# A Survey of Using Swarm Intelligence Algorithms in IoT
## 1 Introduction  
For ease of reading, we abbreviate nouns, as shown in Table 1.  
In nature, many creatures--such as fishes, birds, and bees--have group behaviors. The abilities of individual members in a group are limited, but the whole group has a strong vitality. The strong vitality is not only a simple superposition of individual abilities but also an adjustment of individual behaviors through exchanging information, cooperating, and finally reflecting group intelligence. Swarm intelligence (SI) [1] algorithm is a simulation method to simulate biological group intelligence. The potential parallelism and distributed characteristics of SI algorithms enable \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviations** & **Instructions** & **Abbreviations** & **Instructions** \\ \hline WSN & Wireless sensor network & UAV & Unmanned aerial vehicle \\ PSO & Particle swarm optimization & CH & Cluster head \\ ABC & Artificial bee colony & MS & Master station \\ BOA & Butterfly optimization algorithm & LP & Label propagation algorithm \\ FCM & Fuzzy c-means algorithm & BS & Base station \\ LS & Local search & DBS & Drone base station \\ GA & Genetic algorithm & VANET & Vehicular ad-hoc network \\ IoT & Internet of Things & ACO & Ant colony algorithm \\ GWO & Grey wolf optimization & DBS & UAV as a base station \\ LEACH & Low energy adaptive Clustering hierarchy & BFO & Bacterial foraging algorithm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Noun abbreviation. the possibility of solving complex nonlinear problems with advanced capabilities in terms of self-adaptability, robustness, and search ability. Up to now, there are many SI-inspired optimization algorithms, such as classical particle swarm optimization (PSO) [2] and ant colony optimization (ACO) [3]. In recent years, there exist many improvements--such as artificial bee colony (ABC) [4], bacterial foraging algorithm (BFO) [5], and butterfly optimization algorithm (BOA) [6]. SI algorithms search the optimized solution based on heuristic information. It can be applied to a wide variety of optimization problems (e.g., dynamic optimization problems, multi-objective optimization problems) and NP problems. With the ever-increasing development of IoT, SI exhibits great applicational prospect in IoT-related applications. IoT is a powerful Internet based on advanced technologies, such as radio frequency identification devices (RFID), sensor technology, cloud computing, and wireless sensor network (WSN) [7]. It allows users to connect any physical object to the Internet for information exchange, communications, and decision-making. However, the development of IoT still faces some challenges. For example, plenty of smart devices from heterogeneous sources can be connected, so security is one of the key challenges in IoT. Also, the vast number of devices in the IoT will cause energy consumption problems and'spam' problems. Furthermore, the IoT system is complex and dynamic; thus, many SI algorithms are developed to compute such complex activities with simple individuals. By modeling the IoT system as a group of simple devices, the SI-based algorithm can be adopted to achieve global optimization. Besides, cloud computing plays an important role in data analysis based on the huge amount of data acquired from IoT systems. SI also applies to the applications of cloud computing, facilitating solving multi-objective optimization problems. In general, the category of IoT can be classified into four parts according to its hot topics and the latest research directions, as shown in Figure 1.  
WSN is an advanced technology in the IoT, which consists of a large number of nodes that have capabilities of sensing, computing, processing, and communicating [8]. Nevertheless, for each sensor, its battery capacity and sensing ability are limited; thus, there are still some crucial problems in WSN, such as routing protocol planning, node localization, and cluster head selection.