## 7 Conclusions and Future Research  
In this paper, we have reviewed IoV concepts and discussed the need for AO and DML algorithms to address vehicle networking problems in smart cities. IoV is becoming a predominant research topic due to the emergency of self-driving vehicles and the exponential development experimented in IoT and communication technologies, including 5G and newer developments. At the same time, this technology not only opens a wide range of possibilities in the field of urban transportation and mobility, but it also raises a series of optimization problems which are large-scale and NP-hard. In addition, these problems need to be solved in real-time. Moreover, and due to the dynamism of the traffic in any major city, the models need to be re-optimized every few minutes, which calls for the need of AO and DML algorithms.  
Hence, the paper has reviewed the state-of-the-art in IoV main uses, putting special emphasis in protocol requirements and vehicle routing operational challenges. Further, the most frequent optimization tools employed so far to deal with these operational challenges have been discussed. AO algorithms have been introduced next as a combination of constructive heuristics, biased randomization techniques, and parallel computing. Some computational results, gathered from previously published articles, contribute to illustrate the potential of AO algorithms in terms of computing time and quality performance. These results cover some of the main optimization models in the literature of vehicle routing, including: vehicle routing problems, arc routing problems, team orienteering problems, and facility location problems. Distributed/decentralized training algorithms for ML models are discussed in detail. In particular, techniques such as collaborative and federated learning being promising solution methods for IoV problems are presented along with the important benefits and past studies.  
Despite AO and DML approaches can be extremely useful to address vehicle networking problems in smart cities, their practical implementation is not trivial since it requires from the availability of reliable and valuable data on the status of the city transport and mobility systems. In addition, even when we consider AO and DML to be two of the  
Figure 6: A comparison of AO performance for different research topics.  
most relevant methodologies in the context of IoV, the number of challenges related to ISVN is huge, and no single approach can solve all of them. Finally, IoV scenarios might vary depending on each city, and further analytical studies might be required in order to quantify the potential benefits that could be obtained with the proposed methodologies.  
Some future work lines are described next: (i) the design of distributed AO algorithms that can run inside each vehicle instead of in a centralized computer system, and the development of the associated communication protocols in order to guarantee a fluent coordination among the vehicles; (ii) the use of edge computing as an intermediate solution that provides better response times than a centralized approach while, at the same time, facilitates coordination of vehicles at a local scale; (iii) the exploration of other optimization and machine learning methodologies that can constitute an alternative to the use of constructive heuristics and biased randomization techniques.  
Conceptualization, D.T., F.A. and A.A.J.; methodology, D.T., F.A. and A.A.J.; writing--original draft preparation, M.A., E.H., F.A., A.A.J., S.S.S. and D.T. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the Spanish Ministry of Science and Innovation (PID2019-111100 RB-C21/AEI/ 10.13039/501100011033), RF-VOLUTION project (PID2021-122247 OB-I00) and the Barcelona City Council--La Caixa' Foundation (21S09355-001).  
Not applicable.