## 3 Proposed Fires Monitoring and Detection Framework
### The Proposed Sensor Deployment Scheme  
There are usually requirements that need to be met when deploying a WSN. Two such requirements are (1) the average distance between neighboring sensor nodes, and (2) deployment pattern (random or a regular pattern). For forest fire monitoring and detection applications, a deployment scheme with features of low and balanced energy consumption, early detection and low channel contention should be proposed. Moreover, the scheme should also consider some other features of the forest. The average distance between neighboring sensor nodes is significant to the performance of a WSN applied in forest fire monitoring and detection. The detection time of temperature at a node increases as the distance between node and fire ignition location increases. Therefore, to reduce the detection time of a forest fire, the average distance between neighboring sensor nodes should be shortened. However, this requires the deployment of more sensor nodes in a certain area and may cause channel collision issues. Hence, this is a tradeoff between reducing the detection time and collision probability. The study of [29] shows that the detection time of a sensor node to forest fire depends also on environmental conditions such as the fuel type of the forest, the slope of the location and wind power. Meanwhile, these environmental factors' effects on forest fires are investigated by the National Fire Danger Rating System (NFDRS) [30]. NFDRS calculates a fire spread component value for a forest and this value indicates the forward spreading rate of a fire. In our proposed system, we propose a new parameter called fire occur degree (FOD), the value of which in a forest represents the probability of a fire taking place at a particular location. Furthermore, we consider some other parameters that may affect the decision of the average distance between neighboring sensor nodes such as the required maximum detection time (DT), the initial energy resource (E) and the required network lifetime (LT). Therefore, we propose the following formula to obtain the average distance (D) between neighboring sensor nodes:  
\[\mathrm{D}=\varepsilon\cdot\frac{\mathrm{E}\cdot\mathrm{DT}}{\mathrm{LT}\cdot \mathrm{FOD}^{2}} \tag{1}\]  
where, \(\varepsilon\) is a normalization factor. 1 FOD calculation
2 Begin
3 Write ('give the value of temperature, humidity, rain, wind, season and solar radiation');
4 Read (temperature, 'T');
5 Read (humidity, 'H');
6 Read (rain, 'R');
7 Read (wind, 'W');
8 Read (season, 'S');
9 Read (solar radiation, 'SR');
10 FOD (T, H, R, W, S, SR) // calculate the fire occur degree
11 if (FOD \(\geq\) 0 and FOD \(<\) 10)then
12 Write ('the danger level is low');
13 if (FOD \(\geq\) 10 and FOD \(<\) 20)then
14 Write ('the danger level is medium');
15 if (FOD \(\geq\) 20 and FOD \(<\) 30)then
16 Write ('the danger level is high');
17 if (FOD \(\geq\) 30 and FOD \(<\) 50)then
18 Write ('the danger level is extreme');
19 endif
20 endif
21 endif
22 endif
23 endif
```

**Algorithm 1**The pseudo-code of the algorithm to calculate FOD. Another important factor that affects the performance of WSN-based forest fire monitoring and detection is the deployment pattern of sensor nodes. Two general deployment schemes can be considered: (1) random deployment, and (2) regular deployment. In regular deployment, nodes are deployed based on a regular pattern and the distances between neighboring nodes are nearly equal. Therefore, this has the benefit of balanced energy consumption among sensor nodes in the overall networks. In the case of random deployment, nodes are deployed randomly, hence the distance between each neighboring node is a random value. In such a case, since the nodes are not uniformly distributed, some sensor nodes which must transmit longer distances may consume much more energy and therefore will run out of energy earlier. Even through regular deployment is more preferential compared to random deployment as it can guarantee the overall network performance, in this paper, we adopt random deployment.