# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 2 Related Works
On the other hand, many biologically inspired artificial intelligent methods have been implemented to select the best combination of parameters from several possibilities [34]. The GA is broadly applied as it is considered one of the best optimization techniques that can reach the ideal combination of dependent parameters that help minimize the dissipation of power. Using the GA, many efforts have been made to optimize the WSN clustering protocol, especially LEACH [15; 31; 35]. Other works are considered as related to ICSI although they aim to enhance another step with different conditions, but the process of ICSI benefits from their ideas. In addition, the current work is based on GA, but it is different from all mentioned GA-related works in the number of competitions levels in addition to the used factors. In [36], they use the GA to select the best CHs based on their location relative to the BS to lower the data transmission energy cost. They add a new preparation phase that starts before the setup phase in the first round. All nodes initially select a CH and send a decision regarding whether it can be a CH candidate, along with other information. The optimal probability of nodes being CHs is obtained by the BS using the GA based on the minimum energy needed to complete one round. The preparation phase is essential only in the first round. The installation and steady-state phases in each round are the same as those of LEACH. Similar to this work [37], use the GA to enhance LEACH. They also add an additional preparation phase. The optimal probability of nodes being CHs is achieved by using the GA for reducing the total energy consumption. The preparation phase is performed only once before the set-up phase of the first round. Their simulations confirm the effectiveness of LEACH-GA to produce optimal energy consumption, and an extension of the network lifetime. In fact, these works ignore important factors such as the traffic load to the BS, and the adequacy of residual energy to the needed transmission energy. It differs from ICSI again as it acts to improve the CH-election step versus the CH-selection step using ICSI. Additionally, the work in [34] further uses the GA to improve the quality of service (QoS) parameters of LEACH to discover the optimal routing path using its fitness function. The GA helps to find the optimal route by using its fitness function according to the equality of the residual energy of the CH to the average of the residual energy of all nodes. Their optimized method is effective, as can be seen from their results, but it ignores the important effect of the traffic load, and the adequacy of the residual energy for the needed transmission cost. Finally, the work by [38] introduces a GA-based optimized approach to enhance the CH-election step based on the distance and residual energy, which are considered as limitations by the current work. Their objective function depends on three fitness functions to evaluate the residual energy of the elected CH proportional to the residual energy of the surrounding nodes, and the distance between the CH and the BS. Table 1 is presented to summarize the related optimized methods used to enhance LEACH. In fact, the main motivation of this work is the limited number of efforts that have tried to optimize the clustering formation (CH selection) step. This research seeks to implement a custom fitness function to choose the optimal values of the parameters that affect the selection of the CH that achieves the lowest consumed energy. Although a few works have addressed the cluster formation phase, the present work is the first to focus on the competition in selecting one CH from several CHs that invite the same node and even on the competition in deciding which nodes should be invited by each elected CH.