## 4 Gs-Eera
### Algorithm Procedure
The reason is that GS-EERA introduces a reception state adjustment mechanism, and thereby nodes can dynamically adjust packet reception according to the remaining buffer size and energy state (i.e., easing the congestion problem). Due to the mechanism, the performance of packet delivery ratio, energy consumption and end-to-end delay is

Figure 13: The impact of data buffer capacity on (**a**) average energy consumption, (**b**) energy variance, (**c**) average end-to-end delay, (**d**) packet delivery ratio and (**e**) average hop count. also improved. Take the packet delivery ratio as an example, GS-EERA outperforms the EHPRP, EHR and SAGREH by about 34.41, 24.73 and 16.12%, respectively. EHR achieves good performance in energy consumption, energy variance and end-to-end delay. EHPRP achieves better performance in energy variance and average hop count, while worse in energy consumption and packet delivery ratio. SAGREH achieves acceptable performance in the energy consumption and packet delivery ratio but performs poorly in energy variance and end-to-end delay (the reasons have been explained in Section 5.3.1). #### 5.3.5 The Impact of Packets Generation Rate

In this simulation group, we set the number of nodes \(n=300\), initial energy \(E_{0}=15\) J, simulation time \(T_{s}=600\) s, data buffer capacity \(B_{max}=40\) packets and the packets generation rate \(PGR\) varies in 1-5 packets/s. Figure 14 shows the impact of \(PGR\) on average energy consumption, energy variance, average end-to-end delay, packet delivery ratio and average hop count. Overall, as the packets generation rate \(PGR\) increases, energy consumption, end-to-end delay and hop count increase and packet delivery ratio decreases (not absolutely). When \(PGR\) increases, there are more packets in the buffer of nodes, which intensify the packet transmission frequency. Congestion probability would increase if the frequency is too high, which decreases the network performance. In contrast, if \(PGR\) is lower, the generated packets can be processed in time and congestion probability is decreased. The energy variance is affected by multiple factors. Thus, it has an unclear change rule. For GS-EERA, it still achieves the optimal performance in energy consumption, end-to-end delay, packet delivery ratio and hop count and acceptable performance in energy variance. That is because the introduced reception state adjustment mechanism can reduce the probability of congestion occurrence and enhance the efficiency of packet transmission. Take the average hop count as an example, GS-EERA is 22.85% less than EHPRP, 51.35% less than EHR and 17.18% less than SAGREH, respectively. EHR performs better in energy consumption, energy variance and end-to-end delay, while worse in packet delivery ratio and hop count. EHPRP achieves better performance in energy variance and average hop count, while underperforming in energy consumption and packet delivery ratio. SAGREH achieves acceptable performance in the energy consumption and packet delivery ratio but performs worse in energy variance and end-to-end delay (the reasons have been explained in Section 5.3.1). Figure 14: The impact of packets generation rate on (**a**) average energy consumption, (**b**) energy variance, (**c**) average end-to-end delay, (**d**) packet delivery ratio and (**e**) average hop count. #### 5.3.6 The Performance Change with Time

In this simulation group, we set the energy harvesting time from 18 to 24 h (Figure 3), in which energy input from the photovoltaic energy harvester falls down drastically, to observe the performance change of the routing algorithms. We set the number of nodes \(n=300\), initial energy \(E_{0}=15\) J, packets generation rate \(PGR=2\) packets/s and data buffer capacity \(B_{max}=40\) packets. Figure 15 shows the performance of average energy consumption, energy variance, average end-to-end delay, packet delivery ratio and average hop count change with time.