# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 4 Data Aggregation and Communication
### Data Aggregation Level 2 Algorithm
Selecting optimized cluster heads has a strong impact on end-to-end delay. The results show that our proposed scheme (FSNet-OC-DA) performs better than other schemes under consideration. Figure 7 presents the number of UAVs vs. PDR. The simulation results show that, initially, the PDR decreased with an increase in UAVs. The PDR of the proposed scheme falls, while the EE-UAV-DA, OC-mUAV, and TA-UAV-DA have more decreases than our proposed algorithm. The impact of optimum cluster formation using HBA reflects that once optimized cluster heads are selected, this will increase the PDR. This is because the cluster heads belong to optimal zones with high energy, relative mo \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Network Simulator & MATLAB \\ \hline Covered Area & 2 km \(\times\) 2 km \\ \hline MAC Protocol & IEEE 802.11 and IEEE 802.16 \\ \hline Antenna Type & Omni directional \\ \hline Propagation Model & Two-ray ground reflection model (intra-cluster) \\   Long-distance propagation loss model (inter-cluster) \\ \hline Radio Frequency & 2.4 GHz, 5 GHz \\ \hline Number of UAVs & 10 to 60 \\ \hline UAV Altitude & 40–50 m \\ \hline UAV Transmission Range & 200 to 300 m \\ \hline UAV Mobility Model & Random waypoint model \\ \hline Transport Protocol & Stream control transmission protocol (SCTP) \\ \hline Traffic Model & Poisson traffic model \\ \hline Application Packet Size & 1000 Bytes \\ \hline Initial Energy & 2 to 5 J \\ \hline Channel Model & Multi-Propagation Channel (MPC) Model \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. As the packets are routed via cluster heads, the proposed scheme with optimal cluster heads performs better than others. Figure 8 illustrates the number of UAVs and packet drop ratio of the proposed scheme and other existing approaches. The simulation result shows that the drop ratio increased in the existing methods and our proposed scheme with the increase in UAVs. The packet drop ratio increased in all schemes because, in dense networks, the number of packets increases, and the load on other nodes also increases. This results in packet drops because the nodes are overloaded. The packet drop in our proposed scheme is less than others because we optimized cluster formation using HBA. Figure 8: Number of UAVs vs. packet drop ratio. Figure 6: Number of UAVs vs. end-to-end delay. Figure 7: Number of UAVs vs. PDR. The increase in the number of UAVs has a direct impact on residual energy. Figure 9 shows that the proposed FSNet-OC-DA residual energy (recorded in round 10) is better than all existing approaches. The proposed scheme eliminates duplicate data transmission to the UAVS-CH by using a near-linear time algorithm. Eliminating data reduces long-distance communication. As we already know, communication consumes more energy than computation. Our model focuses on optimization and reduces communication costs. The efficient optimization and data aggregation enable nodes to consume less energy. Hence, FSNet-OC-DA energy consumption is better than the other schemes. Figure 10 shows that the communication overhead increases with the increase in UAVs, but the FSNet-OC-DA has significantly less communication overhead. The number of UAVs in each cluster is fixed in the simulation, i.e., 40, and the data rate varies from 50 to 250 Kbps. Figure 11 represents the UAVs' data rate and end-to-end delay of the FSNet-OC-DA, EE-UAV-DA, OC-mUAV, and TA-UAV-DA. The simulation result in Figure 10 shows that the end-to-end delay increases with the increase in data rates from 50 to 250 Kbps. It is observed that the FSNet-OC-DA has less end-to-end delay than the existing approaches. The PDR for different data rates (50 to 250 Kbps) is represented in Figure 12. Initially, when the data rate is 50 Kbps, the PDR is very high, but the delivery ratio decreases gradually with the increase in the data rate. Figure 10: Number of UAVs vs. message overhead. Figure 9: Number of UAVs vs. residual energy.