# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 4 Results and Discussion  
The 5G-UDN V2X environment simulation was developed using Python 3.10.13 to test and evaluate the effectiveness of the CAP method in selecting the best candidates. It offers a comparison of the probability of similar HO issues between the proposed mechanism and the other two algorithms: the traditional handover and ACAVHD [8]. Table 4 lists the network configuration settings that are considered in the testing procedure. The behavior of vehicle movement is represented by the Random Walk mobility model [30]. The vehicle traverses the network area by randomly changing its direction and velocity at distance intervals. SCs that are not in the direction of the vehicle movement or have a low PI are filtered out and excluded from the final decision handover process. Typical 5G NR configuration parameters are assigned randomly to SCs. Mid-band 5G bands are utilized to support the applications of intelligent transportation systems [31]. The MEC-based end-to-end V2N2V is the modeling component considered for V2X 5G latency [25]. The simulation was developed using Python 3.10.13 and the Numpy, Matplotlib, Pandas, and OpenPyXL libraries. The experiments were conducted in a simulated 100 \(\times\) 100 km\({}^{2}\) network grid, which was subdivided into 25 network sectors. The number of UEs was set at 10 and 20 per sector, respectively, with varying numbers of SCs. The performance of SCs was evaluated by extracting QoS metrics (RSSI, SINR, BER, data rate, delay, and packet loss) had their weights assigned according to the UEs application context (Audio, Video, General, AR, DataOff, Relay). These contexts serve as the decision-making policy used in the TOPSIS process. Each testing scenario allows for the adjustment of the UE numbers, UE movements, and the number of SCs within a sector. The UE mobility functionality considers the UE's ability to move at different angles between the sectors. This displays an imitation of a real V2X environment. Figure 3 shows the application-specific dynamic weight adjustment of each parameter examined for the mechanism testing scenario in accordance with UE traffic communication requirements, ensuring that the total for each connection is equal to one. The performance of the proposed methods is evaluated using standard handover metrics, encompassing the number of handover attempts, successful handovers, the probability of link failure and HO delay, the occurrence of ping-pong HO, and the prevention of unnecessary handovers. Figure 4 shows the evaluation of the proposed and traditional techniques for the probability of handover issues. The test was conducted considering 30 SCs and 20 UEs moving at different speeds. Traditional handover protocols in 5G V2X networks often depend on preset thresholds for characteristics such as SINR or RSSI, which result in less-than-ideal performance under different circumstances. They are unable to adjust their priorities in accordance with the demands of individual applications, which can lead to subpar services for such applications. The CAP mechanism, on the other hand, offers a more sophisticated and adaptable method. It guarantees that the handover decision is ideal for the present context by dynamically modifying the weights of the connection quality factors and using a multi-criteria decision-making process. This improves the overall network performance and user satisfaction. This method works especially well in V2X circumstances where application needs and network conditions can change quickly.