# A Survey of Using Swarm Intelligence Algorithms in IoT
## 7 Current Situation and Development Trend  
### Current Statuses  
In this section, we conduct a statistical analysis in two aspects. On the one hand, we analyze the number of related literature using PSO, ACO, and ABC in wireless, WSN and UAV in the past three years (2017-2019) and further analyzed the specific applications of the three algorithms in WSNs. On the other hand, we analyze the measures considered by these SI algorithms in optimization. Figure 7 presents related works where SI algorithms (PSO, ACO, and ABC) are applied in wireless network applications in the past three years (2017-2019) (this data comes from Scopus' search results). \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Platform** & **Percentage** \\ \hline Did not mention & 32.4 \\ MATLAB & 38.3 \\ Java & 5.9 \\ Ns2 & 5.9 \\ OMNeT++/Castalia & 8.8 \\ jMetal & 2.9 \\ CloudSim package & 2.9 \\ QT Creator 2.4.0 & 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation platforms analysis. \begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Scenarios** & **Percentage** \\ \hline One or two & 61.8 \\ Three or four & 17.6 \\ At least five & 20.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental simulation scene selections analysis. We can see that the PSO has the largest number of applications in the field of wireless network, even up to two or three times than ACO and ABC literature. The PSO relies on its optimization ability with simple parameters and fast convergence to find the optimal solution to solve the continuity problem, so it is most widely used in function optimization, neural network training, fuzzy system control, and other aspects because of its advantages with fewer and simpler parameters. It is suitable for solving continuous functions. In UAV application, there is a requirement for the positional design between each individual in the flock because the PSO algorithm originally generated to simulate the movement of bird groups. For example, to prevent collisions between individuals, the distance between individuals should not be too close to convenience of communication, individuals should not be too far apart. It is similar to the flight patterns of UAVs, so the PSO can be used in path planning, UAV positioning, task assignment, multi-UAV cooperation. The ACO is originally from ant path-finding, so it is widely used in path planning. The ABC is originally from bee collaboration, so it can be used for multi-UAV cooperation. As Figure 8 shows, the applications of SI in WSNs mainly lies in CH selection (about 41%) and routing protocol (about 47%). On one hand, many WSN studies focuses on these two problems. On other hand, most SI algorithms can be applied to these two problems. The applications of PSO in WSN is more about CH selection, routing algorithm, and node location. Among them, SI-based node location algorithm adopts PSO more. The ACO has the characteristics of distribution, adaptive optimization, and strong scalability to residual pheromones. Therefore, it is often used to solve routing protocol problems in WSNs. The algorithm has few parameters and high precision. In the optimization, only fitness functions are used as an evolutionary basis. In WSN applications, the ABC has been widely used to solve CH selection, sensor deployment, node location, and other related problems. Figure 8: Different applications for PSO/ACO/ABC in WSNs. Figure 7: Numbers of SI literatures about wireless, WSN and UAV in Scopus (by October, 2019). The search results by the keywords: (**a**) PSO, ACO, ABC, and wireless; (**b**) PSO, ACO, ABC, and WSN; (**c**) PSO, ACO, ABC, and UAV. There are various problems in traditional SI algorithms. Two examples are that the PSO is easy to fall into the local optimal, and the ACO's early convergence speed is very slow. The optimization of SI algorithms has been considered in the literature we studied. As Figure 9 shows, most kinds of the literatures (61%) currently select improved parameters.