## 7 Simulation Results
Figure 7 shows the total energy consumption change of the SNs in the optimization layer when the computing power of each SN in the optimization layer changes, comparing 'The Optimal Mechanism' with the two mechanisms of 'TAC' and 'TDBS'. The number of SNs in the optimization layer is set to 15. As shown in the figure, with the increase of SN's computing power in the optimization layer, the overhead of 'TAC' increases quadratically, which indicates that although increasing the local computing power of SNs can make them process more computing tasks locally and reduce the delay of calculation, it will also increase a lot of energy consumption. At the same time, the overhead of 'TDBS' remains unchanged, and the overhead of 'The Optimal Mechanism' gradually increases until the computing power increases to 13 M cycles/s. This is because when the computing power of each SN increases to 13 M cycles/s, the optimization layer SN will not choose mode \(c\) and mode \(d\) to transmit data. Further, the overhead of SN selecting mode \(a\) and mode \(b\) does not change with the increase of computing power. At the same time, the simulation results show that keeping the computing power of SN at a relatively appropriate value is beneficial to control the energy consumption of SN. Figure 5: Comparison of total overhead when the number of SNs in the optimization layer is different. Figure 6: The total overhead when the amount of SN data in the optimization layer varies. In Figure 8, we compare adding a payment mechanism (Add-PM) and not adding a payment mechanism (Non-PM), showing that the addition of a payment mechanism effectively balances the energy consumption of SN in the optimization layer and the assistance layer. In particular, we assume that the assisting layer SN uniformly adopts mode \(c\) after receiving the data from the optimization layer SN, and the distance \(l_{j,B}\) from the assisting layer SN j to the BS is randomly set from 600 m to 800 m. For the total energy consumption of optimization layer SN, with the increase of the number of SNs in the optimization layer, the growth of Add-PM is slower than that of Non-PM, which indicates that the addition of a payment mechanism can effectively restrain the excessive dependence of optimization layer SNs on assistance layer SNs, and effectively balance the network energy consumption. In Figure 9, in the assistance layer, when the data size that can be received by each SN is setting limit (setting) or not setting limit (Non-setting), compare the energy consumption variance of the assistance layer SNs (the assistance layer SNs adopt the same method c as above). Among them, compared with the 'Non-setting', the energy consumption variance of SNs in the assisting layer is much smaller in 'Setting', which proves that setting the limit on the received data size can effectively balance the energy consumption among SNs in the assisting layer. At the same time, when the number of SNs in the optimization layer increases to 30, the variance of 'Setting' tends to 0, because all 6 SNs in the assistance layer reach the upper limit of the data size that can be received. Figure 8: Comparison of upper SN energy consumption between Add-PM and Non-PM. Figure 7: The total overhead when the computational power of the optimization layer SN varies.