## 6 Conclusions and Future Research
The proposed framework can be utilized to achieve high reliability in routing while, at the same time, achieving lower communication costs and latency. The proposed routing framework can be applied practically at the industrial level, as it has been designed to be compatible with the existing OpenFlow protocol. Academicians can use the proposed routing framework for further research in SDVN applications by utilizing the theoretical framework presented to yield better application performance due to better routing. Furthermore, this research opens avenues for applying machine learning to improve the operations of SDVNs by experimentally proving that parameter computation using machine learning yields better performance than algorithmic approaches. However, the proposed routing framework is applicable only in an SDVN paradigm where there is a logically centralized controller for collecting metadata from the network to compute link delay and link lifetime matrices using machine learning required for computing optimum routes using the proposed hybrid routing algorithm. Furthermore, the statistical significance of the results compared to existing routing approaches is lower under low routing frequencies, low network size, and very high and very low link lifetime thresholds. In future work, one may develop the proposed routing algorithm as a machine learning model that is trained to compute routes once all the metadata collected from the vehicular network are provided to the model, such that the whole routing framework will be based on machine learning. Furthermore, the routing framework proposed in this research for SDVN can be extended to its extended paradigm, Knowledge-Defined Vehicular Network (KDVN), as the proposed routing approach uses data-driven routing decisions with the aid of machine learning. Future researchers can further extend the proposed routing framework to consider additional QoS parameters in addition to link delay and link lifetime, such as communication cost of the channels, network congestion, etc., to update the proposedrouting algorithm considering those additional parameters. Not only that, future research can combine the proposed routing framework for a load balancing scenario by integrating it with a machine learning model for network traffic prediction. Furthermore, the proposed routing algorithm can be modified by a future researcher to compute paths that adhere to delay constraints by employing a delay threshold in hybrid routing algorithm, to provide delay-based quality of service-aware routing. Conceptualization--P.A.D.S.N.W. and S.G.; methodology--P.A.D.S.N.W. and S.G.; software--P.A.D.S.N.W. ; validation--P.A.D.S.N.W. ; formal analysis--P.A.D.S.N.W. ; investigation--P.A.D.S.N.W. ; resources--P.A.D.S.N.W. ; data curation--P.A.D.S.N.W; writing--original draft preparation--P.A.D.S.N.W. ; writing--review and editing--S.G.; visualization--P.A.D.S.N.W. ; supervision--S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data and code are available from the authors upon reasonable request after publication. This research is part of a research project that principal and corresponding author [PERSON] is pursuing for a degree at the University of Ruhuna. The authors declare no conflict of interest.