## 1 Introduction
For wireless link lifetime prediction, we first formulate it as an optimization problem using first principles and then estimate the wireless link lifetime using another DNN, by using the factors affecting wireless link lifetime prediction such as differential displacement, differential velocity, differential acceleration, remainingdistance to maximum wireless transmission distance, etc. We were also the first to use a deep learning approach for wireless link lifetime prediction in SDVNs. Results show that wireless link lifetime prediction using DNN is more computationally efficient than the optimization-based approach while still having high accuracy. Using the DNNs for link lifetime prediction for wireless links and one-hop delay prediction for both communication channels, adjacency matrices for link lifetime, position, and link delay are computed at the controller to feed into the novel hybrid routing algorithm. The algorithm finds either the highest stable shortest path or the highest stable least delay path from a given source node to each and every other node and returns the parent vector containing the routes. The routing algorithm first chooses its mode using the computed normalized network contention and finds routes to all other nodes as destination nodes iteratively. For a given destination node, if the algorithm is in the most stable shortest path mode, it will first select the communication channel for the edge in the position matrix by inspecting only link lifetimes. However, if the algorithm is in the highest stable least delay mode, it will select the communication channel for the edge in the link delay matrix by inspecting the ratio of link delay and link lifetime for the edge (If the ratio is lower, link delay in that channel will be selected). Furthermore, the algorithm inspects the link lifetime against a threshold value before adding an edge to a route, where the edge will be added to the route if the edge's link lifetime is greater than the threshold. We decided on the threshold value experimentally. We proposed the routing framework in order to be compatible with the existing OpenFlow protocol, and we proposed to include an additional field \"Route valid timestamp\" as a statistic in the flow table to track the final timestamp that a matched entry of the flow table is valid for forwarding a packet without contacting the controller for new flow rules. Thus, we effectively prevent packet forwarding on expired paths, which can result in poor packet delivery ratios, by modeling \"Route valid timestamp\" in flow table architecture. We furthermore proposed an algorithm to update the flow table at the controller using the computed routes from the routing algorithm, where the link lifetime of the weakest link of a computed path is added to the present timestamp in setting the route valid timestamp field, while the Node ID and link types of the source node, the destination node, and the next hop are mapped with the topology database to update the flow table. Not only that, we refrain from the computationally inefficient approach of a proactive approach and utilize an adaptive algorithm to compute routes, update the flow table, and install flow rules, which compare the current timestamp with the route valid timestamp of the flow table at the controller upon reception of a packet_in message, which then computes routes and update the table only if the flow table entry is expired and composes a FlowMod packet and send it to the corresponding switch to update flow rules at the switch. The purpose of the adaptive approach for flow rule updating is to effectively reduce the communication overhead associated with control plane communication and minimize the computational burden at the controller for computing routes. Finally, we present a systematic approach for metadata collection, parameter computation, route finding, flow table updating, and routing scheduling.