# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 1 Introduction  
A Wireless Sensor Network (WSN) is comprehensively defined as a set of sensors located in an area to sense the environment. Sensors are able to gather different types of data based on their defined functions. After some processing operations, the collected data is sent to a base station (BS) or sink. This process is done by different routing methods. One of the components of the sensor is a battery or power source that is often limited. In addition, these sensors are usually located in areas inaccessible to humans; thus, it is not possible to recharge or exchange these components. For this reason, the power supplies of sensors should be taken advantage of to the fullest possible extent, this process is known as energy efficiency which can increase the network lifetime. Energy efficiency can be achieved by decreasing or balancing the energy consumption of nodes. Clustering is one solution to achieving energy efficiency. There are several reasons to apply clustering techniques in WSNs such as increased scalability, less load, less energy consumption, latency reduction, collision avoidance, guarantee of connectivity, fault tolerance, load balancing, energy hole avoidance and increasing network lifetime [1]. In addition, dividing the network area into subareas helps control the coverage hole problem [2], which appears whenever some parts of the network areas are not covered by any sensor nodes. One important parameter in hierarchical routing protocols is cluster size. With small size clusters, networks may encounter connectivity and coverage problems. Furthermore, most existing clustering based algorithms have not been considered the network coverage to improve the network lifetime [3]. In [3], it is shown that balancing energy consumption per unit area can improve energy balancing ratio throughout the network and a Balanced Energy-Efficiency clustering algorithm (BEE) is proposed to extend coverage sensitive longevity. In [4], the authors showed that if the cluster size is not properly chosen, the total energy consumption of the network will increase exponentially, either when the cluster size is smaller than the optimal value or when the cluster size is larger than the optimal size. In addition, if this parameter is not chosen properly, decreasing the energy usage of member nodes, which is the one objective of applying clustering techniques, will not be achieved; hence, the clustering process will act contrary in this regard. Therefore, the energy consumption of nodes before clustering should be considered to determine the cluster size. Unfortunately, this rule was not considered in previous works. At any rate, grouping nodes into optimal clusters is an NP-hard problem [5] and therefore using optimization algorithms can be an effective method in this regard. Moreover, in multi-hop clustering WSNs, CHs located around the BS relay the data packets from outer CHs to the sink. Accordingly, those located nearby the sink deplete their energy quicker than others. In this situation, the whole network will be partitioned while plenty of energy is left unused and energy holes appear, which can decrease the network's lifetime [6]. In fact, this problem is one of the main causes of premature network death. [PERSON] et al. [7] showed that up to 90% of the total energy of the network can be wasted when the entire network is subject to premature death. The techniques proposed to solve the energy hole problem are grouped into nodes distributions strategies [8; 9; 10], adjusting the data transmission range of nodes [11; 12], usage of sink mobility [13; 14], and adding relay nodes [15]. Furthermore, by optimizing the clustering technique parameters, the problem of energy holes can be mitigated. Parameters that impact the network lifetime include cluster size [16], the number of clusters, and the CH selection technique [6]. Recently, mobile agents such as mobile sinks, relay nodes and data collectors have been widely used to achieve energy efficiency [17].