## 5 Simulations and Results Analysis  
In this section, we make simulations for our proposed MCDM-ECP algorithm to evaluate the performance. To begin with, the weights matrix \(W\) should be settled (see Equations (1)-(5)). In practice, the weights of criteria are different according to the types of disasters. For example, it is essential for performances regarding the lifetime of ECNs in a devastating earthquake, since lots of trapped people need to be rescued as soon as possible without communications, while in a terrorist attack, the latency of ECNs is of great important for the government manipulating situations. There is clearly no influence for the selected weights matrix \(W\) to the final results, so we choose an earthquake scenario to settle the weights matrix \(W\) with:  
\[W=\left[ \begin{array}{cccc}1&2&4&4\\ 0.5&1&0.5&0.5\\ 0.25&2&1&1\\ 0.25&2&1&1\\ \end{array} \right]\]  
From Equations (1)-(4), we can calculate the weights:  
\[W=\left[ \begin{array}{cccc}0.50&0.14&0.18&0.18\\ \end{array} \right]\]In order to make all of the columns of our proposed multi-criteria matrix comparable, we transform our proposed multi-criteria matrix \(A=\left[ \begin{array}{cccc}a_{11}&a_{12}&a_{13}&a_{14}\\ \vdots&\vdots&\vdots&\vdots\\ a_{n1}&a_{n2}&a_{n3}&a_{n4}\end{array} \right]\) by:  
\[b_{ij}=1-\frac{a_{ij}}{\underset{i}{\max}a_{ij}},\,(1\leq i\leq n,1\leq j\leq 4)\]  
So, the linear-transformed matrix \(B=\left[ \begin{array}{cccc}b_{11}&b_{12}&b_{13}&b_{14}\\ \vdots&\vdots&\vdots&\vdots\\ b_{n1}&b_{n2}&b_{n3}&b_{n4}\end{array} \right]\)  
According to Equations (5)-(12), we finally get the preference index of the highest solution to the lowest solution. Table 3 lists the simulations parameters of our research. In order to evaluate the performance, we compare our proposed MCDM-ECP algorithm with other classical protocols through simulations in a set of emergency scenarios with different numbers of nodes. As nodes in ECNs consume energy to communicate with each other, the energy left with nodes critically impacts the efficiency, and thus the lifetime of the network. In our simulations, the emergency energy threshold is set to 10% according to [35] by [PERSON], et al. Figure 4 shows that the number of nodes that are left with more than 10% energy decreases with time. However, more nodes are left with energy above the threshold over time when the total number of nodes is bigger. This is because more nodes contribute to share the energy consumption by transferring the same messages in emergency communication networks. Figure 5 illustrates that the number of nodes left with more than 10% energy is decreasing with time in each protocol. At the beginning of emergency communication (0-2000 s), LEACH performs better than MCDM-ECP and AODV in energy consumption. This is because the cluster head selection process is not carried out frequently under LEACH protocol, since all of the nodes are fully charged at the beginning. After 2000 s, cluster heads selection simply relies on the energy left under LEACH protocol; there may be situations in which the nodes that are one or two hops away from ECVs still have to communicate with cluster heads, which may be far from ECVs. For MCDM-ECP, four criteria are considered to find probable next-hop; thus, it performs better in the long run. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters Descriptions** & **Values** \\ \hline Nodes distributions & 1000 m \(\times\) 1000 m \\ ECV location & 1000 m \(\times\) 630 m \\ Initial energy of nodes & 0.5 J \\ Control message size & 32 bit \\ Data message size & 4000 bit \\ ETX (Energy for transmitting per bit) & 50 \(\times\) 0.000000001 J/bit \\ ERX (Energy for receiving per bit) & 50 \(\times\) 0.000000001 J/bit \\ Efs (Effective power consumption of transmitter per bit) & 10 \(\times\) 0.000000000001 J/bit \\ Emp (Transmitter gain control per bit in space) & 0.0013 \(\times\) 0.000000000001 J/bit \\ EDA (data aggregation energy per bit) & 5 \(\times\) 0.000000001 J/bit \\ \hline \hline \end{tabular}
\end{table}