\prime}}=1\), and \(\eta=0.9\). For generating the marine environment, the path loss model defined in (12) was considered for the simulation. A moderate sea state with a wave height between 1.83 m and 2.29 m [22] was used for the stability between the two environments. These simulation parameters are listed in Table 1.  
As mentioned above in Section 1, the literature has no publicly recognized integrated coastal city combining cognitive vehicular and maritime communications to provide a routing service for different users' demands. Therefore, we first evaluated our proposed scheme for V2V, S2S, and V2S communications in the integrated framework, one by one, and then chose to compare the V2V and S2S communications of our proposed scheme with the hierarchical software-defined VANET (HSDV) [24] routing protocol. To evaluate the impact of our proposed integrated scheme in the respective environments, we only compared V2V and S2S with their respective reference schemes. In HSDV, the controller selects the relay nodes to make stable links from the source to the destination by computing the distance with reference to the destination. On the other hand, our integrated architecture chooses the path by selecting the maximum reward value, hop-by-hop, where the reward value is based on less transmission delay and high connectivity among the neighboring nodes. The second reason to choose HSDV as a reference scheme is the deployment of local controllers. Both our proposed scheme and HSDV consider local controllers to divide the burden of the main controller and to provide the local view of the network to the main controller. Moreover, the vehicular and maritime networks are concerned with topological constraints, and a ship at sea is analogous to a vehicle in traffic; therefore, we evaluated HSDV in the marine environment just for the sake of comparison. For simplicity, we used the fog cloud as a central controller for the proposed scheme, which provides service according to the users' demands by communicating with the corresponding \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline number of vehicles and ships, \(VS\) & between 6 and 24 \\ communication range of each \(VS\) & 200 m \\ speed of \(VS\) & up to 15 m/s \\ speed of marine LC & 10 m/s \\ number of channels, \(M\) & 5 \\ number of PUs & 2 \\ communication range of PU & 500 m \\ rate parameter of exponential on/off activity & 0.05 \\ number of RSUs & 2 \\ communication range of RSUs & 350 m \\ \(\beta\) & 0.7 \\ \(\gamma\) & 0.8 \\ \(\eta\) & 0.9 \\ P\({}_{ss}\) & 0 \\ P\({}_{ss}^{s}\) & 1 \\ wave height & between 1.83 ans 2.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. VNF. Likewise, in our previous work [16], we simulated HSDV with a spectrum-sensing scheme for vehicular networks proposed by [PERSON] et al. [25] to make it cognitive HSDV, and referred to it as the reference scheme for V2V.