# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 4 Simulation Results and Discussion
### Verification of Proposed Algorithm
#### 4.2.2 Number of Node Survivor and Network Residual Energy  
The working nodes need to constantly obtain the moving target state, and as the amount of data processing increases gradually, the energy consumption of the working nodes increases continuously, and their residual energy decreases gradually. When the residual energy of the working node is less than 0, it is regarded as the dead state, which leads to the reduction of the number of surviving nodes. The amount of time from network start running to first nodes death is defined as the life cycle of the network. Figure 6 shows the changes of the number of the surviving nodes corresponding to the four algorithms.  
In Figure 6, when the network starts running, there are 100 surviving nodes corresponding to the four algorithms. As the network running time is extended, the energy consumption of the working nodes increases, and the remaining energy of the working nodes will gradually decrease. It can be seen from this figure that when the run-time corresponding to the four algorithms run to 407, 365, 814, and 1142, respectively, the four corresponding curves begin to decline, that is, the death nodes begin to appear in the network. By comparing the time of death of the first node of the four algorithms, it can be seen that the LEACH-MTC algorithm has the longest time to the appearance of the first dead node. Therefore, the LEACH-MTC algorithm can effectively extend the time of the first dead node by constructing an ellipse monitoring area of some working nodes. When the number of running rounds is the same, the curve corresponding to the LEACH-MTC algorithm is above the curve corresponding to the other three algorithms in the whole process, which indicates that the LEACH-MTC algorithm has the largest number of network surviving nodes when the running time is the same. When the network runs to the maximum number of running rounds, i.e., the number of surviving nodes in the network corresponding to the four algorithms is 4, 4, 51, and 87, respectively. In summary, the LEACH-MTC algorithm makes the number of surviving nodes higher than that of other three algorithms; it effectively increases the number of nodes surviving. Accordingly, Figure 7 shows the variation trend of the total residual energy of network nodes with the working time.  
Figure 6: The number of network surviving nodes.  
In Figure 7, the total initial energy of the four algorithms is 100 J. As the network running time increases, the energy curves corresponding to the four algorithms all have a downward trend. When the number of running rounds is the same, the curve of the LEACH-MTC algorithm is obviously above the other three curves, which means that the network residual energy of the algorithm is larger than the other three algorithms. When the network runs to the maximum running rounds, i.e., the remaining energy of the network nodes of the algorithms LEACH, LEACH-DBCH, LEACH-RARE, and LEACH-MTC are 0.0807 J, 0.2345 J, 48.0405 J, and 55.6336 J, respectively. It can be seen that the total residual energy of the LEACH-MTC algorithm is higher than that of the other three algorithms. The simulation results show that the residual network energy corresponding to the four algorithms decreases with the increase of running time, but the residual network energy corresponding to LEACH-MTC algorithm is higher than the other three algorithms, which can save network energy more effectively and prolong the network's life cycle.