# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 5 Performance Evaluation
### Clustering Scheme Comparison  
#### 5.3.1 Analysis of Network Density  
The performance of IWLC is examined and compared with the clustering scheme of WSCA and ACO, by varying the network density. We evaluate the impact of the network density on the clustering scheme, by varying the number of UAVs. The maximum speed of UAVs is 30 m/s. Figure 9 compares the PDR of the IWLC with that of the WSCA and ACO. The PDR is defined by  
\[PDR=\frac{P_{R}}{P_{S}} \tag{31}\]  
where \(P_{R}\) represents the number of packets successfully received by the destination nodes, and \(P_{S}\) represents the total number of packets generated by the source nodes. The destination nodes are the CHs and GCS. The UAV nodes in each cluster send data packets to the corresponding CH, and CHs forward the data packets to the GCS. The higher the PDR, the better the communication quality. The UAV nodes' distribution is uneven, and the relative mobility is not regular when the number of nodes is small; thus, the PDR is lower. The PDR of three schemes grows with the increasing number of UAV nodes. The IWLC outperforms the other two schemes with a maximum PDR of around 99.7%, when the number of UAVs increases to 100. The higher number of UAV nodes maximize the probability that transmits packets to the destinations. The reason for this is that the IWLC scheme selects stable CHs to forward packets and adopts an efficient clustering algorithm to connect UAV nodes and GCS. Moreover, the IWLC scheme adopts effective cluster maintenance to avoid network congestion and reduce the probability of link disconnection, which improves the connectivity of UAVs and transmission success ratio. Figure 10 shows a comparison of the number of clusters as the number of UAVs varies. The number of clusters for three schemes is constantly growing with the increasing number of UAV nodes in the network. It is clear that the proposed IWLC forms fewer numbers of clusters than the other two schemes. It is because uniform clustering prevents the existence of clusters with isolated nodes or few numbers of nodes, which optimizes the clustering structure. The number of clusters is fewer than the initial optimal value \(k^{*}\). It could bring an excessive load on the CHs with the increasing number of CMs. However, it can be observed from the IWLC scheme that the growth trend of the number of clusters is more stable, which can better cope with the increasing number of nodes and improve the scalability of the clustering network. In addition, in the cluster maintenance phase, the scheme takes into account the changes and movement of nodes to keep the stability of each cluster in the network. Figure 9: PDR in different clustering schemes with varying numbers of UAV nodes. Figure 11 shows the performance comparison of IWLC with WSCA and ACO in network lifetime, by varying the number of UAVs. The network lifetime of the clustering schemes decreases, owing to the larger number of UAVs changing the mobility and stability of the network topology with increasing network density. The IWLC has the longest network lifetime compared with the other two schemes, due to its comprehensive CH selection algorithm and cluster maintenance. The proposed scheme fully considers the remaining energy and relative mobility of UAVs, and the weight parameter settings of them are relatively large, which could adapt to the frequently changing energy consumption and network topology. The proposed scheme significantly improves the robustness and adaptability of the network. Figure 12 depicts the energy consumption under different numbers of UAV nodes. It indicates that the energy consumption of the proposed IWLC scheme is the lowest, as compared to the other two algorithms. The energy consumption of IWLC and WSCA is close when the number of UAV nodes varies from 20 to 40. However, as the number of nodes increases, the energy consumption of IWLC is less than that of WSCA, because of its efficient CH selection and cluster maintenance algorithms.