## 2 Materials and Methods
The system was deployed in the West Europe Azure region. Azure VMs used were Standard B2s instances (2 vCPU, 4 GB RAM). A total of 16 simulated IoT devices were configured using Postman. The telemetry scripts were scheduled with constant intervals of 15 s per device, emulating real-time sensor data trans \begin{table}
\begin{tabular}{c c c c c} \hline
**Postman to IoT Hub** & \begin{tabular}{c} **Event Received \&** \\ **Enqueued From IoT Hub** \\ **to Stream Analytic** \\ \end{tabular} & \
\begin{tabular}{c} **Event Processed** \\ **in SQL Database** \\ \end{tabular} & **Total Delay (s)** &
\begin{tabular}{c} **Total Delay** \\ **(ms)** \\ \end{tabular} \\ \hline
0.141 & 11:34:59.560000 & 11:34:59.7522558 & 0.3332558 & 333.2558 \\ \hline
0.140 & 11:35:37.2810000 & 11:35:37.3325194 & 0.1915194 & 191.5194 \\ \hline
0.140 & 11:36:21.5620000 & 11:36:21.7218104 & 0.2998104 & 299.8104 \\ \hline
0.140 & 11:36:49.2820000 & 11:36:49.3193850 & 0.1773850 & 177.3850 \\ \hline
0.141 & 11:36:59.0000000 & 11:36:59.4567142 & 0.5977142 & 597.7142 \\ \hline
0.141 & 11:37:01.0000000 & 11:37:01.4535031 & 0.5945031 & 594.5031 \\ \hline
0.141 & 11:37:43.0000000 & 11:37:43.6309585 & 0.7719585 & 771.9585 \\ \hline
0.141 & 11:37:50.0000000 & 11:37:50.5196650 & 0.6606650 & 660.6650 \\ \hline
0.140 & 11:38:15.0000000 & 11:38:15.8199687 & 0.9599687 & 959.9687 \\ \hline
0.141 & 11:38:43.0000000 & 11:38:43.5339984 & 0.6749984 & 674.9984 \\ \hline
0.141 & 11:39:11.0000000 & 11:39:11.7082835 & 0.8492835 & 849.2835 \\ \hline
0.141 & 11:39:35.0000000 & 11:39:35.5139984 & 0.6549984 & 654.9984 \\ \hline
0.141 & 11:39:49.0000000 & 11:39:49.6918658 & 0.8328658 & 832.8658 \\ \hline
0.141 & 11:39:59.0000000 & 11:39:59.6926158 & 0.8336158 & 833.6158 \\ \hline
0.141 & 11:39:28.0000000 & 11:39:28.7982800 & 0.9392800 & 939.2800 \\ \hline
0.141 & 11:39:49.0000000 & 11:39:49.5055097 & 0.6465097 & 646.5097 \\ \hline  & & & Average Delay & **626.1457** \\ \hline \end{tabular}
\end{table} 
Table 1: Before the implementation of security measures. mission. Each script sent timestamped messages to the IoT Hub, and responses were logged using Postman monitors. To validate the assumptions underlying the M/M/1 queueing model, we analyzed the interarrival times of telemetry messages. A histogram of these intervals showed a general conformity with an exponential distribution under standard load. We further applied the Kolmogorov-Smirnov and Anderson-Darling statistical tests, both of which confirmed an acceptable fit. However, we acknowledge that IoT traffic may exhibit bursty or time-correlated patterns, particularly under atypical conditions. We have therefore discussed the potential use of more flexible models, such as M/G/1 or G/G/1, in the Conclusion section. To provide a more precise evaluation of the financial impact of the implemented security measures, we referred to Microsoft Azure's publicly available pricing data (July 2024). The Table 3. summarizes estimated monthly costs associated with key security components under different usage conditions. These calculations assume a deployment in the \"West Europe\" Azure region and provide a reproducible reference point for real-world planning. Including cost breakdowns highlights the balance between security investment and operational budgeting. To account for real-world fluctuations in sensor traffic and system load, we conducted a sensitivity analysis by varying the arrival rate (\(\lambda\)) and service rate (\(\mu\)) within realistic bounds. We