## 6 Experimental Results
The dashboard displays the pressure values of the pipeline that remain constant from the different points. Figure 20 of the Cayenne dashboards '2' displays the pressure value of the pipeline from the different detection end devices. The dashboard displays the pressure values of the pipeline, where it can be an observer that the pressure value of Node0 and Node1 is constant. However, the pressure of the pipeline at three different points is reducing below the threshold value. The pressure value visualizes in the dashboard concludes that the pressure is decreasing drastically in Node2, Node3, and Node4. The pressure value of Node2 was reduced to 1000 \(\mathrm{kN/m^{2}}\), Node3 reduced to 800 \(\mathrm{kN/m^{2}}\) and Node4 reduced to 500 \(\mathrm{kN/m^{2}}\). The pressure value in node 2, node 3, and node 4 indicates that the amount of pressure is low when comparing with the threshold value. Moreover, it indicates that the pressure is low, and it needs to be carefully identified. These results on the cloud server assist the authorities in identifying the point at which the pressure is reducing from the different points. Figure 19: Cayenne dashboard ‘1’. Figure 20: Cayenne dashboard ‘2’. The results are addressed in the Figures 19 and 20 provide the status of the pressure inside the pipeline. In case if the pressure of the pipeline is high i.e., above the threshold level, then the detection end device sends the alerts to trigger the alarm in the safety operation controller for minimizing the damage. Table 4 illustrates the comparison of previous studies with the proposed work. The comparison is done based on certain parameters that are illustrated in Table 4. The proposed study is having designed customized detection end device for monitoring the critical parameters of the oil pipeline such as vibration, flow rate, temperature, humidity, and pressure. Moreover, a monitoring end device acts as the coordinator node for the detection end device. A customized gateway is also developed and implemented. A hybrid architecture based on 2.4 GHz and LoRa communication is implemented in this study for oil pipeline monitoring. OPNET simulator is utilized for performing the simulation of ZigBee. With bit rate, link budget, and receiver sensitivity by varying the CR, BW, and SF. Generally, the implementation of customized sensor nodes and gateway are missing in the previous studies. Additionally, in The IoT's implementation, Zigbee and LoRa communication are utilized separately for oil pipeline monitoring. The simulation of ZigBee for oil pipelines is also not addressed in the previous studies. Moreover, the real-time implementation of IoT-based devices is not implemented as in our study we have implemented with IoT-enabled Cayenne server. Finally, the pressure sensor data is receiving in the Cayenne server. The critical monitoring parameters component is included in the Table 4 for discussing the studies that are monitoring the critical parameters of the pipeline such as temperature, humidity, pressure, flow, vibration and, propagate pulses. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
\begin{tabular}{} \end{tabular} \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of our study with previous studies.