## 1 Introduction  
IoT networks cover progressively more aspects of our daily life and represent a convergence field of multiple technologies. The basic idea is to allow the variety of devices, or things, present around us (smartphones, sensors, cameras, laptops, Radio-Frequency Identification tags (RFID), etc.) to interact and cooperate to achieve a common goal and make users lives easier [1]. With extensive attention, IoT has rapidly spread to various fields of interest, i.e., smart healthcare, smart city, smart home, intelligent transportation systems and many other applications [2]. Such systems could be seen as a large number of heterogeneous devices that need to access and be accessible from the Internet [3]. This paradigm enables new technologies [4, 5] such as heterogeneous types of big data, Cloud and Fog Computing, Data Centers, etc. IoT devices perform more functionalities than mere sensing, leading to a fast depletion of the available energy where battery recharging is often costly or economically disadvantageous. Therefore, energy efficiency is a basic concept that should be incorporated into the overall networks infrastructure [6]. Network clustering is a technique that has been widely used in wireless networks. Indeed, grouping sensor nodes into clusters is an effective way to improve the network performance due to its ability to extend the networks lifetime [7, 8] and increase the scalabilityof the network [9]. Figure 1 illustrates a simplified example of devices connecting with the Base Station (BS) using a flat architecture (Figure 1a) versus connecting through a cluster based scheme (Figure 1b). This figure shows how the number of direct communication channels with the BS can be considerably higher using a flat network. The clustering is used as a functional solution for managing the multi-channel communications [10]. Many channel management mechanisms were proposed to improve the radio capacity of the network [11; 12]. However, in a flat and dense network, the base station may not satisfy the simultaneous connection resulting in a waste of wireless communication resources and energy. Several previously proposed clustering protocols focus on the multi-hop inter-clustering [7; 9; 13] between the cluster heads (CH's) and the base station to increase the network durability, however only few consider the intra-cluster communication (between devices and their CH). Usually, existing intra-clustering schemes [14; 15; 16] assume a direct connection between Cluster Members (CMs) and their CHs, therefore, a high number of clusters are formed. In a large scale network, the distance between nodes and their CHs may not be short enough for communication. Therefore, direct communication becomes obstructive and k-hop intra-clustering communication should be applied to ensure the network scalability. The k-hop intra-clustering model received a considerable interest from the research community [17; 18; 19] since it expresses (in a better way) the features of well-organized network. Indeed, k-hops intra-clustering model reduces the number of clusters which reduces the number of long-range communication channels and increases network durability [9]. The objective of k-hop clustering is to organize nodes into clusters where the path between cluster members and their corresponding cluster head is shorter than \(k\) hops of distance. It provides a robust topology in dynamic networks since cluster members may not be in direct contact with their CH, hence reconfiguration events (cluster head election and re-affiliations) are limited [20]. The maximum number of hops \(k\) inside each cluster could be easily determined according to the targeted application requirements and the deployment area's nature. The proposed algorithm uses a weight based mechanism during the clustering process which consists of selecting the node with the maximum weight among its k-hop neighborhood as a CH. The weight of each node is based on three parameters, namely: the two-hop connectivity ratio, the remaining energy and the communication quality.