## 4 Gs-Eera
### Algorithm Procedure
EHPRP achieves better performance in energy variance and average hop count, while performing worse in end-to-end delay and packet delivery ratio. SAGREH achieves acceptable performance in energy consumption, packet delivery ratio but performs badly in energy variance and end-to-end delay (the reasons have been explained in Section 5.3.1). #### 5.3.3 The Impact of Simulation Time

In this simulation group, we set the number of nodes \(n=300\), initial energy \(E_{0}=15\) J, packets generation rate \(PGR=2\) packets/s, data buffer capacity \(B_{max}=40\) packets and simulation time \(T_{s}\) varies in 600-1800 s. Figure 12 shows the impact of \(T_{s}\) on average energy consumption, energy variance, average end-to-end delay, packet delivery ratio and average hop count. Overall, with the simulation time \(T_{s}\) increases, energy consumption, end-to-end delay and hop count increase. The reason is that when \(T_{s}\) increases, the residual energy and buffer size of more nodes reach up to the bound, and thereby they cannot forward packets. Accordingly, the data transmission is potentially interrupted. It should be noted that since EHR, EHPRP and SAGREH do not consider the relationship between energy state and data buffer capacity, the number of nodes which are in the sleep mode with unforwarded packets would increase with \(T_{s}\). Therefore, the end-to-end delay of these three algorithms increases dramatically. In addition, for these reasons, the packet delivery ratio decreases

Figure 12: The impact of simulation time on (**a**) average energy consumption, (**b**) energy variance, (**c**) average end-to-end delay, (**d**) packet delivery ratio and (**e**) average hop count. with the increasing \(T_{s}\). As \(T_{s}\) increases, some nodes may have lower residual energy (at a fixed number of nodes, some nodes are repeatedly used for data transmission), while some nodes are used infrequently but still access the harvested energy. Hence, the energy variance increases. Compared with the other three routing algorithms, GS-EERA still achieves the optimum performance in energy consumption, end-to-end delay, packet delivery ratio and hop count and acceptable performance in energy variance. Take the end-to-end delay as the example, GS-EERA is 334.21% less than EHPRP, 202.32% less than EHR and 365.27% less than SAGREH, respectively. EHR achieves good performance in energy consumption, energy variance and end-to-end delay but performs poorly in packet delivery ratio and average hop count. EHPRP achieves better performance in energy variance and average hop count, while obtaining worse energy consumption and packet delivery ratio. SAGREH achieves acceptable performance in the energy consumption and packet delivery ratio (the reasons have been explained in Section 5.3.1). #### 5.3.4 The Impact of Data Buffer Capacity

In this simulation group, we set the number of nodes \(n=300\), initial energy \(E_{0}=15\) J, packets generation rate \(PGR=2\) packets/s, simulation time \(T_{s}=600\) s and the data buffer capacity \(B_{max}\) varies in 20\(\sim\)60 packets. Figure 13 shows the impact of \(B_{max}\) on average energy consumption, energy variance, average end-to-end delay, packet delivery ratio and average hop count. Overall, as the data buffer capacity \(B_{max}\) increases, the energy consumption, end-to-end delay and hop count decrease, while the packet delivery ratio increases. With the increase in \(B_{max}\), the congestion issue can be effectively alleviated, more sensor nodes can receive data and the routing strategy may find an optimal relay node. As a consequence, the performance of energy consumption, end-to-end delay, packet delivery ratio and hop count are improved. The energy variance is affected by multiple factors. Thus, it does not have a clear 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.