## 6 Performance Evaluation
### Performance Comparison Analysis of Different Algorithms  
In order to verify the effect of the proposed algorithm in prolonging the network lifetime, we compared the ADRMDG with ADRMDG in direct upload (ADRMDG-DU), low-energy adaptive clustering hierarchy with energy replenishment (LEACH-ER), and autonomous load regulation mechanism-based routing (ALRMR). In ADRMDG-DU, each node uploads data directly without adaptive dual-mode routing when the MCGV sojourns at its nearest anchor point, as shown for the  
Figure 4: Impact of \(\alpha\) on average energy consumption (P = 0.3 W). Figure 5: Impact of \(\alpha\) on network lifetime (P = 0.3 W). network on the left of Figure 2. In LEACH-ER, we add the same charging strategy of ADRMDG to LEACH [28] in order to make a relatively fair comparison with traditional clustering routing. ALRMR is an efficient framework of joint wireless energy transfer and multi-hop routing, where the routing strategy is adapted to the charging scheme. The corresponding results are described below. We firstly analyzed the lifetimes of different algorithms as the node number changed. The MCGV needed to charge more nodes as the node number increased, but its maximum power did not change. This means that the charging efficacy of the MCGV was diluted with an increasing number of nodes. Hence, as shown in Figure 6, the network lifetime inevitably declined as the node number increased. Compared with the traditional single sink mode in ALRMR and LEACH-ER, the network lifetime could be extended through mobile data gathering in ADRMDG-DU. In addition, the network lifetime could be further enhanced with the introduction of adaptive dual-mode routing in ADRMDG, as demonstrated in Figure 7.  
Figure 7 shows the corresponding residual energy ranges of different algorithms. The residual energy range is the difference between the maximum and minimum residual energy when the first  
Figure 6: Impact of node number on network lifetime in different algorithms (P = 0.3 W). Figure 7: Impact of node number on residual energy range in different algorithms (P = 0.3 W). dead node appears, and it is used to reflect the energy balance of algorithms. We found that the residual energy ranges of LEACH-ER and ALRMR were very large. This is because there was only a static single sink to collect data in these two algorithms. Fringe nodes have to upload data over a long distance, which means that some nodes have higher energy consumptions due to them relaying too much traffic. Their energy equilibrium is weak, and the network lifetime is small. After introducing mobile data gathering, the load distribution becomes more uniform and the average energy consumption decreases. The residual energy range of ADRMDG-DU decreased obviously, which prolonged the network lifetime. However, the number of anchor points was constrained by the charging efficiency, and some nodes still had relatively higher energy consumption caused by the longer communication distance when directly communicating with the MCGV. Combined with adaptive dual-mode routing, the energy consumption of nodes could be balanced effectively when the MCGV gathered data, and the residual energy range of ADRMDG was the smallest. Thus, its network lifetime was the longest. Finally, we analyzed the charging efficiencies of different algorithms, as shown in Figure 8. It can be seen that the network lifetime could be prolonged significantly as the charging power increased. This is because the effect of energy balance could be enhanced with the increase in charging power. Furthermore, the charging efficiency of ADRMDG was the highest, as demonstrated in Figure 9.  
Figure 8: Impact of charging power on network lifetime in different algorithms (\(N=100\)). Figure 9: Impact of charging power on residual energy range in different algorithms (\(N=100\)). In Figure 9, it can be seen that the residual energy range of LEACH-ER was the highest and could not decrease as the charging power increased.