# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks  
[PERSON]  
1 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, China; [EMAIL_ADDRESS]  
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2 College of Information Engineering, Yangzhou University, Yangzhou 225000, China;  
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[PERSON]  
2 College of Information Engineering, Yangzhou University, Yangzhou 225000, China;  
[EMAIL_ADDRESS]  
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4 School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350000, China  
4 School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India; [EMAIL_ADDRESS]  
[PERSON]  
5 Liberal Arts & Convergence Studies, Honam University, Gwangju 622623624, Korea  
5 Correspondence: [EMAIL_ADDRESS]; Tel. : +82-10-5269-9873  
impossible because of the enormous quantity and harsh environment. Therefore, it is necessary to design energy-efficient routing protocols. In clustering-based protocols, all sensors are divided into different clusters according to some specific rules. Usually, one cluster head (CH) is selected in each cluster and the other nodes will communicate with the CH directly. By introducing clustering methods, the following benefits can be achieved. First, the clustering technique makes the data transmission between sensors easy and the network topology is easy to organize. CH can adopt the Time Division Multiple Access (TDMA) schema to its cluster members for data uploading. In this way, package loss rate will be reduced and much energy can be saved from data retransmission. Second, clustering can help to alleviate the hot spots problem which is caused by centralized data transmission. The rotation of CHs can largely balance the energy consumption of different sensors. Third, clustering can reduce the total energy consumption of the network by reducing the average intra-cluster communication distance. Although many benefits can be obtained by introducing clustering methods, some drawbacks still exist in the practical applications. Many clustering protocols such as low-energy adaptive clustering hierarchy (LEACH) [15] and power-efficient gathering in sensor information system (PEGASIS) [16] prefer to select the CHs in a random way, which causes the uneven distribution of CHs. Sensors have to adopt long distance communication with CHs in those areas which contain few CHs. One good solution for this problem is to set the competition range of sensors during the selection procedure. Once a sensor claims to be a final CH, other sensors in its competition range will give up the election to avoid the dense distribution. Many schemas are less energy efficient and the total energy consumption of the network is high, because they do not optimize the intra-cluster and inter-cluster communication distance. Therefore, it is a good choice to introduce a heuristic algorithm such as ant colony optimization (ACO) and particle swarm optimization (PSO) to decrease the average communication distance. Additionally, premature death of sensors occurs in some protocols. Those protocols commonly not take the residual energy into consideration when they select the CHs. Premature death of CH will result in blind spots for monitoring and the performance of the network will sharply decrease. Therefore, it is important to take energy balancing strategy in routing protocol designing. In order to solve the above problems, affinity propagation (AP) and the modified K-medoids [17] are combined for better clustering in this paper. AP is commonly used to calculate the similarity of nodes. It adopts the similarity matrix to represents the similarity between different nodes and the value of the diagonal of the similarity matrix is used as a criterion to judge whether the node can become a cluster center.