# Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node
## 3 Proposed Methodology
### Algorithm
It was observed that our proposed method could maintain on average a lower delay while the speed of

Figure 6: Packet loss rate versus vehicle density. Figure 7: Handoff jitter versus vehicle density. vehicle increased. The reason behind this improvement is that our proposed algorithm has utilized the backup mobile edge-node that could maintain an intermediate session to backup the directed traffic data of the vehicle performing the handoff procedure; thus transport protocol has not faced an increasing packet loss rate in comparison with the other benchmark methods. On the other hand, Figure 9 demonstrates the handoff delay compared to the velocities of vehicles. Diagrammatically it is shown that an up to 3 msec delay is the same when compared to simple cluster methodology. While the velocity of vehicles increases, delay is reduced when compared to a simple cluster-based method. This improvement was due to the factor introduced to the probabilistic method used: the cluster head would have been highly selected when it held a lower velocity value. In other words, a vehicle that travels with relatively low velocity has a higher chance of being selected as a cluster head. Hence, this factor will lead to a lower handoff delay, as there will be a lower frequency of obtaining a network switching over time. In order to investigate the cost value when a cluster head attempts to leave the existing clustered network, an experiment was conducted with 10 scenarios. Figure 10 shows the obtained average cost factor of our proposed DEBCk algorithm compared across the benchmark methods. We have conducted 10 different simulation scenarios; the first starts with a single intersection and increases

Figure 8: Handoff jitter versus the vehicle speed. Figure 9: Delay in msec versus velocity of vehicles. additively up to 10. The obtained cost was the average data loss during the cluster head forming process. We observed that our proposed method could maintain a relatively low average within no more than 0.6 (60 percent). The reason behind this is that our proposed methodology can maintain a safety net for the handoff process; two nodes are used. The first node being used here is called the head node and the second node is called an edge backup node. It was assumed that in the working of a network system, nodes are further divided by making more groups later on. Both nodes being used here are dynamically selected on the basis of the following three parameters. 1. Storage capacity;
2. Communication range;
3. Energy. A list of candidate backup mobile edge-nodes will be created, and scores will be calculated on the bases of these parameters. When a node is entered in the cluster, its score will be calculated and entered into the candidate backup list. Both cluster head and backup edge nodes have a copy of this list. If the cluster head faces disconnection, the backup mobile edge-node will become the cluster head and a high-scoring backup mobile edge node from the candidate backup list will be selected. Hence, we have observed that when our selected backup node faces disconnection the head node will select a high-scoring backup mobile edge-node from the list. Issues of overhead on the cluster head and packet loss rate are decreased and improved through this methodology compared NEMO and simple cluster methods. ## 5 Conclusions

In this paper, we have reviewed some of the handoff management techniques, their importance, and major issues within handoff process in VANET. Additionally, different technologies and algorithms of handoff management with respect to throughput, latency, and delay in VANET were investigated to point out the gaps and challenges that led us to conduct this study. The proposed technique was founded on cluster handoff exploring the recent concept of mobile edge computing in IoV communication. The backup mobile edge-node was added to upgrade the current cluster-based

Figure 10: Normalized cost of cluster head selection versus the number of intersections per simulation.