# An Energy-Aware Method for Selecting Cluster Heads in Wireless Sensor Networks
## 2 Literature Review  
Although authors have proposed several papers to select a suitable cluster head, we can categorize the methods into two groups. The first group has focused on the distribution of energy load with the cluster head selection. The authors in [8] proposed a cluster-based approach in which the energy load is distributed among the sensors in WSNs by using the rotation of local cluster-based stations, which is randomized. To improve robustness and scalability for dynamic networks, they used a coordination that was localized. Furthermore, for decreasing the amount of transmitted information to the base station, the data were also incorporated into the routing protocol. In [9], the authors developed an adaptive clustering hierarchy analysis with a low energy level. In this study, they proposed a protocol architecture for the networks that have microsensors. The authors also integrated the cluster-based routing and media access with each other. This method also consists of a new integrated cluster formation technique to save communication resources. By considering this method, the performance of the proposed algorithm considerably improves system lifetime, latency, and application-perceived quality. In [10], the power consumption of wireless microsensor networks was reduced. The authors changed a communication protocol, and by using a deterministic component, they extended LEACH and proposed a new stochastic cluster head selection algorithm. Furthermore, to increase the lifetime of microsensor networks, they introduced a new method that uses three new metrics. In [11], the authors proposed a cluster head selection method in which the node degree is restricted. In order to reduce energy consumption and balance load, this method first chooses the cluster head nodes and then makes well-distributed clusters. [PERSON] et al. [12] introduced particle swarm optimization to produce the clusters with an optimal cluster head selection. This method decreases the cost of placing the optimal position for the cluster head nodes. The authors in this study performed PSO implementation within the cluster, which forms a semi-distributed method. The objective function criteria were four parameters, including the residual energy, minimum average distance from the member nodes, and the headcount of the probable head nodes. In [13], for heterogeneous WSNs, the authors proposed an improved distributed clustering scheme, which consumes energy efficiently and effectively. This method extends the lifetime and stability of the network. The authors in [14] proposed an energy efficient load balanced data gathering protocol. In this protocol, the partitioning procedure is performed by fuzzy logic, and the authors also used fuzzy logic to select an appropriate cluster head. This new approach was compared with LEACH, Fuzzy LEACH, and FCMLEACH in terms of prolonging the network lifetime and in a load balanced way. The performance of the proposed method is better than the other methods. [PERSON] et al. [15] introduced a new cluster head method to transmit the data with the lowest energy consumed effectively. Furthermore, the node that is close enough to the base station and the other sensor is considered as a cluster head. For improving the energy consumption of the network, a new firefly algorithm was proposed. This algorithm also prolongs the lifetime of nodes by selecting the cluster head optimally. The second group of methods, however, consider several significant factors to select an appropriate cluster head. [PERSON] et al. [16] proposed a new clustering model, which chooses the optimal cluster heads by considering four significant factors including energy, delay, distance, and security, and in this paper, the dragonfly and firefly algorithms were utilized to choose the optimal cluster heads. In [17], the authors introduced an approach to select the cluster head according to the energy drain pattern and location of the sensor nodes.