# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 2 Related Work  
Some classic or recent literature about the clustering techniques for WSN is listed in Table 1. Low Energy Adaptive Clustering Hierarchy (LEACH) [15] is a hierarchical routing protocol with two layers, CHs layer and member layer. In each round, every sensor generates a random number and once the number exceeds the threshold value, it will be elected as a CH. One of the serious weakness of LEACH is that the selected CHs are uncontrolled and they distribute unevenly among the sensor field. Additionally, it does not consider the residual energy of sensors which may lead to the premature death of sensors. LEACH-centralized (LEACH-C) [18] is an improved version of LEACH. In LEACH-C, before CHs selection in each round, the information of sensors such as residual energy and position are reported to the base station (BS). The BS calculates the average residual energy of the network and excludes the weak sensors from the candidate CHs. The main drawback of LEACH-C is that the topology of the network is not optimal and the total energy consume of the network is high in each round. LEACH using the AP algorithm (LEACH-AP) [19] adopts iterations to select CHs. The proposed scheme generates clusters by iteration to exchange information between sensors. However, the number of clusters still needs to be set manually. Power-Efficient Gathering in Sensor Information Systems (PEGASIS) [16] is a chain-based clustering algorithm. The whole sensors in the network are connected into several chains and the chain leaders are dynamically selected. Chains are constructed by greedy algorithm and each node in the chain takes turn to be the leader. Each node on the chain only needs to communicate with its nearest neighbor. Due to the chain construct of the network, once a sensor in the chain fails, the whole chain will stop work. Additionally, chains in a larger-scale network will result in severe network latency. Hybrid energy-efficient distributed Clustering (HEED) [20] is another hierarchical routing protocol. The mainly contribution of HEED is that it firstly presents a novel method for CHs selection by competition. The residual energy of sensors is taken into consideration for CHs selection. Each node calculates its average minimum reachability power (AMRP) to determine which CHs should join. The threshold-sensitive energy-efficient sensor network protocol (TEEN) [21] is an event-driven and responsive algorithm. During the process of CHs selection, each candidate CH broadcasts two parameters, soft threshold and hard threshold, to other sensors among the cluster. The hard threshold records data eigenvalues, and the soft threshold records the maximal range of data change. One of the shortcomings of TEEN is that it achieves the clustering result by iterating and the algorithm is executed in the local sensors with low computing power. Therefore, it leads to great network latency. The saving energy clustering algorithm (SECA) [22] is a centralized clustering algorithm. Candidate CHs are firstly selected according to the average residual energy. Then, the modified K-means algorithm which considers the location and residual energy is adopted to determine the final CHs. The algorithm mainly contains two parts, set-up phase and steady-state phase. In the set-up phase, the center location is calculated to set the initial means of points according to the location of the stationary nodes. Then K-means algorithm is utilized to divided the network into several clusters according to the previous initial means. The steady-state phase mainly conducts the data transmission. Member nodes send data to their corresponding CHs using the allocated TDMA schema and the CHs forward the data to the sink. Energy aware unequal clustering (EAUC) [23] is a fuzzy logic-based clustering algorithm. In EAUC the sensor field is divided into several heterogeneous clusters. Those clusters close to the sink own smaller scale.