# Blockchain Solutions for Enhancing Security and Privacy in Industrial IoT
## 5 Experiment and Results
### Performance and Scalability  
Performance metrics highlighted the system's scalability and efficiency. Under normal conditions, the system achieved a throughput of 1200 transactions per second with a latency of 120 ms and an energy consumption of 0.8 kWh/h. During the attack, throughput dropped by 20% to 960 transactions per second, latency increased by 275% to 450 ms, and energy consumption rose to 1.2 kWh per hour. Post attack, the system recovered to 1150 transactions per second (\(-4.2\%\)) with a latency of 180 ms (\(+50\%\)) and energy consumption of 0.9 kWh/h. Edge computing played a critical role, reducing cloud load by 40%, akin to diverting traffic from a congested highway to local roads.  
Additionally, the PoS consensus mechanism consumed 76% less energy than traditional blockchain methods, aligning with sustainability goals. Table 7 compares the system's performance metrics under three scenarios: normal conditions, during the DDoS attack, and after the attack recovery. It quantifies the attack's impact on throughput, latency, energy consumption, and CPU load. Table 8 quantifies the energy savings achieved by adopting the Proof-of-Stake (PoS) consensus mechanism compared with the Proof-of-Work (PoW) mechanism.  
The key achievements included achieving 100% data integrity across 1 million transactions, ensuring reliability and trustworthiness. The system demonstrated rapid threat response, with a Mean Time to Detect (MTTD) of 12 s and a Mean Time to Mitigate (MTTM) of 28 s. The cost savings were significant, with energy consumption reduced by 62% compared with traditional blockchain systems and latency improved by 40% through edge computing. The system enabled predictive maintenance for factories, allowing operators to  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time (s)** &
\begin{tabular}{c} **Network Traffic** \\ **(req/s)** \\ \end{tabular} & **Action Taken** & **Outcome** \\ \hline
0 & 50,000 &
\begin{tabular}{c} Edge nodes detect surges \\ in network traffic. \\ \end{tabular} & Attack identified. \\ \hline
12 & 50,000 &
\begin{tabular}{c} AI flags anomaly; \\ blockchain triggers IP \\ blocking. \\ \end{tabular} & Malicious IPs flagged \\
28 & 2500 (95\% blocked) & \
\begin{tabular}{c} The system was restored \\ to normal operation. \\ \end{tabular} &
\begin{tabular}{c} 95\% of malicious \\ traffic mitigated. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 6: Detailed DDoS attack simulation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scenario** & \
\begin{tabular}{c} **Throughput** \\ **(tv/s)** \\ \end{tabular} & **Latency (ms)** &
\begin{tabular}{c} **Energy Use** \\ **(kWh/h)** \\ \end{tabular} & **CPU Load (\%)** \\ \hline Normal & 1200 & 120 & 0.8 & 50 \\ \hline During Attack & 960 (\(-20\%\)) & 450 (\(+275\%\)) & 1.2 & 78 (Edge Nodes) \\ \hline Post Attack & 1150 (\(-4.2\%\)) & 180 (\(+50\%\)) & 0.9 & 60 (Edge Nodes) \\ \hline \end{tabular}
\end{table}
Table 7: Comparative performance metrics during DDoS attack.  
detect overheating or other issues before machinery failures occurred. For security teams, automated defense mechanisms minimize downtime and operational disruptions.