# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 4 Application
Zones that have medium and low impact respectively have been shown in yellow and blue. Zones that are still at ambient temperature and that have not  
Figure 3: LPWAN sensors and gateway triggered in a timely manner. Triggered sensors are grouped geographically at each time (\(Tn\)), and the LID and timestamp are used to calculate the map of impact. Direction of propagation (Dp) is used to predict the future impact. been affected are shown in white. Isovalue contours (points with the same temperature) could be plotted from sensor data for the creation of a 2D map of parameter values, determination of the disaster propagation front, directions of disaster propagation and the calculation of affected areas. The direction of disaster propagation is perpendicular to the propagation front and is different at each location. The subsequent changing of impact status at each location at \(T_{2}\) is shown in Figure 5. The rate of change of the parameter (temperature) at each sensor location can be shown as  
\[R_{MN}(T_{N})=\frac{P(T_{N})-P(T_{N-1})}{T_{N}-T_{N-1}} \tag{2}\]  
where parameter measurements \(P\) are obtained from a particular sensor at successive moments in time (in our example, \(T_{N}\) = \(T_{2}\) and \(T_{N-1}\) = \(T_{1}\)). The rate of parameter change, \(R\), can be represented in a matrix to reflect values at each sensor as  
\[ \begin{array}{cccc}R_{11}&R_{12}&\ldots&R_{1N}\\ R_{21}&\ldots&\ldots&\ldots\\ \ldots&\ldots&\ldots&\ldots\\ R_{M1}&\ldots&\ldots&R_{MN}\end{array}  \tag{3}\]  
where \(R_{MN}\) are rate of change values for individual sensors. As an example, the following reflects the rate of parameter change at sensor S11. \[R_{11}(T_{2})=\frac{P_{11}(T_{2})-P_{11}(T_{1})}{T_{2}-T_{1}} \tag{4}\]  
Figure 4: An example contour plot of the temperature distribution of the sensor network at time \(T_{1}\). The locations of sensors are overlaid on the plot. Direction of disaster propagation is shown with arrows at selected sensors. Temperature rise at \(T_{3}\) can be predicted at each sensor location. Sensors closer to the fire would be affected to a greater extent and experience a greater rate of temperature change until the maximum temperature is reached or the sensor goes offline [33]. Parameter values at time \(T_{N+1}\) at each sensor can be predicted as  
\[P(T_{N+1})=P(T_{N})+R_{MN}(T_{N})\times(T_{N+1}-T_{N}) \tag{5}\]  
The 2D impact map is then updated to reflect the new parameter values at each sensor and the new impact zones are reallocated accordingly. The predicted value can be compared with the actual value measured at time \(T_{N+1}\), and the rate of change for the next prediction at \(T_{4}\) can be corrected as  
\[P(T_{N+2})=P(T_{N+1})+R_{MNcor}(T_{N+1})\times(T_{N+2}-T_{N+1}) \tag{6}\]  
where  
\[R_{MNcor}(T_{N+1})=R_{MN}(T_{N+1})+\Delta R \tag{7}\]  
\[\Delta R=R_{theor}(T_{N+1})\pm R_{meas}(T_{N+1}) \tag{8}\]  
While this method can be used to predict temperature at a particular sensor, it cannot be applied to sensors that have not yet been affected without some degree of extrapolation. In estimating the future parameter values for these sensors, a 2D vector map of the disaster propagation velocities should be established. As a vector quantity, velocity is characterized by both magnitude as well as direction. For a rectangular array of sensors, the velocity of disaster propagation can be calculated along each of the horizontal (\(x\) axis) and vertical (\(y\) axis) directions as  
\[v_{x}=d_{x}/t \tag{9}\]  
\[v_{y}=d_{y}/t \tag{10}\]  
where \(d_{x}\) and \(d_{y}\) are the distance between the sensors in \(x\) and \(y\) directions, and \(t\) is the time taken for the second sensor to reach the same value as the first.