# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
###### Abstract
In this way, the volume of transmitted data is reduced, and the energy consumption of the network is lowered. In addition, collisions between transmitted packets are also reduced, resulting in better throughput under high loads and conservation of communication bandwidth [6]. Much experimental effort has been exerted to fulfil this requirement and to achieve good power preservation in cluster-based WSNs. Consequently, low-energy adaptive clustering hierarchy (LEACH) [7] was initially introduced as the most essential clustering protocol for WSNs; it is identified as a circulated, single-hop clustering protocol for reducing power consumption and extending network lifetime. The adaptive process of LEACH that occurs in each round has two stages. The first stage, the'setup phase', is subdivided into two steps: the CH election step, where the CHs are randomly elected; and the CH selection step, where the formation of each cluster starts by allocating each node to the closest CH. In the second stage, the'steady-state phase', the collected data are transferred to the sink node via CHs. During the setup phase, each node employs a probabilistic technique at each round to determine whether it will be a CH in each round. As the node succeeds in being a CH, it broadcasts an invitation message to join its clusters. At the end of each round, non-CH nodes select the closest CH and send an acknowledgement message to join it. Then, the CH generates a schedule for each node to send its data. The optimal election and selection of CHs directly affect the network lifetime, so they are considered key steps in conserving the availability of WSNs [8]. In fact, LEACH paved the way for the introduction of many works that attempted to implement LEACH, resulting in the discovery of many drawbacks of LEACH at each phase. Concerning the clustering formation (CH selection) step, LEACH has the following limitations:  
* Although each node should be able to be linked with its CH with minimal energy, LEACH does not consider any factors other than distance; it disregards all other influential factors that have a direct effect on the communication cost, such as the location, bandwidth, and traffic load. * The adequacy of the remaining energy is not considered when selecting the CHs for nodes. It is defined as the ability of the remaining power of the node to cover the required transmission cost to the CH. * The cluster size is another important factor that influences the energy consumption and consequently the lifetime of the whole network [9]. The cluster size is disregarded by LEACH. The selection of the optimal CH for each node and the optimal size of each cluster are hard problems that hinder the use of deterministic algorithms. Therefore, using optimization techniques can be more suitable here to optimize the system performance and achieve the lowest energy cost among different numbers of choices [10; 11]. On the other hand, optimization techniques are used to drive the best possible solutions of the decision under a certain group of constraints according to a selected optimization function. Several optimization techniques that are inspired from the biological world have been introduced to solve complex problems in many domains, especially WSNs [12]. Genetic algorithms (GAs), evolutionary algorithms (EAs), particle swarm optimization (PSO), and ant colony optimization (ACO) are examples of these techniques. Notably, the GA has an extremely wide range of choices to optimize the clustered WSN for minimizing the consumed energy by estimating the optimal solutions [13]. In fact, the GA is a known heuristic technique that selects the optimal solution through producing several possible solutions [14]. The process of the GA includes three essential tasks. The first task is the initialization, where it begins by generating the basic population randomly. The second task is the fitness task, where scoring each solution base is qualified. This task uses a repetitive activity involving crossover and selection operators.