## 5 Results
### Functional Validation  
The functional validation phase focused on verifying that each scenario described in this work was capable of operating autonomously and generating reliable data flows. In the LoRaWAN + The Things Network scenario, communication between Dragino sensors and The Things Network was tested. The Mikrotik LR8 gateway forwarded sensor data to TTN, where it was made available via MQTT. The experiment validated that \begin{table}
\begin{tabular}{l l l} \hline \hline
**Scenario** & **Protocol** & **Practical Application** \\ \hline LoRaWAN + TTN & LoRaWAN + MQTT & Remote sensing of livestock and environmental conditions (public infrastructure) \\ LoRaWAN + ABP & LoRaWAN + MQTT & Local monitoring of facility access (e.g., barn doors) without cloud dependency \\ WiFi + MQTT & MQTT & Motion detection in livestock housing or machinery rooms \\ WiFi + HTTP & HTTP & Environmental monitoring in greenhouses or storage \\ WiFi + UDP & UDP & Vibration/ water level monitoring in tanks or silos \\ WiFi + CoAP & CoAP & Distance sensing in milking stations or rural facilities \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental scenarios designed. Data packets were transmitted successfully and without loss. * The Things Network dashboard confirmed regular reception of messages. * Node-RED flows subscribed to The Things Network MQTT topics, and parsed and routed the data to InfluxDB and MongoDB. * Grafana dashboards updated in real time. The system maintained stable transmission, with low packet loss. Messages sent by Dragino sensors were successfully forwarded through the Mikrotik gateway to The Things Network and retrieved via MQTT. The integration with Node-RED and the data processing pipeline functioned without error. In the LoRaWAN + ABP scenario, a fully autonomous LoRaWAN network was configured using Activation by Personalization. The Dragino LDS02 sensor sent data directly to a private Dragino LPS8 gateway, which forwarded the data to a local Mosquito MQTT broker. The following elements were verified:  
* The gateway received and published sensor data on the expected topic. * Node-RED correctly decoded the payloads and stored them locally. * No Internet connection was needed at any stage of the experiment. * Local visualization via Grafana worked independently of external services. In this scenario, all devices operated locally, without requiring Internet access. The Dragino LPS8 gateway forwarded packets to the Mosquito MQTT broker and the messages were successfully processed by Node-RED. Payloads were correctly decoded and stored in the local databases. No connectivity interruptions were observed. These two experiments confirmed the feasibility of deploying LoRaWAN in cloud-connected and fully offline rural environments. In addition, in each of the four WiFi scenarios, ESP32 devices were programmed to send periodic sensor data using one of the following protocols: MQTT, HTTP, UDP, or CoAP. In all cases,  
* Devices successfully connected to a local access point. * Sensor data were transmitted at regular intervals and received by the backend. * Node-RED flows were configured to handle data based on the protocol used, and messages were parsed as expected. * InfluxDB and MongoDB stored all records, without loss or corruption. * Grafana dashboards visualized the incoming data in real time. In all four WiFi scenarios, the system demonstrated correct operation of the communication protocol and reliable interaction with the backend services. However, during the CoAP-based experiment, minor limitations were observed due to the CoAP libraries available for the Arduino ecosystem. In particular, the absence of the CoAP POST method was noted. While basic functionality was achieved, this indicates the need for careful library selection and testing when implementing on embedded platforms. Each scenario demonstrated correct and stable behavior. Moreover, traffic capture using a Raspberry Pi and Tcpdump confirmed that communication packets were visible on the network and could be collected for further analysis.