## 3 Proposed Methodology  
### Overview of the Routing Framework  
Vehicular networks are characterized by dynamic topologies and high mobility. Thus, a given link will cease to exist with the passage of time. Shortest path algorithms, such as Dijkstra, which are proposed for the centralized architecture of SDVNs, find the multihop shortest path from a given source node to a given destination node. However, such shortest path algorithms do not take into account the lifetime of links and routing delay, which are very important parameters in determining the optimum routing path in a heterogeneous network such as a vehicular network consisting of vehicular nodes and RSUs. Consider the network instance given in Figure 4. Note that notations \(T\), \(D\), and \(X\) represent the link lifetime, link delay, and displacement, respectively. The subscripts in order represent source, destination, and link type. Assume that a packet needs to be sent from source node A to destination node G in the vehicular network given in Figure 4. A shortest path algorithm will choose the path ABCG, as that path has a total minimum distance of 200 m from node A to node G. However, note that link BC in the shortest path has a link lifetime of 1 ms, such that at the time of routing the packet, link BC is not available. Thus, the packet originated from node A will not be delivered to destination node G as the link BC is broken at the time of routing. Furthermore, the path ABCG is not the least cumulative delay path either. As that path consists of only wireless links, the cumulative delay is 0.41 ms. A shortest path algorithm such as Dijkstra, which does not take link lifetimes and communication delays into account, will fail to deliver packets due to broken links in the shortest path. On the other hand, if path ABDFG was selected, a packet could be delivered to destination \begin{table}
\begin{tabular}{l l} \hline
**Routing Framework** & **Routing Technique** \\ \hline Multipath routing [54] & Q6 and flow rule space constrained routing using machine learning \\ \hline Trust-based routing [55] & Selects routing paths with highest trust using Deep Q learning \\ \hline Intelligent fuzzy-based routing [56] & Uses reinforcement learning to select most stable routing path \\ \hline Mobility adaptive routing [57] & Routing using Q-table updated by distributed Q learning \\ \hline Hierarchical routing [58] & Routing using optimum sequence of grids using Q learning \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: A vehicular network instance showing link lifetimes, link delays, and link distances. node G, as all the links in the specified path are stable, even though it is not the shortest path (250 m). However, note that, between nodes B and D, there are 2 stable links, each having 10 s link lifetimes for wired and wireless communication channels, as nodes B and D are stationary RSU nodes. When multiple stable links exist between the same source node and destination node among different communication channels, the link with the least delay should be selected. Thus, for the given network instance in Figure 4, wired link BD should be selected, as it has a lower delay (0.01 ms) compared to wireless link delay (0.25 ms). Furthermore, note that the path ABDFG, having BD link as wired, is the most stable and least delayed path available, as that path has a cumulative delay of 0.34 ms. As pointed out, the shortest path may not be the optimum routing path, as the shortest path may not be stable enough to deliver packets or the shortest path may not be the fastest path (path with the least cumulative delay). However, there are instances in which the exact multihop delay cannot be accurately predicted, such as random delays introduced due to contention delays in the network. In such instances, if the link delays are unknown or erroneous due to high contention, either a wireless or wired link between nodes B and D may be selected.