# A Survey of Using Swarm Intelligence Algorithms in IoT
## 4 Application of Swarm Intelligence Algorithms in UAV-Aided Wireless Networks  
According to previous literature, the UAV application in the wireless network can be divided into three conditions: (1) in the base station or network congestion problem, the UAV as a temporary base station (DBS); (2) when two or more users distance is far, the UAV can be used as a relay with a wireless connection; (3) as the application of the sensor, the UAV can be used for collecting sensory information. According to these three applications, we further discuss what problems are more inclined to solve in a \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Package** & **Delay** & **Packets** & **Run** & **Energy** & **Life** & **Active Node** & **Other** \\  & **Loss** & **Received** & **Time** & **Consumption** & **Time** & **Ratio** & & \\ \hline
[19] & Y & Y & N & N & N & N & Y & - \\
[20] & N & Y & N & N & Y & Y & N & - \\
[21] & N & N & Y & N & Y & Y & Y & - \\
[22] & N & N & N & N & Y & N & N & Connection rate \\
[23] & N & N & N & Y & N & N & - \\
[24] & N & N & N & N & Y & Y & N & Number of hops \\
[25] & N & N & Y & N & Y & Y & N & - \\
[26] & N & Y & Y & N & Y & Y & Y & Throughput \\
[27] & N & N & N & Y & N & N & - \\
[28] & N & N & Y & Y & N & Y & N & Throughput \\
[29] & N & Y & Y & N & Y & Y & N & - \\
[30] & N & N & N & N & Y & Y & Y & Throughput \\
[31] & N & Y & N & N & Y & N & N & - \\
[32] & N & N & N & Y & Y & N & Path length, Packet deliver \\
[33] & Y & N & N & N & Y & N & Routing load, \\
[35] & N & Y & N & N & Y & N & N & - \\
[36] & N & N & N & N & Y & N & N & Coverage rate, \\
[38] & N & N & N & N & N & N & Sensing radius of nodes \\
[40] & N & N & N & N & N & N & N & Deployment cost \\
[41] & N & N & N & N & N & N & N & Target nodes, Convergence \\  & & & & & & & behavior, Location \\
[41] & N & N & N & N & N & N & N & Node localization \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters to be considered in the validation of SI applications in WSNs. specific application, and we also analyze some factors that should be considered for each problem, which are illustrated in Figure 6.  
For improving the flexibility of the network, the UAV can be integrated into the wireless network as a flight base station (BSs) named DBSs. DBSs are a promising application in the wireless network. However, the deployment of DBSs can face several challenges. First, a 3D-DBSs layout is needed to meet the dynamic requirements of the system. Second, the availability and related resource allocation issues of reliable wireless backhaul links. The final problem is the association between the users and BSs [46]. Literature [47] put forward an algorithm to find an efficient 3D layout of DBSs in addition to the user-BS associations and wireless backhaul bandwidth allocations to maximize the sum logarithmic rate of the users. This is the first time to consider the 3D layout of backhaul resource allocation, user associations, user types, and DBSs simultaneously. User-BS association and wireless back trip bandwidth allocation are found by the decomposition method, and the location of DBSs is updated by the PSO algorithm. Reference [48] presented a fair-sense multi-DBS 3D deployment based on PSO, which maximizes the logarithmic sum of user accessibility. The PSO algorithm is adopted in which the stop criterion is selected as the condition for the particle to converge to a certain point. The method achieves optimal fairness with the least number of DBSs deployments. Also, the proposed algorithm can work in networks with different user distributions. Reference [49] studies the problem of providing wireless coverage when a UAV is used to distribute users on each floor of a high-rise building evenly. PSO algorithm is adopted to find an efficient three-dimensional layout of UAV. When the same number of users is evenly distributed on each floor, the total emission power required to cover all indoor users is minimized.