## 6 Experimental Results  
This section covers the deployment of the proposed hybrid architecture for monitoring oil pipelines. Here, we have deployed the detection end device on the pipeline for sensing the critical parameters of the oil pipeline, i.e., pressure as shown in Figure 17. In this study, the sampling rate for transmission of the data of oil pipeline between the two-node is preset to 15 s. Moreover, the Zigbee and LoRa transmit data rates at the same baud rate in serial communication. The same has been embedded in the devices during the firmware development. Monitoring end device act as the coordinator node for all the detection end devices. To record the pressure sensor value, we have utilized the cloud and The IoT enable Cayenne application programming interface (API). To obtain Cayenne API and initiate sending and receiving the data we have uploaded the Cayenne MQTT ESP8266 Library in the gateway. Moreover, we have created the Cayenne account through [38]. After creating the account, a new device is created for visualizing the sensor data in the Cayenne dashboard as shown in the figure. The API key of the new device in the Cayenne account is utilized for accessing and visualizing the data from mobile and web applications by individual authority. Figure 17: Experimental setups with devices and gateway. In the experiment, the five detection end devices are deployed on the different points of the pipeline with the 100-m distance between two end devices for continuous sensing the pressure value. To measure the pressure value, the pressure transducer is embedded with detection end devices. As discussed in the architecture section, the detection end devices communicate the sensory information to the monitoring end device through 2.4 GHz-based Zigbee RF communication. As a monitoring end device integrating with 2.4 GHz-based Zigbee RF communication and LoRa communication, it transmits the sensory data to the gateway. The gateway is the amalgamation of two different communication protocols, namely: LoRa communication and ESP 8266 Wi-Fi module. The received RF packets of sensory data are converted into internet protocol (IP) packets for sending to the cloud server through internet connectivity. The experimental setup shown in Figure 17 is implemented in real-time in the pipeline. Figure 18 illustrates the setup of the detection end device, monitoring end device, and safety operation controller. Figure 18a shows the position of the detection end device on the pipeline with a solar-powered; Figure 18b shows the safety operation controller with alarm is positioned at the nearby controller authority based on 2.4 GHz ZigBee communication. Figure 18c presents the position of the flow meter and a pressure sensor for monitoring the flow rate and pressure on the pipeline. Moreover, the gateway is positioned at a location where the internet connectivity is good. From the gateway, the data of the pipeline such as pressure and flow meter are logged in the Cayenne cloud server. Figure 18: Deployment of hardware in a real-time environment. As discussed above, the Cayenne-based cloud is utilized for recording and visualizes the sensory value from the detection end devices. The Figure 19 presents the Cayenne dashboard that visualizes the sensory value that is receiving from the detection end devices. In the Cayenne dashboard, we have denoted detection end devices as \"Node\" and the five detection end devices are represented as Node0, Node1, Node2, Node3, Node4, and Node5. The unit of pressure values that are visualized in the Cayenne dashboard is Kilonewton per square meter (\(\mathrm{kN/m^{2}}\)). Figure 19 of the Cayenne dashboard '1' displays the pressure value of the pipeline from the different detection end devices. Here, the threshold value for the pressure sensor is preset to the 1600 \(\mathrm{kN/m^{2}}\), if the sensor value is going above and below this threshold value reveals that the pressure value needs to be carefully inspected by the authorities.