# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 2 Related Works  
Clustering techniques have an important role in extending WSN durability. The low-energy adaptive clustering hierarchy (LEACH) protocol [7] is a basic clustering protocol for WSNs. The proposed research addresses some limitations in the CH selection step of the setup phase. Depending on the assumption that the consumed energy rises by increasing the distance, much effort is dedicated to enhancing the setup phase of LEACH by selecting the shortest distance to minimize the energy consumption [20; 21; 22; 23; 24]. As stated in [16], the main problem of most works recommending LEACH is the assumption that distance is the only factor that affects energy consumption. This hypothesis does not necessarily propose an efficient method of selecting the proper CHs for SNs that can extend the network durability. Notably, the energy consumption of any network is strongly influenced by further factors such as traffic load, network bandwidth, and type of provided service, in addition to some demographic factors represented by the physical distance between communicated sites and the density of the communicated area. Similar to distance, the network traffic load directly influences the power consumption of the network [25]. Therefore, some works try to use a combination of some of these factors to select the most suitable CH for each node. This approach encourages the exertion of much effort to enhance the LEACH protocol using different strategies and technologies [5; 26; 27]. Some research works utilize both residual energy and distance to enhance LEACH and achieve more accurate results [28; 29; 30]. Other efforts have selected CHs based on the node degree [31] or communication cost [32]. The multiplicity of factors that influence the process of LEACH necessitates the emergence of new methods to produce an optimal decision under these factors. Thus, the optimization of energy consumption is a significant WSN research topic that is highly concerned with minimizing energy dissipation. Some works introduced to optimize LEACH use custom fitness functions. The work by [12] also uses a custom fitness function to optimize the CH election step of LEACH based on a quadratic competition. The candidate CHs are also elected after the determination of the CHs. The final CH node is identified by the second competition. There are two major differences between ICSI and this work. First, this work acts to enhance a different step (CH-election step) while ICSI tries to enhance the cluster formation step which involves a much larger number of nodes. Another difference is in the factors that are used in the optimized selection process. The recommendation made by the authors of [29] uses a custom fitness function to optimize the enhancement of LEACH by determining the scope of the CH selection according to the energy level and by controlling the cluster size. In fact, ICSI benefits from their idea to control the cluster size during the optimization process. While they depend on the energy level and a fixed cluster size, ICSI is based on the adequacy of the energy level compared to the required energy. In addition, ICSI controls the optimal size of each cluster according to the capability of the CH to join new members. The authors of [33] introduced a fuzzy-based enhanced cluster head election algorithm (FBECS) that anticipated the remaining energy, distance, and density of the node in its neighborhood. The authors calculated the eligibility index for each node during the CH selection. This work differs from ICSI again as it acts to improve the CH-election step versus the CH-selection step by ICSI. Specifically, it does not consider the sufficiency of the residual energy when allocating SNs to their optimal cluster. The current work is affected by this work but with consideration of the effect of the CH-density not in the density cluster of members in addition to different factors.