# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 3 Proposed Methodology
### Algorithm
The area is again refreshed through the backup mobile edge-node. Support of run down is done through seeking BS by backup mobile edge-node. The delicate signal comes, and connection with a different BS is set up as indicated by the closest area. ## 4 Performance Evaluation

In this section, the simulation result of our work is described. The simulation parameters are shown in Table 2. Network Simulator2 NS-2 [35] simulator was used to implement the proposed method. The simulation running time was 200 s. The plotting of average results evaluates the performance of the system. It is worth mentioning that our proposed DEBCK method was implemented using street map of the city of Erlangen, which was integrated via \"Simulation of Urban Mobility\" SUMO [36] into our simulated scenario of NS-2 simulator. This map was extracted from the open-source \"OpenStreetMap,\" which was widely used by the researchers in deploying their VANET methods [37; 38]. Figure 4 demonstrates the selected city sector that was simulated of Erlangen city. Performance metrics of interest are:

1. Packet Loss: It describes the number of lost packets in the handoff procedure. 2. Handoff jitter: The handoff jitter is calculated during the handoff time. 3. Delay: Time elapsed during handoff procedure. 4. Throughput: It describes the average rate of successful message delivery to a destination with NEMO and cluster-based methodologies. The proposed work is compared to them. Figure 4: Simulated section of Erlangen city using SUMO. The results clearly describe that cluster-based handoff with backup mobile edge-node works better. Figure 5 shows that for 30 and 60 vehicles, the packet loss rates is the same as NEMO and DEBCK. With an increased number of vehicles, the packet loss rate goes on increasing in NEMO and cluster-based methods due to overhead of the cluster head. Seamless hand-off of the cluster head is achieved through the backup mobile edge-node. Communication between the cluster head and other nodes minimizes the packet loss rate, even with hundreds of vehicles in the cluster. In Figure 6, simulations of cluster-based and DEBCK are shown. Diagrammatically it is shown that for 10 vehicles, throughout is probably the same. With a cluster expansion and an increment in the number of vehicles, throughput decreases. Throughput is gradually minimized in cluster-based technology, whereas in DEBCK throughput did not decreased as much as cluster-based. \begin{table}
\begin{tabular}{c c} \hline Network size & 1000 m \(\times\) 1000 m \\ \hline Maximum Number of vehicles in the cluster & 100 \\ \hline Speed of vehicle & 0–100 km/h \\ \hline Size of Packet & 300 bytes \\ \hline Time of Simulation & 200 s \\ \hline Rate of packets & 90 packets/s \\ \hline Transmission range of WIFI & 300 m \\ \hline Transmission range of 3G or LTE & 1000 m \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation setup. Figure 5: Packets loss rate versus vehicle speed. In Figure 7 the proposed DEBCK algorithm was benchmarked with its representatives from the related work in-terms of delay measuring metric with respect to vehicle velocity. It was observed that our proposed algorithm could maintain on average a lower delay while the velocity was increasing. The reason behind the improvement is that our proposed algorithm has utilized the introduced backup mobile edge-node that could maintain an intermediate session to backup the directed traffic data to the vehicle that performed the handoff procedure; thus transport protocol did not face increasing packet loss. When the connection is maintained with a smaller number of retransmissions, this will reflect directly on the delay produced on data communication and on the handoff latency. Another winning factor from the proposed concept of the backup mobile edge-node was having more resources dedicated to such a node in support of the handoff process. Figure 8 shows the performance measure of our proposed DEBCK algorithm along with the other methods with regard to handoff jitter variations as the speed of the vehicle increases.