## 2 Literature Review  
Several researches focus on clustering techniques for wireless networks [8; 20; 22; 36; 37; 38; 39]. In this section, we present some of the prevalent one-hop and k-hop intra-clustering approaches designed for wireless networks (Table 2). Many researchers have addressed the one-hop intra-clustering [5; 40; 41; 42] in order to design an energy efficient network. Low Energy Adaptive Clustering Hierarchy (LEACH) [43] is one of the first and most well-known energy efficiency clustering algorithms in this field. In LEACH, each sensor joins the closest one-hop away CH. The role of CH is rotated randomly and periodically over nodes to extend the network lifetime. This work has motivated many variations and improvements such as EP-LEACH [44], FL-LEACH [45] and MH-LEACH [46]. [PERSON] et al. [44] improved LEACH protocol by introducing the energy harvesting capability in the CHs election, which minimizes the energy consumption of devices. Authors in [45] proposed an amelioration of LEACH that shares the same heuristic and integrates a fuzzy inference system to determine the desired number of CHs in the network by only using the network density as an input. [PERSON] [47] proposed a bio-inspired clustering algorithm. The idea is to use a multi-metric optimization algorithm to find a sub-optimal set of cluster heads and balance the number of CMs over clusters in order to reduce energy consumption. The one-hop intra-clustering provides a convenient solution to structure the network. However, for large networks many clusters are formed. Therefore, isolated devices may appear and connectivity may be difficult to ensure. Multi-hop (or k-hop) intra-clustering alleviate the problem by producing a consistent structure in dynamic and large scale environments. MH-LEACH [46] or Multi-Hop LEACH is a novel extension of LEACH that supports the k-hop intra-clustering to increase energy efficiency. In this approach, cluster members send their data to the cluster heads through multi-hop communication and the CH aggregates data directly to the BS. The objective is to reduce the intra-cluster energy consumption by using multi-hop communications. However, in addition to the disadvantages of LEACH, this technique does not provide load balancing during the routing process. E-PEGASIS [48] is a chain-based intra-clustering mechanism that aims to reduce the redundancy of data transmitted toward the BS. The algorithm finds a dominating set in the network and uses an ant colony optimization to elect the sub-optimal routing chain from the dominating nodes. This approach is an energy efficient solution. However, the time complexity required to organize the nodes into chain is costly. In [49], authors presented a k-hop Energy Constrained intra-clustering technique based on the Dominating Sets theory called K-ECDS. The proposed algorithm considers the energy limitation and models the problem of optimally choosing cluster heads based on the quality of communication channels and nodes cardinality. The clustering is distributed and aims to scale the network. Authors in [14] proposed a new energy efficient hierarchical clustering using an intra-cluster communication scheme to improve the lifetime of the wireless networks. The proposed method uses an uneven clustering technique to alleviate the hot spot problem. The BS divides the network into three unequal clusters level, the CHs selection is based on the residual energy and the number of neighbours. The multi-hop intra-clustering is only applied in clusters located faraway from the BS. This protocol reduces the number of clusters and the messages overhead. However, the clustering is centralized, a potential BS failure will lead to the failure of the whole protocol. Authors in [19] presented a multi-hop intra-cluster clustering architecture. Initially, nodes enter a sleeping period and compute a wake up time slot according to their degree and the average distance to their neighbors.