## 4 Proposal
### Proposed Algorithm
#### 4.2.2 Modified Dijkstra Algorithm (MD)
Figure 19:9 Behavior for Fairness and Congestion. **(a)** Fairness obtained in a state k; **(b)** Avg. congestion obtained in a state k.

\[efficiency_{v}=\frac{number\ of\ moves}{number\ of\ moves+waits} \tag{8}\]0 Best value comparison for efficiency and trip distance. **(a)** Percentage comparison against the best value obtained for efficiency; **(b)** Percentage comparison against the best value obtained for trip distance. \[efficiency_{v}=\frac{number\ of\ moves}{number\ of\ moves+waits} \tag{8}\]1 Behavior for route efficiency and trip distance. **(a)** Mean efficiency value for each 50-vehicles in a 100 vehicles sample; **(b)** Mean trip distance value for each 50-vehicles in a 100 vehicles sample. As we can see, for a simulation in which both the source and destination of each vehicle, and the flow of vehicles entering the network in a step of the simulation is random, the results of control scenarios are replicated. * It is appreciated that for GC, efficiency is more stable with the best results among the other implementations. Nevertheless, we can see again that the GC implementation that provides better efficiency is one of the implementation that has a greater distance, and the distance to DT usually better. * Additionally, the average congestion in the simulation was not very big and the fairness values for implementations are very similar, however, GC has a better Fairness when the levels of congestion are no over the limit. * For the Normal implementation, efficiency values are low (up to 60%-70% worse compared with the best value) and distance is not the best, so this implementation really does not offer any advantage over the others. ## 7 Conclusions

The Internet of Things allows the integration of elements in such a way that old problems can be addressed in new ways, especially for cases where the need of dealing with a constant changing environment becomes a priority. It was proved that the vehicular congestion problem, where it becomes a challenge provide a mechanism capable of dealing with the high dynamism of this parameter is constantly altering the global state of a vehicular network can be supported by this new paradigm. A routing mechanism based on communication V2I in which nodes able to interact with vehicles was developed and compared against other approaches, namely, a heuristic approach based on shortest path distance, an approach aware of local parameters only, and an approach in which the decision regarding the route did not rely in any supporting communication. The results shown prove that: (1) the decision should not only be aware of the congestion state and act in a reactive manner to it, but should be oriented to achieving a lower congestion in every moment; (2) the information about local state of congestion is not enough for achieving an optimal efficiency and better results in a vehicle's trip, so it is necessary to communicate the global information to every element in such a way a proper decision is made; and (3) the lack of any supporting mechanism in search of the best route always leads to worse results. The proposed solution had the best performance for both efficiency and fairness, in scenarios with different characteristics. Therefore, it is a more robust solution and applied in a real context it is likely to generate a benefit in the route taken by a vehicle. In addition, the relation shown between fairness and efficiency and the higher results of the former obtained by the proposed solution imply that vehicles are using the vehicular infrastructure in a more efficiently way. That's why in the future work is the development a V2I and V2V communication mechanism that allows a global optimization of the congestion not for individual trips of the vehicles, but for the whole vehicular network. ## Acknowledgments

This work was performed for the Science in Computer Systems Engineering master program at Universidad de Los Andes, Bogota, Colombia. ## Author Contributions

The algorithm proposal, implementation, and validation were developed by [PERSON].