## 4 Gs-Eera
### Algorithm Procedure
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network size & 200 \(\times\) 200 m\({}^{2}\) \\ The number of nodes \((n)\) & 100–300 \\ Simulation time \((T_{s})\) & 600–1800 s \\ Battery capacity \((E_{m})\) & 20 J \\ _Level_ 1 & 5\%\(E_{m}\) \\ _Level_ 2 & 15\%\(E_{m}\) \\ Initial energy \((E_{0})\) & 5–15 J \\ Maximum energy harvesting rate \((EH_{max})\) & 0.3 J/s \\ Data buffer capacity \((B_{max})\) & 20–60 packets \\ Data buffer upper threshold \((B_{upper})\) & 90\%\(B_{max}\) \\ Data buffer lower threshold \((B_{lower})\) & 70\%\(B_{max}\) \\ Data packet size \((L)\) & 512 Bytes \\ Packets generation rate \((PGR)\) & 1–5 packets/s \\ Data transmission rate \((R)\) & 10 kb/s \\ Dissipation energy consumption of hardware \((E_{s})\) & 0.1 J/s \\ Transmission power \((P_{t})\) & 200 mW \\ Weighting coefficient & \(\alpha=\beta=\gamma=\delta=0.25\) \\ Time slot \((t_{slot})\) & 0.1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters setting. (4) Average energy consumption. The ratio of total energy consumption by network to the number of packets successfully received by the destination. (5) Average hop count. In multi-hop networks, the routing hop affects the transmission delay. Therefore, it also reflects network performance to some extent. We measure the performance of routing algorithms from five aspects: (1) the impact of the number of nodes, (2) the impact of initial energy, (3) the impact of simulation time, (4) the impact of buffer capacity, (5) the impact of packets generation rate, (6) the performance change with time and (7) the network recovery time. ### Simulation Result

#### 5.3.1 The Impact of the Number of Nodes

In this simulation group, we set the initial energy \(E_{0}=15\) J, simulation time \(T_{s}=600\) s, packets generation rate \(PGR=2\) packets/s, data buffer capacity \(B_{max}=40\) packets and the number of sensor nodes \(n\) varies in 100-300. Figure 10 shows the impact of \(n\) on average energy consumption, energy variance, average end-to-end delay, packet delivery ratio and average hop count. Overall, as the number of nodes \(n\) increases, the energy consumption and end-to-end delay decrease (not absolutely). The reason is that when \(n\) increases, more optimal relay nodes with higher residual energy and lower transmission delay can be found (through executing the routing strategy), and the void nodes can be reduced. Thus, the increases in \(n\) can ease the congestion to some extent. Meanwhile, due to this reason, packet delivery ratio increases with the increasing \(n\). For the energy variance, due to the negative relation between average energy consumption and \(n\) (nodes harvest the same amount of energy while consuming less), the energy difference between the nodes diminishes. Hence, energy variance is inversely correlated with the number of nodes. It should be explained that EHR always selects the relay node with the maximum energy state level. As a result, \(n\) does not have a significant impact on EHR's energy variance. The number of the hop count is affected not only by \(n\) but also by the detailed routing strategy. Thus, the average hop count does not absolutely change with \(n\). From Figure 10a,c,d, we find that our algorithm, GS-EERA, achieves the optimum performance in average energy consumption, end-to-end delay and packet delivery ratio. Take the energy consumption as the example, GS-EERA outperforms the EHPRP, EHR and SAGREH by about 193.38, 52.86 and 98.93%, respectively. The reasons can be explained as follows: Firstly, our algorithm leverages the reception state adjustment mechanism to

Figure 10: The impact of the number of nodes on (**a**) average energy consumption, (**b**) energy variance, (**c**) average end-to-end delay, (**d**) packet delivery ratio and (**e**) average hop count. alleviate the congestion problem, which can reduce the energy wasting, transmission delay and packets loss.