**5. Results and Discussion**

Table 1 summarizes the parameters used in the simulation. We evaluated the performance by adjusting the number of nodes in the network, number of clusters, and network size in appropriate ranges. We consider a square network area in an indoor environment, place the HAP at the center of the area, and randomly distribute the sensor nodes. Assuming all of the sensor nodes to be homogeneous, their EH efficiencies (*ζ*) are all equal to 0.8 [21]. The ratio of energy used for transmission (*η*) is set to 0.9 for the sensor nodes and 0.7 for the CH. For ease of exposition, we consider a simple distance-dependent path loss model given by *hi* = *Gd*−*<sup>γ</sup> <sup>i</sup>* and *gij* <sup>=</sup> *Gd*−*<sup>γ</sup> ij* assuming the channel fading effect to be averaged out over the frame and all of the channels to be reciprocal [22–24]. Here, *di* is the distance between the HAP and CH *i*, *dij* is the distance between nodes *i* and *j*, *G* refers to the average power attenuation at a reference distance of 1 m and is set to −30 dB, and *γ* is the path loss exponent, which is set to 2.5 [46]. Moreover, we set the length of the WET slot (*Te*) to 5 s and set the lengths of the SWIPT slot (*Ts*) and WIT slot (*Td*) both equal to 0.1 s, assuming the aggregated sensing data to have the same size as the individual sensing data through proper data fusion [47].


**Table 1.** Parameter Setup.

For performance comparison, we consider the following five schemes:


Figure 3 presents the clustering results of an example case when 300 sensor nodes are deployed randomly and grouped into five clusters in a square network with a width of 30 m. Note that the LEACH protocol is omitted because the CH is randomly selected and continuously changed in LEACH. The size of each node point represents the amount of energy dissipated in the sensor node. The smaller the dot, the less energy is wasted. The results of the K-means algorithm show that the five clusters are geographically well divided. However, there is dissipated energy in most of the nodes because the K-means algorithm does not use SWIPT. Since the non-SWIPT scheme uses the K-means algorithm for clustering, the cluster set is the same, but the selected CH is different. This behavior occurs because the non-SWIPT scheme considers *Ri*, as shown in Equation (6), and eventually selects the CH that provides a higher rate. In this non-SWIPT case, most of the nodes have the dissipated energy distributions similar to those in the K-means algorithm because SWIPT is not used too. Figure 3c,d show the proposed SWIPT-based ENO schemes. There is no significant difference between the PS and TS methods. It is evident that the clustering results obtained using the proposed schemes are different from those generated using the K-means and non-SWIPT schemes. In addition, there is little energy dissipation at the nodes in the clusters other than Cluster 1 in the lower left corner. This is because each node transfers its remaining energy to the CH through SWIPT and the CH uses it to transmit its data. Meanwhile, SWIPT is not used in Cluster 1 because the rate of the CH is higher than the rates of the other sensor nodes (i.e., *Ri* > min*j*{*Rij*}), so Cluster 1 has a dissipated energy distribution similar to that in the non-SWIPT scheme.

**Figure 3.** Clustering results of an example case when *N* = 300, *K* = 5, and *W* = 30 m: (**a**) K-means, (**b**) non-SWIPT, (**c**) ENO with PS, and (**d**) ENO with TS.

Figure 4 depicts the achievable rate and energy dissipated in each cluster of Figure 3. In the case of LEACH, both the achievable rate and energy dissipated are the worst because the CH is selected randomly without considering each link rates (i.e., *Rij* and *Ri*). In the K-means case, the performance is improved because *Rij* is considered for clustering and CH selection. In the non-SWIPT case, the performances are better than in the K-means case because *Ri* is additionally considered for CH selection. In the case of the proposed ENO with PS/TS, the achievable rate is improved more than in the non-SWIPT case because SWIPT is performed while considering both *Rij* and *Ri*, and the energy dissipated in each cluster is close to zero except in Cluster 1. This result occurs because each sensor node in the cluster transfers the remaining energy to the CH using SWIPT. This energy is used by the CH to increase the achievable rate of the cluster.

**Figure 4.** (**a**) Achievable rate and (**b**) energy dissipated in each cluster.

Figure 5 shows the average achievable rate in cluster and the total energy dissipated in network versus the number of clusters (*K*) when the number of nodes in network (*N*) is 300 and the width of the square network (*W*) is 30 m. As *K* increases, the achievable rate increases in all schemes. These increases occur because the distance between nodes decreases on average as *K* increases. The LEACH, K-means, and non-SWIPT schemes show better achievable rates in that order. The proposed ENO with PS and ENO with TS have similar performances and outperform the other conventional schemes. Moreover, as *K* increases, the amount of energy dissipated decreases in all schemes, because the average distance between nodes decreases and the variance of link rates becomes smaller as *K* increases. The three conventional schemes have similar dissipated energy, which is greater than that in the two proposed ENO schemes.

**Figure 5.** (**a**) Average achievable rate in cluster and (**b**) total energy dissipated in network vs. number of clusters (*K*) when *N* = 300 and *W* = 30 m.

Figure 6 shows the average achievable rate in cluster and the total energy dissipated in network versus the number of nodes in network (*N*) when *K* = 10 and *W* = 30 m. As *N* increases, the average achievable rate in cluster does not change much. This lack of variation occurs because the minimum rate that determines the rate of the cluster does not change significantly even if *N* increases. Likewise, the achievable rate improves in the order of LEACH, K-means, and non-SWIPT, and the proposed ENO with PS and TS shows the best performance. On the other hand, as *N* increases, the total energy dissipated increases, because the total energy dissipated is linearly proportional to *N*. Nevertheless, the two proposed ENO schemes show significantly lower dissipated energy levels.

**Figure 6.** (**a**) Average achievable rate in cluster and (**b**) total energy dissipated in network vs. number of nodes in network (*N*) when *K* = 10 and *W* = 30 m.

Figure 7 shows the average achievable rate in cluster and the total energy dissipated in network versus the width of the square network (*W*) when *N* = 300 and *K* = 10. As the network area increases, the average achievable rate decreases because the average distance between nodes increases. On the other hand, the total energy dissipation decreases as the network area increases because the amount of energy harvested in all of the nodes becomes smaller. Likewise, the proposed ENO schemes show better performances than the conventional schemes according to the change in network size.

**Figure 7.** (**a**) Average achievable rate in cluster and (**b**) total energy dissipated in network vs. width in square network (*W*) when *N* = 300 and *K* = 10.

## **6. Conclusions**

In this study, we proposed a novel ENO framework based on SWIPT in a hierarchical WPSN environment. To maximize the achievable rate of sensing data while guaranteeing ENO, new protocols and algorithms related to the frame structure, ENO, SWIPT ratios, clustering, and CH selection were presented. The simulation results showed that the proposed scheme using SWIPT performs much better in terms of the achievable rate and dissipated energy than the conventional schemes, which do not use SWIPT. It is also evident that the effect of SWIPT is greater when the number of clusters is large and the density of nodes is high. Therefore, we expect that the proposed SWIPT-based ENO can be applied to future WSNs using WPT technologies. In further research, we will investigate the distributed operations of the proposed algorithms and apply other multiple access protocols [48] considering access collision instead of the conflict-free TDMA protocol for more practical operation.

**Author Contributions:** H.-H.C. contributed to propose the main idea, derive the simulation results, and write most of the paper. J.-R.L. was responsible for mathematical development, verification, and proofreading of the paper.

**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2019R1A2C4070466 and NRF-2019R1F1A1058587).

**Acknowledgments:** The authors are grateful to the anonymous reviewers for their comments and valuable suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.

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