# A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks
## 2 Adaptive Hierarchical-Clustering-Based Routing Protocol for EH-WSNs
### HCEH-UC Routing Algorithm
MATLAB is also a commonly used verification platform for the contrast routing algorithm. The distribution of 100 sensor nodes in the 200 m \(\times\) 200 m area is shown in Figure 2. In the practical WSNs, the location of the sensor nodes can be obtained from the deployment of the nodes. Various colors represent different cluster attributions, which will be described in the next section. The initial cluster head could also be appointed or elected. The other simulation parameters are depicted in Table 3. In order to conduct fair comparison experiments, these parameters are consistent with the comparison algorithm [25]. ### Network Lifetime Evaluation

In this section, the proposed HCEH-UC would be verified through comparison with the conventional routing algorithm including LEACH [11], LEACH-C [17], CEEC [31], HUCL [32], SEED [33], and NEHCP [25]. The conventional routing algorithms do not possess an energy-harvesting phase. To obtain the equal and effective contrast experimental results, the HCEH-UC would first be verified without energy harvesting to prove its ability to improve energy efficiency. For different routing algorithms, the data transmission mechanisms are different from each other. Even for the clustering-based routing algorithm, the 100 sensor nodes could be clustered through various methods and lead to different data fusion modes. The cluster results of the proposed environment-adaptive hierarchical clustering algorithm are shown \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Sensor Network Size & 200 m \(\times\) 200 m \\ Nodes Number & 100 \\ Base Station & (100,100) \\ Initial Energy & 0.5 J \\ Data-Packet Size & 4000 bit \\ Packet Header Size & 25 bytes \\ Control Message Size & 50 bytes \\ \(E_{elc}\) & 50 nJ/bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ \(E_{DA}\) & 5 nJ/bit/message \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 2: Node distribution of the WSNs. in Figure 2. Each shape in the figure represents one node-cluster and the solid circle denotes the base station. The results of clustering would directly influence the final results and the data transmission mechanism. Additionally, the cluster formation would also spend some non-negligible energy. Thus, the optimization of the clustering algorithm and the adaptive transformation would be meaningful research. In the verification experiments, the number of data transmission rounds performed when the first node dead (FND), the half node dead (HND), and last node dead (LND) of all routing algorithms are recorded in Figure 3. The corresponding quantitative comparison of the metrics are shown in Table 4. These crucial metrics would be directly compared to evaluate the performance of different routing algorithms. As shown in Figure 3, the conventional routing algorithm LEACH and LEACH-C can prolong the FND index to 452 and 513, respectively. The CEEC, HUCL, and SEED routing protocol can prolong the FND to 1000, 1250, and 1510, respectively. The advanced NEHCP protocol leverages the clustering algorithm and extends the FND to 1756. The HCEH-UC that is proposed in this paper can accomplish the optimized clustering of the WSNs nodes through the environmental-adaptive clustering algorithm, which can reduce the energy consumption inside the cluster and balance the energy consumption of the network. Therefore, the proposed algorithm can prolong the FND rounds to 2535 rounds. Simultaneously, as shown in Table 4, the superior lifetime metrics, including HND and LND, when compared with the conventional protocol, can also verify the proposed HCEH-UC, which means that the proposed routing algorithm can accomplish a more energy-efficient node topology through an environment-adaptive clustering algorithm. Therefore, an optimized data-routing mode could reduce the energy consumption needed for data transmission.