# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 3 Proposed Methodology  
### Network Building and Nodes Positioning  
EEP-MFO is a FANET communication model that seeks to reduce the computational, energy consumption, and communicational cost to the absolute minimum. When the clustering mechanics are kept simple, the computational overhead may be lowered, and the communicational overhead can be reduced by lengthening the cluster's lifetime. Once the UAVs begin flying, network creation begins [11]. In addition to task-oriented sensors, unmanned aerial vehicles (UAVs) are equipped with GPS and height sensors. These two pieces of equipment are responsible for providing the 3D positioning information of the UAV node to the controller. As an additional assumption, we suppose that UAVs have three discrete transmission grid levels corresponding to communication ranges of 1000 to 3000 m for each of the four discrete power levels [12]. Nodes begin by selecting the highest power level available. Later on, nodes can choose the power level that is best appropriate for them based on their position and the position of their neighbors [22]. This strategy is used to conserve the energy of the node. Figures 6 and 7 illustrate our proposed EECP-MFO algorithm's workflow. Given fitness values, random positions in solution space (m \(\times\) n) are allocated to each moth and their respective moth arrays. Arrays and matrixes are produced in the same way as flames. The flame matrix contains the best value for the moth discovered [4]. Every time the best moth against its flame is found, the procedure iterates until it reaches the ideal number of search space flames. After each successful iteration, the moth position is updated. The quest for an optimal solution is similar to a moth's journey across the solution space. The fitness values are kept in the order of the m \(\times\) n solution space, starting with the random location provided to each moth during the startup phase and continuing with the moth array. Similarly, the flame matrix and related array are created. The flame matrix contains the value of the moth that has been discovered to be the best so far [24]. Moths are moved around in a solution space to find the best solution or end the search procedure. Figure 6: Clustering structure. This procedure took advantage of the dimension of the search space's lower bound and upper bound boundaries. It is also used to determine the fitness value of each moth depending on their location in the search space [11; 12; 24]. Creating a fitness matrix is an iterative process; updated values are placed in the matrix in ascending order as the process progresses. The fitness matrix supplies the lower fitness value for each moth [13]. It is calculated by combining the moth's location in the search space with its fitness value, and this value is used to update the moth's position in the search space as the moth moves through the search space. Convergence was achieved using a linear decreasing factor \"x\"  
Figure 7: Flowchart of proposed algorithm. for the ideal solution. Selecting a Cluster Head (CH) follows the creation of a cluster, as seen in Figure 6. The selection of the CH can be made based on several factors, including the number of nodes, node density, residual energy, and load balancing factor [22; 26]. Weights are assigned to these parameters before sending them to the fitness function. The selection process in EECP-MFO relies heavily on using a fitness function. Extending the cluster's lifespan by picking the optimal CH, reducing network energy, and avoiding excessive broadcast overhead [26].