# Handover Management in 5G Vehicular Networks
## 2 Related Work  
In general, the HO process usually consists of the HO initiation, the network selection, and the HO execution subprocesses. Several HO schemes have been proposed in the research literature supporting the above network management functions. In [24], the authors introduced the necessity of Quality of Service (QoS) and Quality of Experience (QoE) parameters in mobility management algorithms. In this approach, a QoE algorithm for video streaming in 5G vehicular networks (HoVe) is proposed in order to improve the overall handover performance. The Analytic Hierarchy Process (AHP) technique is used to identify the importance of the current metrics including radio resources, vehicle's mobility, QoE. etc. The Structural Similarity (SSIM) is evaluated as a QoE metric estimating the corruption between the original and the transmitted video. The proposed algorithm outperforms the standard LTE RSSI-based algorithm [25] for video services. In [26], the authors proposed an HO triggering scheme based on dynamic criteria. An important aspect of the handover process is the momentum of the trigger event, which initiates the HO. An imprecise trigger could produce service interruptions, data loss, latency, and radio-link failure. The proposed scheme benefits from the IEEE 802.21 Media Independent Handover (MIH) and its services, introducing the link going down event. A dynamic value is calculated to adjust the trigger point during the handover delivery time according to the mobile node's current speed. In [27], the authors presented a machine learning technique to predict the necessity of a handover so as to maintain the users' QoE. The proposed method makes use of a support vector regression algorithm (SVR) to evaluate an HO score periodically per mobile node. The algorithm obtains the signal strength, the throughput, and the previous handover score of the node as the input criteria. The mobile node is triggered to perform the HO to a new network in case its handover cost is above a threshold specified by the system policy. In [28], an MIH-enhanced FPMIPv6 methodology was proposed. In particular, the functionalities of the Evolved Packet Core (EPC) [29] were considered, while a scheme that implements the IEEE 802.11 MIH protocol in the EPC architecture for performing seamless HO was described. Additionally, the use of a transient flag was proposed for eliminating both the ping-pong effect and false HOs by retaining the initial and transient bindings at the LMA. In [30], the Predictive Handover Mechanism for Video streaming in cloud-based urban VANET (PHMVV) method was proposed considering a list of alternative Points of Access (PoAs). The PHMVV takes into account parameters such as the velocity of the vehicle, its distance from each PoA, its moving direction, and the link quality of each PoA. Accordingly, when the vehicle approaches an alternative PoA with better link quality than that of its current PoA, the vehicle performs an HO to it. In [31], the Mobility-Aware Handover scheme for Smart Cities Environment (MAH-SCE) was described. In particular, road traffic conditions were gathered from a Smart City [32] environment using sensors. An HO initiation mechanism initiates an HO considering the road traffic, as well as the SNR that the vehicle perceives from its current PoA. Additionally, in order to reduce the HO rate, Femtocells are not considered as network alternatives when the vehicle's velocity is above 16 m/s. However, it has to be noted that the authors did not provide an adequate explanation about the selection of this specific threshold value. However, an adequate explanation about the selection of this specific threshold value was provided in [31]. Therefore, in our work, the vehicle's velocity was also monitored and a different approach to this task was implemented by considering the operation principles of the LTE MSE technique [22]. In [33], the Mobility-Aware Handover in Ultra-Dense Vehicular Environment (MAH-UDVE) method was proposed.