# A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes
## 2 Literature Review  
In the last decades, extensive research has been carried out around wireless nodes clustering. Some studies were performed on the basis of centralized clustering whilst some others presented a distributed clustering method. A subset of widely-used clustering algorithms could be mentioned as LEACH, LEACH-centralized (LEACH-C) [3], and hybrid energy-efficient distributed clustering (HEED) [20]. One of the main distributed clustering algorithms used to reduce energy consumption in WSNs is the LEACH algorithm. In this algorithm, CH is selected based on a random rotation. Therefore, the energy consumption has been optimized and the energy load in distributed throughout the network. If fixed nodes are selected as CH, their energy will end soon, and they die earlier than other nodes. Hence, nodes are selected as a CH by a fixed probability, and they introduce themselves to the whole network. Since CH is selected in a probable manner, it is possible that they are close to each other; therefore, one of LEACH problems is the heterogeneous distribution of CH in the environment. Another widely-used algorithm proposed is the HEED algorithm [20]. In this algorithm, each node creates a random number between zero and one. If a number smaller than CH probability is selected for the node, that node is then selected as tentative CH. If CH is equal to one, then the node becomes the final CH. Otherwise, the tentative CH remains, and finally, CH probability becomes doubled. In each ordinary node, if the CH probe is equal to one, then the node becomes final CH. Otherwise, it creates a random number between zero and one. If the number is smaller than CH probability, the node is then selected as a tentative CH, and finally, CH probability becomes double. This procedure continues until CH is selected. CH is selected according to the remaining energy of the node and they are considered as the second parameter of communication costs inside the cluster. Collectively, nodes join a CH whose distance to the related CH is smaller. Another distributed clustering algorithm is density and distance based CH selection algorithm (DDCHS) [21]. In the clustering step, the area is virtually divided into hexagons so that circular cluster overlap is prevented and in fact, some borders are considered for clusters. For each hexagon, a CH is selected and subsequently, some subcircles are taken into account in the virtual hexagons according to the average of usual nodes distances to the cluster center. This algorithm is composed of three steps: (i) local grouping which determines a center in length and width of the clustering area. The area is divided into four equal parts, (ii) comparison of nodes density--the node density is determined in each part and one of the parts is selected as a candidate quarter. (iii) Comparing the distance between nodes by computing the distance of each node to other available nodes in candidate quarter and selecting the node with the shortest distance to other nodes as a CH. An energy-aware distributed dynamic clustering protocol, based on fuzzy logic (ECPF) [22], is another algorithm introduced in 2012. This method performed the clustering by using the fuzzy logic and taking the energy of nodes as a nonprobabilistic parameter. The node selection was done sporadically. This protocol reduced the overhead of the network, by providing the mechanism similar to a setup phase, rather than performing the setup phase in each round. With regard to energy saving, the purpose of this algorithm was to select a set of appropriate clusters which covers the whole region selection by means of a fuzzy system. So in this fuzzy system, input parameters such as the degree and the center of the nodes were considered. The use of fuzzy logic in selecting the CH and the rule of overhead reduction contributed to improving the lifetime of the network. One of the challenges proposed in this clustering is the lack of complete coverage in the network in the time range.