# V2X-Based Highly Reliable Warning System for Emergency Vehicles
## 5 Analysis and Discussions  
The efficiency of the J2H scheme is evaluated by carrying out extensive simulations on SUMO and NS3. The SUMO simulation tool helps in modeling the mobility of vehicles, and the NS3 tool helps in simulating the network environment such as V2X. The main objective of the J2H approach is to effectively utilize the benefits of V2X communication by minimizing the traveling time of the EVs. We find the fastest route for EV based on the traffic density, which is more efficient than the shortest path algorithm, which can also predict the total travel time to reach the destination. The J2H approach has the time complexity of O(ElogV), where E is the number of edges and V is vertices. Typically, V2V works at \(360^{\circ}\) and when passing an alert to the non-EVs. From the literature, we observed that it is not assured that the alert is only from the vehicle approaching behind it. We overcome this by assigning an identification number to the roads so that the vehicles will travel without confusion. Each road will have a unique ID, and while sending an alert, this ID is also included in the warnings even to the non-EVs. We consider the same source and destination for both models, and the path is determined for the given information. Figure 3 denotes the route given by the shortest path algorithm, and Figure 4 denotes the fastest route given by our proposed algorithm based on traffic density. From Figure 4, we can infer that the selected path has less time delay than the path in Figure 3. From the figures, we can analyze that the waiting time at every traffic signal is less when compared to the existing model because our work does not need any intermediate such as RSU to communicate with a traffic signal and then to customize it. Instead, we use ITS, which itself contains a receiver and transmitter that receives the information about the EV and customizes the traffic signals on its own as a result of the waiting time at every traffic signal reducing gradually. Thus, the EV reaches its destination on time. Thus, J2H works more efficiently than the shortest path algorithm. Figure 4: The shortest path derived from the proposed model. Figure 3: The shortest path derived from the existing model. In traffic customization, our J2H algorithm will work efficiently, as every signal knows which way the EVs will come. To enhance their decision making, while more than one EV comes at the same time, J2H takes the decision based on constraints such as the distance between the vehicle and the signal, the level of emergency, and the distance between the signal and their destination. Thus, customization takes place easily. As a result of our work, the EV can reach its destination without any delay. In order to evaluate the performance of the J2H approach, the extensive simulation results were compared with the OTREM and constant rate algorithm (CRA) approaches. The CRA approach transmits the warning messages at an invariant rate without any consideration of other factors. We simulated the approaches based on two major categories. One has low vehicular density and the other has high vehicular density. Figures 5 and 6 refer to the transmission delay of the warning system when the road has a low vehicular density (\(n=0\) to \(n=100\)) and high vehicular density (\(n=0\) to \(n=500\)). From Figure 5, it is observed that the transmission delay of OTREM and CRA rises gradually until the density of vehicles reaches 30, yet the same for J2H does not have any sharp rising transmission delay. J2H reaches the maximum transmission delay of 0.003 s, when the number of vehicles reaches the maximum of 100. However, OTREM and CRA have 0.008 and 0.02 s, respectively. In the analysis of Figure 6, it is observed that with the increase in traffic density, all the approaches have comparatively higher transmission delay than with lower density.