## 2 Related Works
### Clustering Sensor Nodes Based on Computational Intelligence  
Clustering sensor nodes based on computational intelligence is another active research trend [32; 33; 34; 35; 36; 37; 38; 39]. Compared with the non-computational intelligence methods, technologies of computational intelligence has the characteristics of self-learning, self-organization, and self-adaptive, which enables them to provide more effective solutions for sensor node clustering. The researches have tried to optimize the sensor node clustering with computational intelligence technologies from many different aspects. For example, [PERSON] et al. [32] optimized the cluster structure of sensor nodes to minimize the transmission distance and reduce the energy consumption of the WSNs by an improved PSO, in which the inertial weight of PSO is adjusted to avoid particles being trapped in local optima. To address the problem where the probability of selecting an optimal sensor node as rendezvous point are very low, [PERSON] and [PERSON] [33] proposed a particle swarm optimization based selection (PSOBS) method to select the optimal rendezvous points which were responsible for gathering the data of other sensor nodes. To balance the load of CHs so as to prolong the network lifetime, [PERSON] and [PERSON] [34] proposed an energy efficient clustering protocol based on K-means algorithm (EECPK-means). The EECPK-means method can balance the number of sensor nodes between clusters. To maximize the lifetime of the network, [PERSON] et al. [35] proposed a LEACH-Fuzzy clustering method which realized CH selection and cluster formation based on fuzzy logic. To effectively cluster the sensor nodes and select the optimal CH for each cluster, [PERSON] et al. [36] proposed a hybrid clustering method based on fuzzy c means (FCM) and moth-flame optimization method (MFO) to improve the network quality (FCMMFO). In FCMMFO, FCM was used to form energy-efficient clusters and MFO was used to select the optimal CH for each cluster. To address uneven clustering and poor energy consumption, an inter-cluster multi-hop hierarchical routing algorithm based on iterative self-organizing data analysis techniques algorithm (IICMH) is proposed by [PERSON] et al. [37]. In addition, [PERSON] et al. [38] proposed a single multi-objective problem to find the best network configuration formulation. The proposed formulation took into account the number of CHs, the number of clustered nodes, the link quality between the cluster members and CHs, and the link quality of the constructed routing tree. Furthermore, [PERSON] et al. [39] proposed an improved artificial bee colony algorithm to select the CHs, which took into account the energy of CHs, the density of CHs, the location of CHs, and other similar factors. The existing sensor node clustering methods based on computational intelligence have proved the effectiveness of computational intelligence technologies in providing effective solutions for WSNs. However, sensor nodes are usually randomly scattered in the sensing region. Most existing sensor node clustering methods do not balance the number of sensor nodes between clusters, which causes unbalanced energy consumption. In addition, in different applications, the distribution and number of sensor nodes are different, it is critical to automatically determine the number of clusters according to the number and distribution of sensor nodes. Furthermore, existing technologies of computational intelligence still have some shortcomings. For some optimization methods of computational intelligence, it is easy to converge into the local optimum when dealing with high-dimensional and complex problems. Additionally, in order to cope with different WSN problems, technologies of computational intelligence should be improved accordingly. To prolong the network lifetime and make WSN systems effective data collection [40], the sensor node clustering strategy and the performance of computational intelligence technologies need to be further studied.