# Handover Management in 5G Vehicular Networks
## 5 Simulation Setup and Results  
In our simulation, the software-defined vehicular cloud topology presented in Figure 13 was evaluated. A mobility trace designating the map of the city of Athens along with transportation data was generated using the Open Street Map (OSM) software. Afterwards, the mobility trace was used as the input in the Simulator of Urban Mobility (SUMO), establishing a realistic mobility pattern including 77,797 vehicles driving inside Athens within a 24 h period of time. The network topology, as well as a Cloud/Fog infrastructure were created on the map using Network Simulator 3 (NS3) [49]. The Fog infrastructure consists of 28 802.11p WAVE RSUs, 5 LTE-A Pro FD-MIMO Macrocell e-Node Bs (eNBs), 8 LTE-A Macrocell eNBs, 70 LTE-A Femtocells eNBs, 8 ViMAX Macrocell Base Stations (BSs), and 41 WiMAX Femtocell BSs, equipped with additional computational and storage resources. Furthermore, the access networks are located in geographical positions based on the data available from the Hellenic Telecommunications and Post Commission (EETT) [50]. The positions and the spectrum of the base stations are presented in Tables A2-A4 (Appendix A) for the LTE-A Pro, the LTE-A, as well as the WiMAX and the WAVE technologies, respectively. Furthermore, the Fog infrastructure includes an SDN controller that constantly monitors the entire topology by implementing the Mobility Management as a Service (MMaS) model. Additional components including the Fog Manager, Fog Monitor, Network Manager, Network Monitor, Topology Discovery, Joint Resource Provisioner, and Power Consumption Estimator are incorporated into the Fog and are administered by the SDN controller. Specifically, the Fog Manager, as well as the Fog Monitor control the available resources of the topology. At the same time, the Network Manager manipulates OpenFlow forwarding tables, default path settings, and QoS-aware configurations. The Network Monitor provides the status of the switches of the entire infrastructure. Furthermore, the Topology Discovery component stores information about the generated topologies and the available UEs. Moreover, the Joint Resource Provisioner is utilized as a load coordinator for the VMs, the nodes, and the network traffic. Finally, the Power Consumption Estimator identifies the energy-demanding network access elements. The Cloud infrastructure deploys a number of Virtual Machines (VMs) providing both Non-Disaster Management (nDM) and Disaster Management (DM) services. In particular, the nDM services include Navigation Assistance (NAV), Voice over IP (VoIP), Conversa  
Figure 13: The simulated topology. tional Video (CV), Buffered Streaming (BS), and Web Browsing (Web) applications. On the other hand, DM services include Disaster-Aware Navigation Assistance (DA-NAV), Disaster-Aware Voice over IP (DA-VoIP), Live Video Streaming for Emergency Manipulation (LVS-EM), and Image Transmission for Emergency Manipulation (IT-EM) systems. Furthermore, the Cloud infrastructure includes a Software-Defined Cloud (SDC) controller. The SDC controller consists of a Cloud Monitor and a Cloud Manager, which monitor and manipulate the Cloud resources, respectively. Each access network supports at least one of the aforementioned services. Furthermore, three Service Level Agreements (SLAs) are determined. The SLA1 supports the entire service types, while at the same time, it provides the best values for both the QoS and the operator's policy decision criteria. SLA2 does not support CV, BS, and LVS-EM services. In addition, it provides slightly worse values about the criteria in comparison with the ones provided by SLA1. Finally, SLA3 supports only the NAV, web, and DA-NAV services with satisfactory values for the considered criteria. The specifications of the networks are expressed using linguistic terms corresponding to specific value ranges per service type according to [23], whereas Table 1 summarizes the parameters of the simulation.