# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 2 Related Work
The gateways were permanently installed alongside the road. The gateways transmit their presence to 1-hop neighbors using the neighbor discovery protocol (NDP) [29]. As the vehicles receive the gateway advertisement message, they collect the data from it and calculate the gateway index for each gateway. The gateway with the highest gateway index is selected as the servicing gateway. However, because of the high speed of cars, using a fixed gateway, particularly in a VANET situation, increases the number of handovers. Numerous multicriteria decision-making algorithms have been suggested in the literature to improve gateway selection decisions. In [30], the authors suggested a GWS mechanism based on the ViseKriterijumska Optimizacija I Kompromsino Resenje (VIKOR) [31] approach for vertical handover. Mobility, direction, packet cost, number of hops, RSS, location, latency, throughput, packet jitter, and packet loss rate are the parameters used to select the gateways. All of these parameters are fed into VIKOR, which calculates the VIKOR index (Q) for each alternate gateway. The gateway candidate with the lowest Q value is chosen as the best gateway. In sparse vehicular networks, this approach is said to improve the average number of vertical handovers, average latency, and throughput. This method's computing complexity, on the other hand, has not been quantified. The authors of [32] suggested a multicriteria mechanism for GWS, intending to increase intervehicle communications, thus reducing the number of connections to a single gateway. The GWS method is based on the preference ranking organization method for enrichment evaluation (PROMETHEE) [33]. The vehicle speed, vehicle direction, vehicle position, radio communication frequency, and number of vehicles covered by each gateway are among the five parameters used by the GWS method. Each criterion is given a separate weighting based on its significance. Each gateway candidate is assessed based on a pairwise behavior comparison for each of the listed criteria. The gateways are then rated using the PROMETHEE index, with the highest-ranking gateway being selected as the serving gateway. The authors of [34] proposed a multimetric GWS algorithm for VANETs in fading channels. The algorithm considers the fading problem that normally exists due to the presence of buildings and other obstacles. Three criteria are used for the selection of the optimal gateway: correctly decoded probability (CDP), gateway delay time (GDT), and velocity relative to gateway (VTG). As in [15], the SAW technique was applied in [35] to evaluate each GW candidate. The GWs with a higher weight are selected as the serving gateway. However, this work only considered vehicles moving in the same direction, while movements in opposing directions were not taken into account. This results in a frequent occurrence of the handoff mechanism, which normally increases the packet drop and delay, and affects the overall performance of the vehicular network. Table 1 summarizes the related works on gateway selection techniques. The table shows the existing work in tabular form based on the important key parameters and drawbacks observed in each technique. In the existing literature that is relevant, there is not a single scheme that takes into account the load state of the LTE infrastructure as well as the QoS of the applications before carrying out the gateway selection process. All of the proposed schemes in existing works conduct the gateway selection even when there are a small number of cars that would be sufficiently served by a single LTE-eNB. This leads to the inefficient usage of the LTE infrastructure as well as a poor QoS for the desired applications. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Technique** & **GWD** & **GWS** & **Selection** & **Simulator** & **Drawbacks** \\ \hline
[27] & Non-Cluster & Hybrid & Velocity, inter-vehicle distance & Pairwise comparison & Customer &