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When the moving time is 80 s and the number of moving nodes is 10, energy consumption has the maximum, since the larger number of moving nodes can result in more energy consumption.
Energy consumption increases significantly, when the moving time is about 60 s and the number of moving nodes is about 6, but it drops when the number of moving nodes decreases or increases.
In order to understand the impact of moving time and the number of moving nodes on energy consumption, their relationship is shown in Fig. 5. Figure 5 shows that, on the one hand, when the number of moving nodes is less than 4, energy consumption is small for any moving time, and when the moving time is less than 40 s, energy consumption has similar result.
Compared with Fig. 4, when the number of moving nodes is 7, execution rounds of Algorithm 1 decreases 31.1 %, and execution rounds for 10 moving nodes is less than that of 7 moving nodes.
This is because that, on the one hand, more the number of moving nodes is, more energy consumption is.
Similarly, if the previous state of moving nodes is nonleader, the time complexity of electing new leader after moving is O((1+ logN max N max).
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In Fig. 10, the simulation is done with the number of moving nodes being 1, moving time being 80 s, execution rounds being 100, we consider the performance of the proposed algorithms.
Another is that although energy consumption decreases, with the number of nodes increasing (from 100 to 250), the average roses on energy consumption for the same number of moving nodes are 152.6, 156.5, 110.4, 115.1, and 80.2 %, respectively.
Moreover, it is noticed that all energy consumption is less than those obtained by Algorithm 1, when the number of node is 100, the average roses on energy consumption for different number of moving nodes are 30.4, 43.8, 45.8, 56.2, and 61.6 %, respectively.
Step 5: After that, the neighbour with SNF value 0 is removed, and high-speed moving node is filtered from CN for route reconstruction.
If the moving node is a non-leader node, it needs to update the information of ID and distances with its neighbors, then algorithm restarts to execute the new leader election in the new area.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com