# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
## 5 Result and Discussion
### Performance Evaluation of the Proposed Architecture
From the empirical results, we analyzed that there is a higher divergence in the log loss score of the LR and SVM classifiers. Here, XGBoost's log loss score is small; therefore, it performed well compared to the other ML classifiers. Additionally, Figure 3c depicts the validation curve of the XGBoost classifier that indicates the sensitivity between changes in a classifier's accuracy and changes in some parameters of the classifier. Here, if the training score is more than the validation score, the estimator is overfitting, and otherwise performs well. Moreover, we want to mention that most of the literature we observed does not amalgamate the mentioned key enabler technologies, i.e., blockchain, 6G network, AI, etc. Their approach is based on a single technology, but by using that, there must be a \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline XGBoost & 87.93 & 85.45 & 80.32 & 83.23 \\ \hline RF & 80.63 & 79.34 & 76.21 & 74.43 \\ \hline KNN & 72.06 & 71.21 & 69.32 & 70.21 \\ \hline LR & 59.68 & 55.43 & 51.22 & 52.10 \\ \hline SVM & 55.87 & 52.32 & 50.32 & 51.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of ML classifiers’ performance for different metrics. Figure 3: Performance evaluation of ML classifiers. (**a**) Accuracy. (**b**) Log_loss score. (**c**) Validation curve. loophole that exists that the attackers can exploit. Therefore, an amalgamation of technology is needed to offer a secure pipeline for data exchange for critical applications, such as public safety applications. The work of [63] is similar to our proposed work with better accuracies; however, their work is based on an image dataset where they used rigorous data augmentation techniques and deep learning approaches to acquire better accuracies. Because of data augmentation and deep learning approaches, their proposed model is highly complex and does not have a commendable system response time. This is because deep learning techniques have a higher training time, due to which the response becomes higher; hence, one could not send critical data on time to the application layer. This can also be seen in Figure 4a, where performance overhead is compared between [63] and the proposed work. It is clear from the graph that due to the high training time, the work of [63] has a high-performance overhead compared to our work. Contrary, the proposed work uses text data where minimal data preprocessing steps are required to train ML classifiers. This can be seen in Figure 4b, where training time is computed for all ML classifiers. XGBoost is outperforming other classifiers by attaining a training time of 280.97 s. In [63], the authors used 1400 iterations to obtain a better training accuracy, which takes longer training time compared to our work. Figure 5 illustrates the blockchain scalability comparison between the proposed blockchain with IPFS and the traditional blockchain. Generally, an IPFS-based blockchain is a decentralized protocol and is open to the public; therefore, anyone who has content hash can access the network. Moreover, it offers persistent data storage, fault tolerance against a single point of failure, and a censorship resistance mechanism. Due to the aforesaid benefits, the IPFS-based blockchain has a better response time than the traditional blockchain (i.e., not using IPFS). In addition, the immutable nature of the IPFS-based blockchain eases numerous cybersecurity threats. It has no overheads of the consensus protocols to add the data and only stores the content hash in the network. Thus, the consensus and data aggregation with transformation must be applied before submitting a block or file to IPFS storage. Due to this mechanism, the IPFS-based blockchain is faster and more scalable than the conventional blockchain network. Furthermore, the proposed architecture consists of UAVs for data accumulation and dissemination between different participants of public safety applications.