## 3 Proposed Method
### Hybrid Protocol-Based Path Selection
\[P_{R}=P_{S}\times PDR \tag{36}\]

\(P_{R}\) denotes the packet received rate; \(P_{S}\) refers to the number of packets sent from the source node; PDR indicates the packet delivery ratio in the network. Figure 9 illustrates the number of vehicles vs. packet received rate. The findings of the comparison reveal that the suggested work outperforms comparable works. The packet received rate in RSBP-RF is 1 k, the packet received rate in fuzzy broadcast is 8 k, and the proposed packet received rate is 12.75 k.

#### 4.2.6 Vehicles Density vs. Transmission Delay

The concept of \"vehicle density vs. transmission delay\" can be performed using the following equation:

\[T_{D}=Propa_{D}+Q_{D}+Pro_{D} \tag{37}\]

Figure 10 illustrates the performance of transmission delay in the network. Figure 4:0 Vehicle density vs. packet delivery ratio (%). Figure 4:1 Number of vehicles vs. packet received. Signal propagation delay \((Propa_{D})\) denotes the time between the transmitter and receiver. It depends on the vehicle distance and light speed. Queuing delay \((Q_{D})\) occurs when packets wait in a queue before transmission. The vehicle density affects network congestion. A vehicle's processing delay \((Pro_{D})\) denotes the time it takes to prepare a packet for transmission. The transmission delay in the fuzzy broadcast is 86 ms, the transmission delay in RSBP-RF is 80 ms, and our proposed transmission delay is 57 ms.

### Research Summary

This section presents a summary of the findings of the experiments, demonstrating the improved performance achieved by the proposed framework. Figure 11 illustrates confidence intervals, the tasks completed as part of the proposed work are listed below: number of vehicles vs. throughput, vehicle density vs. end-to-end delay, vehicle density vs. coverage, vehicle density vs. packet delivery ratio, number of vehicles vs. packet received, and vehicle density vs. transmission delay, all which are described in Figures 5-10. Table 3 shows the numerical analysis of the proposed and existing works, demonstrating the performance metrics. Figure 4:2 Confidence intervals. Figure 4:3 Vehicles density vs. transmission delay (ms). Confidence Interval

The range of values that can be inferred from the sample statistics and is likely to include the value of an unknown population parameter is called a confidence interval; this range can be calculated using the following equation:

Figure 4:4

Sample mean (\(\overline{x}\)) 250, standard deviation (\(S\)) 80, sample size (n) 4, and confidence level 95% (confidence interval is 250 \(\pm\) 78.4 or from 171.6 to 328.4). ## 5 Conclusions

This research aims to utilize the UMBBFS routing protocol to address key challenges in VANETs, focusing on reducing the packet loss, transmission delay, and network complexities through optimal routing and the best forwarder selection. Additionally, our study presents a comprehensive approach to enhance emergency message dissemination in 5G-enabled networks, leveraging advanced communication technologies, efficient fog and cloud resource management, as well as intelligent clustering and path selection techniques, with the primary goal of achieving rapid and reliable emergency communication. Our findings strongly suggest that this approach has significant potential to improve emergency response systems and enhance overall public safety. The proposed supercluster-based UMBBFS routing protocol model was verified with the help of the simulation tools OMNET++ and SUMO. Additionally, an assessment of the current technique was carried out by comparing it with existing approaches. The performance of the proposed approach was addressed using numerical analysis; using this analysis, we demonstrated that our method is superior to all other techniques currently available (in terms of all of the metrics). In future work, we plan to improve the robustness of the protocol against a variety of difficulties, including network segmentation, node failures, and changes in the dynamic topology.