# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 1 Introduction
As the cluster's life span increases, the model's ability to save money and time increases. Clustering algorithms significantly impact a network's performance, and to ensure the cluster's long-term viability, the CHs must be carefully chosen. Networks may be divided into clusters using artificial intelligence algorithms [10]. The fundamental downside of these strategies is their high computing complexity, which prevents them from providing the best outcomes. They take too long to get to the best possible work [7]. Clustering in FANETs cannot be carried out using costly approaches due to the dynamic nature of the environment and the low processing capability of UAVs. UAV energy usage is also heavily influenced by transmission power options. Transmission power and energy use go hand in hand. SNs will use more energy if we choose a transmission power of either high or low [4]. Transmission power should not be as high as possible while not as low as possible to minimize energy wastage. Evolutionary algorithms (EA) are based on the theory of biological evolution [7]. EA includes genetic algorithms/programming, evolutionary techniques, and learning classifier systems. When other strategies fail, evolutionary algorithms can provide a viable alternative. Evolutionary approaches are frequently accepted when faced with issues that seem impossible [8; 11] to solve. Even if EA is computationally costly, a near-optimal solution to an unsolved case is acceptable. Node clustering using evolutionary algorithms may be carried out well in FANETS and VANETS systems. To identify a solution to the problem, an environment will be created that allows for the evolution of viable solutions [12]. It is feasible to find the best possible solution for the issues related to building environments through these biological algorithms. Nodes are aggregated, and their geographic locations are shared to tackle the scalability problem. Load balancing ensures that resources are effectively used in each cluster. One of the best clustering methods is the moth flame optimizer, which provides the ideal number of clusters. Insects that resemble butterflies are called moths and currently, there are about 16,000 moth species known to science. The larvae of moths, as with those of other insects, develop into cocoons as they mature. Moths fly at night and use the moonlight to find their way around [13]. An intelligent moth flame optimization-based clustering for FANET was developed to reduce energy consumption and expand the coverage area of SNs. This study proposes EECP-MFO, which employs a moth flame and a variation of the K-Means Density clustering method to choose CHs for usage in a distributed computing environment. K-Means Density is utilized in conjunction with the MFO algorithm for the first selection of centroids/CH. The original K-Means Density algorithm only considers one parameter, namely the degree of the neighborhood [13]. However, the EECP-MFO algorithm considers two additional factors, namely the energy level and the distance between the neighbors, to select the ideal CH. EECP-MFO extends the lifetime of a cluster while simultaneously reducing its energy usage [14; 15; 16]. It also saves energy by efficiently choosing the transmission power of nodes according to operational needs, reducing the network load [17; 18; 19; 16]. In terms of cluster construction time, cluster longevity, the chance of success [20; 21; 22; 23; 24], and energy consumption, EECP-MFO beats the ant colony optimization [17; 20], PSO-particle swarm optimization [18], and grey wolf optimization-based clustering models [14; 25]. In a nutshell, the originality and contribution of this article are summarized in the following section.