## 2 Literature Review
### Discussion
#### 2.2.1 Sensor Nodes
In the first level, a cluster of neighboring nodes generates an alert using K-means clustering. The second level adopts a fuzzy inference system to evaluate the scale of fire and its direction of propagation. This two-layer system was demonstrated to be more efficient than simple fuzzy logic systems and generated fewer incorrect alarms. These sensor nodes can also be used to predict the likelihood of a future wildfire using algorithms such as the 30-30-30 rule of thumb [34] or the fire weather index (FWI), besides the machine learning or regression methods described above. The 30-30-30 rule states that a temperature above 30 \({}^{\circ}\)C, humidity less than 30% and wind speed above 30 kmph is indicative of extreme fire risk. The FWI is a more elaborate indexing system that predicts fire behavior from moisture content in the organic matter on the forest floor, availability of combustible fuel, fire intensity and rate of fire spread. The fuel moisture content is categorized into three numeric fuel moisture codes, which are determined from temperature, relative humidity, wind and precipitation readings [35]. To analyze and log sensor data at a central node, numerous wireless transmission methods such as long-range radio (LoRa), Bluetooth, Zigbee, XBee, cellular networks, etc., have been explored. Cellular networks provide higher bandwidth and lower latenciesbut are power hungry and extremely complicated to setup in remote areas without prior LTE connectivity. Bluetooth range extends to only a few meters; therefore, it is unsuitable for supporting data transmission in vast networks. LoRa and Zigbee have been the most popular choices for communication, as they can be easily used in remote areas without any cellular connectivity or Wi-Fi. Both are capable of transmitting data over longer ranges compared to Bluetooth, with LoRa surpassing Zigbee's typical transmission range for two devices connected directly. LoRa network architectures are arranged in a star pattern, with a gateway node at the center that must be in line of sight of the sensor nodes to exchange data. In contrast, Zigbee networks that are set up in a mesh pattern can bypass the need for line of sight, as intermediary sensor nodes with Zigbee can be used for relaying data. The Zigbee network can also be set up to be self-healing and re-route the data pathway through healthy nodes if any node in the normal pathway is damaged. Although Zigbee has higher bandwidth than LoRa, LoRa is more suited for outdoor low-power IoT applications that also require longer communication range with fewer nodes and simple encryption methods at a lower cost. Researchers have also proposed direct communication of sensor nodes with geostationary and low orbit satellite networks using a novel collaborative beamforming (CB) technique [36]. In CB, neighboring nodes form a virtual antenna array and transmit the shared data synchronously. Doing so increases the transmission range significantly without the need of a signal booster or gateway, while still operating under low power consumption restrictions on the sensor nodes. In a hybrid approach, unmanned aerial vehicles (UAVs) sweep over the sensor node networks to facilitate data collection from sensor nodes, feasibly solving the transmission issues faced by WSNs spread over large areas, data latency or interruption due to damaged nodes. Such methods have been thoroughly reviewed by [PERSON] et al. [37].