# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 4 Simulation Results and Evaluations
### Simulation Setup  
MATLAB 2017a was used for the simulation. The system on which MATLAB was installed and the simulation processes were conducted had the following properties: CPU: Inter Core i7, memory: 8 GB RAM and operating system: Windows 10 Enterprise-64 bit. The parameters and their values are presented in Table 2. For the AWS method, we assign the same weights to the parameters in the simulation [27; 38], \(\alpha_{1}=\alpha_{2}=\alpha_{3}=0.33\).  
To bring the results closer to the real-world situation and make the theories more practical, it is necessary that the simulation environment for scientific research does not deviate greatly from reality. Many works, such as [13; 20], have been conducted on forest fire modeling and have investigated how they burn. Given the simulation's capabilities, the computational complexity, and the difficulty of predicting the direction of fire, we decided to consider the speed and direction of the wind as being constant. By examining the previous works, we concluded that the fire burning model could be as shown in Figure 7; for instance, the NetLogo group implements forest fire models and confirms that fire spread can be random [47].  
In this work, a forest fire spreads from one direction and expands to the center [47]. To show the validity of the results, in addition to the scenario shown in Figure 7, we performed another simulation (without fire) in Section 4.3. In our random model, the yellow area indicates the non-uniformity of the events and red areas show the burned forest and annihilated nodes. As shown in Figure 7, the forest fire scenario includes different processes: (a) from round 1 to round 6, the simulation shows the normal state of the environment; (b) from round 6 to round 12, there is a non-uniform event such as fire smoke, which is indicated in yellow; (c) from round 12 to round 18, forest fires occur, which are shown in red. Similarly, for the rest of the scenario--(d), (e) and (f)--Figure 7 shows the growth of the forest fire proceeding to rounds 24, 30 and 40 of the simulation, leading to more dead nodes.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**N** & **AN** & **MR** \\ \hline N & Number of nodes & 100 \\ \(m\times m\) & Sensor field region & 100 m \(\times\) 200 m \\ E0 & Initial energy of a nodes & 0.5 (j) \\ K & Number of CHs & 5 \\ DP & Data packet length & 4096 (bits) \\ DA & Energy data aggregation & 5 (nJ) \\ [Xmin, Xmax] & Particle position & [0, 200] \\ [Vmin, Vmax] & Particle velocity & [0, 200] (m/s) \\ [c1 min, c1 max] and [c2 min, c2 max] & Acceleration constants & 2 \\ Eamp & Multipath fading transmitter amplifier energy & 130 (pJ/bit/m2) \\ Eelec & Transmission and receiving energy & 50 (nJ/bit) \\ Efs & Free space transmitter amplifier energy & 10 (pJ/bit/m2) \\ Max Round & Maximum number of rounds & 40 rounds \\ Sw & Swarm size & 15 \\ Iter & Number of iterations & 5 \\ HMCRmin and HMCRmax & Harmonic memory considering rate & 0.8 and 0.9 \\ PARmin and PARmax & Pitch adjustment rate & 0.1 and 0.9 \\ \(\omega_{\text{max}}\) & Maximum values of inertia weight & 0.9 \\ \(\omega_{\text{min}}\) & Minimum values of inertia weight & 0.4 \\ Base Station (BS) & Various sink locations & (0, 100), (100, 100), \\  & & (50, 150), (50, 200) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for simulation.  
Figure 7: Simulation scenarios for forest fire in this work: (**a**) The normal state of the environment; (**b**) Fire smoke changes the state of the environment; (**c**) The fire starts in the forest; (**d–f**)— illustrates the spread of fire until 40 round of simulation.