## 3 Proposed Method
### Hybrid Protocol-Based Path Selection
Within this ensemble of decision trees, the predictions from all the individual trees are combined to effectively categorize and process the incoming emergency messages:

Figure 3.7

Here, k represents the number of trees, T denotes the space that encompasses all conceivable tree structures, and \(t_{k}\) represents a particular tree within this space, complete with its associated EM scores. \(t_{k}\) is derived by minimizing the objective, as follows:

Figure 3.8where \(Tl\) denotes the training loss function and \(\Omega\) denotes the regularization function, usually defined by Equation (29). Figure 3.9

Here, \(\alpha\) denotes the penalty parameter for the number of EMs and \(\omega\) denotes the weight of the EM. When EM uses a squared error loss function, its loss is calculated as follows:

\[\text{SP}=\{SP_{0},\ SP_{1},\ldots,SP_{n-1}\} \tag{6}\]0

\(t_{k}\) is obtained by fitting the residual r.

\[\text{SP}=\{SP_{0},\ SP_{1},\ldots,SP_{n-1}\} \tag{6}\]1

Therefore, a new tree is obtained by minimizing this objective function. Furthermore, the emergency messages of road event notifications and public works are controlled by ITL, and ambulance alerts and nearby pharmacies are controlled by TMS. Here, the LGBM model has the advantage of having a fast training speed, which is suitable for handling large-scale data, and it also has high classification accuracy. In this way, all emergency messages are quickly disseminated in multiple directions and can manage the traffic in the VANET. ## 4 Experimental Results

The experimental examination of the proposed super-clustering-based UMBBFS routing protocol for the EM dissemination method is shown in this section. The results show that the suggested technique is quite effective. This sub-section includes the simulation setup, comparison analysis, and research summary. ### Simulation Setup

This sub-section explains the simulation setup and environment for the super-clustering-based UMBBFS routing protocol for EM dissemination. This proposed approach experimented with a simulation environment of 2750 m \(\times\) 2250 m. Table 1 presents the system configuration. The proposed work was simulated using the Objective Modular Network (OMNET++) and Simulation of Urban MObility (SUMO) simulation tools, where \(++\) denotes that the OMNET environment used C++ for testing. Table 2 presents the simulation configuration. Figure 4 presents the experimental simulation scenario. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Hardware specifications} & Hard disk & 300 GB \\ \cline{2-3}  & RAM & 4 GB \\ \hline \multirow{4}{*}{Software specifications} & Simulation tools & OMNET++, SUMO \\ \cline{2-3}  & Processor & Intel(R) coreTM i5-4590S \\ \cline{1-1} \cline{2-3}  & \multirow{2}{*}{Processor} & [EMAIL_ADDRESS] GHZ \\ \cline{1-1} \cline{2-3}  & OS & Windows 10 Pro \\ \hline \hline \end{tabular}
\end{table} 
Table 1: System specifications. ### Comparative Analysis

The proposed model is assessed by contrasting it with other existing strategies in terms of throughput, end-to-end delay, coverage, packet delivery ratio, packet received, and transmission delay. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Version of OMNET++ & OMNET++ 4.6 \\ \hline Version of SUMO & SUMO 0.19.0 \\ \hline Number of repetitions & 4 \\ \hline Number of vehicles & 100 \\ \hline Blockchain node & 1 \\ \hline Number of ERSU & 4 \\ \hline Controller & 3 \\ \hline TAS & 4 \\ \hline Log collector & 1 \\ \hline Vehicle acceleration & 3.5 m/s\({}^{2}\) \\ \hline Packet interval & 2 s \\ \hline Generated packet number & 100 \\ \hline Packet size & 512 \\ \hline Number of packets & \textasci{}5000 \\ \hline Data rate & Max 2 kbps \\ \hline Simulation time & 500 s \\ \hline Transmission power & 10 mW \\ \hline Rate of transmission & 18 Mbps \\ \hline Bandwidth & 10 MHz \\ \hline Simulation area & 2750 m \(\times\) 2250 m \\ \hline Software between networks & TraCIDemoRSU11p in omnetpp.ini \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters.