## 4 Gs-Eera
### Algorithm Procedure
In Figure 16c, we set the number of nodes \(n=300\), the data buffer capacity \(B_{max}=40\) packets and packets generation rate \(PGR\) varies in 1-5 packets/s. Figure 16c shows the impact of \(PGR\) on network recovery time. Overall, with the increases in \(PGR\), the network recovery time increases. That is because the higher \(PGR\) would bring out a higher congestion probability, causing the network recovery time to be longer. Figure 16d-f shows the performance of network recovery time when 75% of the nodes' initial energy is 0. The changing trend is similar to Figure 16a-c, and GS-EERA still achieves the optimal performance. The reasons have been explained in Paragraph 2. However, overall, when the number of the node in which the initial energy is 0 adjusts from 50 to 75%, the network recovery time increases. ## 6 Conclusions

In order to improve energy efficiency and address the buffer constraint problem in EH-WSNs, we propose a brand new greedy strategy-based energy-efficient routing protocol,

Figure 16: The network recovery time. GS-EERA. In the system modeling process, we firstly formulate an energy evaluation model to determine the energy state of a node and construct a communication range judgment model to identify the effective transmission area. Next, we introduce a reception state adjustment mechanism. By considering the data buffer state and MAC layer protocol, this mechanism can dynamically adjust the data reception state of nodes. With this, we give the detailed execution process of GS-EERA. In addition, we analyze the correctness and computational complexity of this algorithm. Finally, we conduct the simulation experiment. Compared with EHR, EHPRP and SAGREH, our algorithm obtains the optimal performance in average energy consumption, average end-to-end delay, packet delivery ratio and average hop count and acceptable performance in energy variance. Our algorithm provides a meaningful solution for constructing green communication systems. In future studies, we think the following research directions are valuable: (1) adapting our algorithms to vertically heterogeneous 3D environments, (2) considering the impact of Unmanned Aerial Vehicles on routing design and (3) extending our algorithm to multipath routing. Conceptualization, Y.H. ([PERSON]); Data curation, Y.H. ([PERSON]); Formal analysis, S.H. and Y.H. ([PERSON]); Funding acquisition, S.H. ; Investigation, Y.H. ([PERSON]); Methodology, S.H. and Y.H. ([PERSON]); Project administration, S.H. ; Resources, S.H. and Y.H. ([PERSON]); Software, Y.H. ([PERSON]); Supervision, S.H. ; Validation, S.H. and Y.H. ([PERSON]); Visualization, S.H. and Y.H. ([PERSON]); Writing--original draft, Y.H. ([PERSON]); Writing--review & editing, S.H. and Y.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (No. 61772386), National Key Research and Development Project (No. 2018 YFB1305001) and The Fundamental Research Funds for the Central Universities (No. KJ02072021-0119). Not applicable. Not applicable. Not applicable. The authors would like to thank the editor and anonymous reviewers for helpful comments that have improved the quality of the paper. The authors declare no conflict of interest. ## References

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