# An Integrated Cognitive Radio Network for Coastal Smart Cities
## 1 Introduction
NFV solved this problem by providing network services in virtual machines, each performing different operations. Hence, NFV increases the speed of network expansion and network flexibility for the fast delivery of services. A new framework called HomeCloud [7] was proposed in order to focus on open and efficient application delivery to the edge cloud by integrating SDN and NFV. Similarly, [PERSON] et al. [8] proposed an integrated framework that can enable dynamic orchestration of network resources to improve the performance of applications for smart cities. A regional cooperative fog computing-based intelligent vehicular network (CFC-IoV) architecture [9] was proposed for dealing with big data in the internet of vehicles (IoV) in a smart city. Different services for IoV applications were discussed in this architecture, with different resource management hierarchical models. [PERSON] et al. [10] considered FC as an ideal complement to, rather than a substitute for, cloud computing by proposing an SDN-enabled framework for cloud-fog interoperation. The heterogeneous network (HetNet) [11] was proposed in order to integrate heterogeneous satellite-terrestrial networks to improve network elasticity by using SDN and NFV. Collaborative vehicular edge computing (CVEC) [12] is a framework that enables edge computing to collaborate for efficient vehicular networks. It considers multiple edge computing solutions, one of them being fog computing. In the paper cited, the authors proposed the CVEC framework and discussed its architecture, principles, and special cases. Moreover, the authors used NFV and SDN as a potential technical enabler to support the CVEC framework in reality. Similarly, an integrated architecture for software defined and virtualized radio access networks with fog computing was proposed in [13] to enable network-level virtualization. Two hierarchical control levels were adopted to integrate SDN and network virtualization with fog computing. None of these architectures consider cognitive routing as one of the services provided by the NFV infrastructure in a combination of vehicular and maritime networks in a coastal city. Here, we integrated SDN, NFV, and FC to reduce complexities in the existing infrastructure. These complexities have high (unpredictable) latency and put high pressure on the bandwidth due to explosive increases in data traffic, intermittent connectivity in vehicular and marine networks, and inconveniences in applications (that is, no exchange of information among applications). Because an NFV infrastructure supports NFs (network services deployed as virtual functions) across many geographic locations, we therefore took advantage of this notion and implemented it in a combination of vehicular and maritime networks in a coastal city. In this case, two types of applications dynamically received network resources due to the integration. Basically, the purpose of the integration was to route traffic between service functions. NFV provides network services in virtual machines working in cloud infrastructures [6]; therefore, for this proposed architecture, we used FC to preserve latency. In this paper, we present our vision for the design of an integrated coastal smart city. A user is able to operate different applications in parallel, such that a service running in one VN cannot interfere with another [14]. We will show how SDN, NFV, and FC can be integrated in a coastal city. In the following, we consider two different network applications for coastal smart cities. Each VN represents a network. A common network with different environments is virtualized by a hypervisor into several VNs to meet the demands of each application service. The hypervisor is a virtualization controller that allocates and manages the resources (forwarding tables in this paper) over the fog cloud (a local cloud providing resources near the edge users), and launches VNFs dynamically. This means that a large coastal city network is divided into two smaller independent networks.