# LoRaWAN Network for Fire Monitoring in Rural Environments
## 2 Related Work
The authors proposed a dynamic routing solution and a data communication solution that incorporated energy-efficient and priority-based techniques. The Fire Weather Index (FWI), the energy, the security, and the weight were the parameters evaluated to form the dynamic paths. The client application provided information on environmental parameters such as temperature, humidity, rain, and wind speed. Said parameters were used to calculate the FWI. The areas with high FWI values were colored to mark the location of the fire. When fires were detected, an alert message was forwarded to the officers. Simulations were performed using a Microsoft Framework. The results showed the efficiency and the dependability of their proposal obtaining the same efficacy in detecting fires while reducing the consumed energy and the overhead of the data packets. [PERSON] et al. presented in [15] a WSN-based architecture for fire disaster monitoring in developing countries. Simulations were performed using the OPNET Modeler Ver. 14.0. AODV and DSR were compared for the case of mobile nodes moving at a speed of 10 m/s. The results showed that AODV obtained the minimum delay, 0.2 ms, and the maximum throughput, 1.7 Mbps. The authors concluded that AODV was better for larger areas whereas DSR was better for smaller areas. The authors then proposed a hybrid AODV+DSR mechanism to improve the performance compared to DSR. However, AODV+DSR did not outperform AODV. [PERSON] et al. presented in [16] a mixed WSN for the forest fire monitoring paradigm (FFMP). The mobile nodes served as cluster heads that gathered the information from the static nodes. The cluster nodes fused the gathered data to send it to the base station. The data was then processed to create temperature graphs and to detect the fire. Simulations were performed to ensure the good performance of the proposal. Results showed an increase in the lifetime of the WSN. [PERSON] et al. presented a monitoring system for industrial workers to evaluate their safety and health in real time [17]. Sensors were deployed on IoT wearables that communicated with the closest gateway using LoRaWAN and LPWAN technology. The collected information was stored in distributed locations and processed using blockchain to provide immutability and traceability to the data when sharing it with medical facilities or insurance companies. [PERSON] et al. proposed [18] a water scarcity monitoring system intended for arid and semi-arid places. Humidity sensors and anemometers were incorporated to swarm drones to monitor zones with high concentrations of water. These drones forwarded the data using LoRaWAN to the registry for its storage. Then, the data would be transmitted to the central node to perform predict wind direction using classification and regression techniques. [PERSON] et al. designed [19] a range extender for LoRaWAN industrial IoT. The authors suggested the use of a frame relay approach to avert compromising the highest data rates to higher sensitivity. The prototype was created from commercial hardware. Coverage tests were performed measuring the RSSI and the SNR to ensure the correct performance of the proposal and to demonstrate its effectiveness in increasing the range. Furthermore, the range extender was able to perform with legacy networks of LoRaWAN. Previous works established wireless sensor networks using different communication protocols. LoRa is a new technology that has not been thoroughly tested for this purpose and the available studies on the usage of this technology for fire detection systems do not provide much information on the performance of LoRa in this kind of setting. This paper presents a WSN-based forest fire detection system that uses LoRa to establish long-distance wireless communication. Additionally, the system uses the 30-30-30 rule to determine if there is a high level of fire risk. So our system is able to both predict and detect fires.