## 2 Literature Review
### Discussion
#### 2.2.1 Sensor Nodes  
Sensor nodes typically consist of low-power sensors like humidity, temperature and gases to monitor the surrounding area for fire and generate alerts (Figure 3). When multiple sensor nodes are arranged as a network distributed throughout an area of interest and capable of communicating wirelessly, they are referred to as a wireless sensor network (WSN). Figure 3: Basic sequence of operation for the majority of WSNs. Many may not share the exact steps but will follow these core functions. (Images sourced from Creative Commons, following the guidelines on Attribution 3.0 Unported, CC By 3.0 [26]. The only modifications to the images are cropping and resizing). Figure 2: Frequency of papers and articles selected for review from each 5-year span. In Table 1, we have summarized the salient features and capabilities of several such kinds of nodes designed by different research groups. Each node is operated by a microcontroller, typically powered by solar energy and rechargeable batteries for long-term operability. In the event of a fire, the rate of change in environmental variables is considerably different from their normal daily variation. For example, the daily temperature is characterized by a gradual change within a predictable range, whereas the temperature differential during a fire can quickly surpass several hundred degrees Celsius. Several algorithms have been developed to distinguish between wildfire and non-wildfire conditions from sensor data behavior. [PERSON] et al., proposed a low-complexity algorithm using only temperature and humidity [27]. They created two base functions using regression analysis with temperature and humidity as dependent variables, and time as an independent variable. The base functions were modeled to represent the behavior of the dependent variables in a fire event. The data received from the sensor nodes can then be compared with these base functions to determine the occurrence of an actual fire. Other researchers have also proposed more sophisticated algorithms like cellular automata and machine learning to detect wildfire. [PERSON] et al., logged flame, smoke, temperature, humidity and light intensity sensors at a central station, and used a Bayesian classifier to categorize fire events from non-fire events with an accuracy of 97.2% [28]. [PERSON] and [PERSON] fed temperature, humidity and wind speed to a fuzzy logic controller. In the first step, the data was subjected to trapezoidal and triangular fuzzification functions, then compared to presets to infer estimated fire risk levels as Very Low, Low, Medium, High and Very High for each month [29]. [PERSON] and [PERSON] used a similar approach with temperature, smoke, light, humidity and distance as inputs to a five-membership function-based fuzzy logic system to determine the probability of fire. They also studied the dependency of fire on each of these inputs as isolated variables [30]. [PERSON] et al., collected temperature, relative humidity, light intensity and CO level from sensors under different climate conditions and at various times of the day, and then calculated the threshold ratio R\({}_{\text{TH}}\) between current data values and the values recorded 30 s ago. This further strengthened the model with a machine learning algorithm to reduce false alarms. A dataset of 7000 sensor data samples was collected during no-fire and fire conditions to train the algorithm, which was then used to corroborate the presence or absence of fire after threshold analysis [31]. [PERSON] et al., used an artificial neural network (ANN) to identify the phase of combustion in real-time using CO2, air temperature and smoke sensors [32]. Their model considered three combustion phases: no fire, smoldering combustion and flaming combustion, and was able to obtain a consistent accuracy of >82% when multiple sensor input data were considered. [PERSON] et al., implemented a multiple-level alert using a combination of KNN classifier and fuzzy logic [33].