# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 1 Introduction  
The Internet of Things (IoT) is based on the fact that each object or thing can use wireless communication to communicate with each other [1]. Nowadays, IoT has attracted attentions of societies, governments, and industries for a wide range of applications including smart homes,healthcare services, environmental monitoring, smart transportation, smart networks, security, fire detection, finance tracking, smart lighting, etc. [2]. In this context, wireless sensor networks (WSNs) play an important role in increasing the number of networks with low-cost smart devices which can be easily installed. WSNs are widely-used in various fields such as environment, health, military etc. Each node is composed of a sensor unit, processing unit (microcontroller), the radio communication unit, and an energy resource. Figure 1 represents these components. Examples include wildlife and environment supervision, health monitory, pediment supervision, border supervision and control, security, etc. [3; 4; 5]. Wireless nodes typically have capabilities such as sensing, computing and self-organizing operations for routing and data transmission to a base station (BS) [6]. However, limitations of wireless nodes include short-range communications, low bandwidth, processing/storage limitation and, particularly, energy consumption [7]. One of the main problems in IoT based on wireless nodes is the energy consumption in nodes. Each node has a battery and therefore, limited energy is stored in it. Since the implemented wireless nodes are not easily accessible, it is hardly possible to access the nodes and recharge or exchange the battery. Energy consumption mainly occurs in sensing, data processing, and data transmission [7]. However, most of the mentioned energy consumption is related to data transmission. Therefore, reducing energy consumption in these networks is an ongoing research [8; 9]. Routing between two nodes with the highest energy efficiency and balanced energy consumption between them are two of the factors that can affect the lifetime of a network significantly. One of the methods to reduce energy consumption in these networks is to find an optimal route by applying to cluster [7; 9; 10]. In the clustering method, nodes are divided into clusters and one node within a cluster is selected as a cluster head (CH). The cluster member (CMs) nodes, sensing the environment, send the data to the CH. The CH receives data from CMs and aggregates them and finally; the aggregated data is transferred directly or indirectly to a BS with the help of middle nodes. In fact, the purpose of clustering is to find an optimal route to send data to a BS [11]. Clustering protocols are usually classified into two protocols including static and dynamic protocols. In static protocols, clustering is performed once and the nodes always remain in the same cluster. Virtual concentric circle band-based clustering (VCCBC) [12] and an energy-efficient protocol with static clustering (EEPSC) [13] are two of the examples of the static clustering methods in WSNs; although, in terms purely of static protocol performance, network overhead is reduced but shows instability for a long period of time. The main disadvantage of static methods is that it results in energy depletion in several nodes [14; 15]. In dynamic protocols, clustering is performed in each round, and new clusters form during each round. Low-energy adaptive clustering hierarchy (LEACH) [3] is one of the examples of the dynamic clustering methods. Dynamic performance can improve network lifetime, but usually, it has a high overhead [14; 15]. Clustering is the most important energy efficient technique. In this technique, the sensor nodes are organized into groups termed as clusters. The regular nodes in the cluster are called as cluster  
Figure 1: Components of the sensor node. members and a CH is selected among them [16; 17]. In order to prevent the network from hot spot issue, unequal clustering techniques can be utilized for load balancing between the CHs [18; 19].