## 6 Simulations and Results
### Evaluation of the Multiple Terminals  
In this subsection, we mainly investigate the performance of the proposed energy-efficient transmission strategies _versus_ the number of terminals and HAPs, _i.e._, a multiple-user, multiple-HAP and single-satellite integrated network. Then, the energy consumption for the various Rician factors of the T-H links, which are led by the distribution of the multiple terminals, is provided. The same settings for the simulations are used as in the previous subsection, except where stated. Figures 5 and 6 show the energy consumption for the direct path, relay paths and adaptive path selection with the various number of terminals at the source and system level, respectively. The locations of the added terminals obey a uniform distribution in the disaster area. The number of terminals used is 1, 10, 100, 200, 500 and 1000. We can observe that the energy consumption of the energy-efficient transmission strategy based on the proposed adaptive algorithm saves energy compared to the other five transmission strategies, and the performance of the T-S link is the worst. This is due to the proposed strategy tending to select the path with the lowest energy consumption during all of the transmission slots. For the T-S link, there is only one channel with Rician fading, serious free-space loss, due to the remote communication distance between the terminal and satellite. The energy consumption is obviously high. We also observe that the performance of all four T-H-S relay links is adjacent, since the four HAPs play a relatively equal role over the disaster area. As the number of terminals is increased, the energy consumption of the proposed adaptive algorithm increases mildly at both the source and system level, which implies that the proposed adaptive algorithm is energy-efficient especially for a multiple-user scenario. Comparing Figure 5 to Figure 6, it can be concluded that the increase in energy consumption at the terminals is less than the overall system energy consumption, which indicates that the proposed adaptive algorithm is efficient for battery-operated remote sensor devices. Figure 5: Energy consumption for the direct path, relay paths and adaptive path selection _versus_ the number of terminals at the source level. Since the elevation angles of different terminals for the T-H relay links are various and obstruction by buildings and other tall objects of \(h_{2}\) is serious, the Rician factors may change over the disaster area. Figures 7 and 8 depict the energy consumption for the direct path, relay paths and adaptive path selection _versus_ the Rician factor of the T-H relay links at the source and system level, respectively. The values of Rician factors for the T-H links are 0 dB, 3 dB, 6 dB, 9 dB, 12 dB and 15 dB, and the value of the Rician factor for the T-S link is maintained at 12 dB. We can observe from Figure 7 that the energy consumption of the proposed adaptive algorithm remains low even with a relatively poor channel, _i.e._, the value of the Rician factor is small. This is due to the sensor devices receiving and processing the LSAs during the transmission slots. An optimal transmission power \(P_{r}^{opt.,i,j}(t)\) via an optimal transmission path \(v_{i}^{opt. }\) can be selected based on Algorithm 1. When the channels are relatively poor, the probability of channels with mild fading is small. However, as long as this channel exists, there will be an opportunity to save energy. Therefore, it also indicates that the proposed adaptive algorithm is more energy efficient, particularly in severe channel conditions. We can observe from Figure 8 that the overall energy consumption of the relay transmission strategies is lower than that of the direct strategy, especially for the proposed adaptive algorithm. As the value of the Rician factor is increased, all of the energy consumption of the relay transmission strategies is reduced, but the energy consumption of the proposed adaptive algorithm is still the lowest.