# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### CH Selection Algorithms Based on Si
#### 3.1.3 Cluster Head Selection Algorithm Based on ACO and ABC  
Reference [25] proposed an improved ACO algorithm to optimal mobile sink movement track. The movement of mobile sink between CHs is regarded as a TSP problem, and the improved ACO is used to find the optimal path to access all CHs through a single-hop physically close communications. To save energy, rotate CH only when any CH residual energy falls below the energy threshold. This method improves the network lifetime and data delivery, but only grid partition is used for cluster partition, without considering the uniform distribution of cluster nodes. Reference [26] proposed a hybrid MAC, routing and unequal clustering cross-layer protocol (FAMACROW) based on fuzzy and ant colony algorithm. The model used the Mamdani fuzzy reasoning system (FIS) to select CH. Input parameters: residual energy, neighborhood proximity, and link quality indicator. The reliability of the protocol has improved with the use of link quality indicator (LQI) to select CH and transfer the probability of route between clusters. Simulation results showed that the energy consumption of CH is reduced and the node survival time is extended. Reference [27] proposed a multi-path routing in the WSN by using the Fractional ABC (FABC) and the exponential ACO (EACO). The first step is to use the FABC to find the CH by consideringsome parameters, such as energy, distance, and delay. In the second stage, the proposed new algorithm EACO (index ACO) is adopted to realize the multi-path path discovery. Compared with other SI hybrid algorithms, the EACO-FABC has better performance in energy and goodput. Also, it can easily adapt to the IEEE 802.15.4 protocol (a low-rate communication protocol). However, as can be seen from the simulation diagram, the CH distribution is very uneven. Reference [28] proposed a centralized cluster routing protocol based on Sugeno fuzzy reasoning system named LEACH-SF. The ABC is used to adjust fuzzy rules. Each optimization is performed before LEACH -SF runs. The results showed that the LEACH-SF is superior to the existing fuzzy-based clustering algorithms in terms of minimizing the distance within the cluster, maximizing the network lifetime and maximizing the number of packets received by the BS. The LEACH -SF also has the potential to be expanded in mobile sensor nodes and multi-hop routing. The simulation is based on a heterogeneous network. In [29], the authors proposed an improved ABC named iABC. Therefore, iABC used a Student's \(t\) distribution to improve the population's initialization and used differential evolution to improve the solution search equation. The method enhances global convergence by using a compact student T distribution. Simulation results showed that this method consumes less energy and delivers the most packets with minimum end-to-end delay. When the BS is placed outside the network field, the impact on the performance of the protocol is also small. Table 3 compares the CH selection algorithms. With respect to CH selection, clusters can be formed in two ways: (1) First, the family of clusters is divided, and then the CH is found; (2) For the former, the initial CH is usually selected as the node at the center of the cluster. While the latter at the CH when the choice to consider to whether the CH uniform distribution, the size of the cluster (high node can lead to CH burden too big, reduce energy consumption too fast, accelerate CH death) for the CH rotation, now more literature or rotation as the standard with CH energy is lower than the threshold value also has to rotate according to time, but we think the former is more flexible, now for the development direction of intelligent sensors (which can switch of receiving to save energy) or consider QoS of WSN has more practical value. In addition, the WSN is divided into single-hop and multi-hop within the cluster. The multi-hop WSN within the cluster should also consider those member nodes that are close to CH and will consume energy faster.