# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 5 Simulation and Analysis
In Scenario 2, we increase the number of nodes in the same size region, which improves the node density of the whole network. Figure 8 compares the network system lifetime of the three algorithms in Scenario 2 and we can see that the FND of EADEEG algorithm is at 1270 th round, FND of DFLC is at the 1359 th round, while FND of EEDCF is at 1493 rd round. The EADEEG again shows the worst performance due to the same shortages as we discussed in Scenario 1. As for the final round when all the nodes die, the AND of EADEEG is at the 2772 nd round, the AND of DFLC is at 3299 th round, and the AND of EEDCF is at 3712 nd round. EEDCF algorithm still performs the best. According to the results in Figures 7 and 8, we extract the specific number of rounds when some death occurs, such as the first node dies, half nodes dies and all nodes dies, in the two different scenarios, as shown in Tables 4 and 5, to help us make a quantitative comparison and visualize the comparison, we plot those data into line graphs, as seen in Figures 9 and 10, which show the performance gaps between the three algorithms more intuitively. As seen in Table 4, we consider  
Figure 8: Comparison of network lifetime of the three algorithms in Scenario 2 (The vertical axis denotes the number of dead nodes, and the horizontal axis denotes the round number). Figure 7: Comparison of network lifetime of the three algorithms in Scenario 1 (The vertical axis denotes the number of dead nodes, and the horizontal axis denotes the round number). HND metric as the evaluation index, and figure out the result that EEDCF is 11.6% more efficient than EADEEG and 7.7% more efficient than DFLC in Scenario 1. According to Table 5, we figure out that the performance of EEDCF is 21.8% better than EADEEG and 18.5% better than DFLC in Scenario 2. Comparing the experiment results in Scenario 1 and Scenario 2, we can conclude that the proposed EEDCF shows better performance than the other two algorithms, especially with more intensive network node deployment because of the additional consideration of the neighbor nodes' residual energies. Moreover, we can also see from the experimental results that, although considering the factor of distance between each node and BS in CH election can guarantee effective communication between CHs and base station, keeping the communication between each node and BS to get the distance will also increase the entire system energy consumption at the same time. In addition, in the variable and complicated topology in WSNs, it is also very hard for all the nodes to get the accurate distances with BS in real time. The proportion of CH nodes is not very large in the whole networks, and the elected CH node without considering the distance with BS may not be too far away from the BS, it is just a distribution with random probability. Thus, we can figure out that this energy consumption of CH election algorithm based on distances randomness will be smaller than those energy consumptions \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **EADEEG** & **DFLC** & **EEDCF** \\ \hline First Node Dies/Round & 1382 & 1429 & 1505 \\ Teen Node Die/Round & 1519 & 1537 & 1664 \\ Half Node Die/Round & 1943 & 2013 & 2168 \\ All Node Die/Round & 2793 & 3407 & 3783 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Death time of nodes in some specific time points in Scenario 1. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **EADEEG** & **DFLC** & **EEDCF** \\ \hline First Node Die/Round & 1270 & 1359 & 1493 \\ Teen Node Die/Round & 1485 & 1534 & 1679 \\ Half Node Die/Round & 1922 & 1945 & 2341 \\ All Node Die/Round & 2772 & 3299 & 3712 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Death time of nodes in some specific time points in Scenario 2. Figure 9: Number of round corresponding to different proportions of dead nodes of the network in Scenario 1.  
of the algorithm to keep all the nodes communication with BS in every round. In the distributed CH election situation, it does not require grasping all the precise information of the entire network.