## 2 Related Work  
The main design challenge in battery-powered WSN is to prolong the network lifetime by using energy-efficient algorithms and protocols in medium access control (MAC) and Network layers [17; 18]. Therefore, energy efficiency has been a very popular keyword in WSN literature. Researchers have been trying to tackle this issue by different means such as proposing methods that directly focus on energy conservation such as sleep scheduling [19], or by proposing energy efficient algorithms such as energy efficient routing, energy efficient medium access control protocol, using clustering [20], and even energy efficient coverage enhancement specially in mobile sensors case [21; 22], which perform their classical task more energy efficiently. For example, as is discussed in Reference [19], by carefully selecting a certain number of nodes to be activated to cover a desired portion of a monitored area, network life-time can improve more than 80% in certain areas. Clustering algorithms, as one of the important ways to reduce the network load and conserve energy, are widely studied in the literature [16]. Like conventional WSN, clustering approaches are widely applied in EH-WSN. However, in EH-WSN, as the amount of harvested energy is uncertain, the protocols should deal with unique challenges and specific characteristics. Several clustering algorithms are proposed for EH-WSN based on the famous Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [23]. One of these extensions is Energy Potential LEACH (EP-LEACH), [24], which predicts the amount of energy that each node might harvest and tries to avoid using a low energy potential node as a cluster head. Using this method, EP-LEACH tries to construct more stable clusters and balance the power. Solar LEACH (sLEACH) is another extension of LEACH for energy harvesting sensors [25]. In sLEACH, the network consists of both solar and battery powered sensors. sLEACH gives higher priority to solar powered sensors with a high level of energy to become a cluster head. Authors in Reference [26] proposed a centralized genetic-based unequal clustering algorithm for energy harvesting network (EHGUC). The main idea is that a base station creates clusters with different cluster sizes, in a way that the clusters that are closer to the base station will have smaller sizes, so as to make them consume less power. It uses several parameters such as the distance between nodes and energy harvesting rate as weighting parameters to select the cluster head. A gradient-based energy-efficient clustering (GEEC) packet forwarding mechanism is introduced in Reference [27]. Based on the relative position of the nodes and hop counts to the sink, a gradient model is constructed [27]. Cluster heads are selected in a distributed manner by considering the energy harvesting rate and the distance between a CH candidate and the center line of its circular ring. Thus, unequal clusters are formed to lower network overhead and balance network power consumption. The authors in Reference [28] proposed the use of energy harvesting sensors for relaying CH traffic. The concept of clustering is used in [29] to facilitate the routing in EH-WSN. Energy Neutral Clustering (ENC) [30], with the goal of providing perpetual network operation, groups the network into several clusters. It uses a mechanism called Cluster Head Group (CHG), which allows each cluster to have multiple cluster heads to distribute the traffic load. Using this method, the frequency of clustering and control message overhead is reduced. Moreover, using convex optimization techniques, the optimum number of clusters are computed. Another centralized clustering algorithm proposed is Reference [31] uses discrete particle swarm optimization (DPSO) for clustering where base station uses the status of all the nodes to modify the DPSO algorithm for clustering.