# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 1 Introduction  
A FANET is a hybrid model of VANET (vehicular ad hoc networks) and a mobile ad-hoc network (MANET). In FANETs, UAVs are the network nodes, and the peer-to-peer communication model is used in communication. FANETs, MANETs, and VANETs have many similar properties. However, there are also some significant variances. These characteristics make FANETs incredibly fast-moving and capable of coping with various 3D environments. The FANET's changing structure and hostile surroundings make networking and communication difficult [1, 2]. While in the air, the nodes have a clear line of sight and are pretty far apart. UAVs have limited resources (batteries, processing power, and bandwidth), which also hampers UAVs working efficiency. UAVs need to devise a communication system with minimal routing overhead, high throughput, and little processing complexity to make the most reliable working model [2]. We can see that communication inan ad-hoc network can be accomplished via various methods. There are three main types of routing: proactive, reactive, and cluster-based. Routes to other nodes in the network are preserved in routing tables in proactive routing. Each time a message is exchanged, a new routing table is created. We can see a FANETS network in Figure 1 used for monitoring and searching applications. On the other hand, reactive routing protocols do not keep track of the routing table. Every time a node needs to communicate data, it searches for a new route. It helps to find a new path once the present one is disrupted [2; 3]. When nodes constantly move around, maintaining the current routing table or finding a new way is a substantial communication overhead. Network throughput is cut down, extra delays are brought in, and the limited battery power of the UAV is wasted as a result of these communication overheads [4]. There is a solution to the problem of limited resources in FANETS, which is clustering. Clustering is a method for grouping nodes based on their geographic location. It contributes to the network's scalability, efficiency, and throughput [4; 5] as the UAVs' energy consumption will be reduced due to the minimal routing overhead. There is a cost associated with clustering, although it is substantially lower than the cost of the other approaches. When a network is clustered, it is broken down into several distinct groups or clusters [6; 7]. There is a leader or \"cluster head\" (CH) for each cluster, which is in charge of communication between clusters and inside clusters, as seen in Figure 1. CH serves as a first-hop node that takes the message to its final destination to facilitate routing [8]. Cluster creation and maintenance consume some of the computational network radio resources. In the early stages of cluster development, nodes communicate information about their immediate area, including node ID, location, residual energy, etc. are computed. Control messages are used to provide this information to other nodes. As a result, a portion of the network's radio resources is dedicated to clustering [8]. A CH is elected in each cluster during network clustering, where nodes can execute calculations to organize neighboring nodes into clusters. The development of clusters and the selection of CHs are critical to the stability of the cluster structure [6; 7]. It is possible to alter the design of a cluster by repositioning cluster members (CMs). It is imperative that each CH regularly broadcasts its presence to its CMs and that each CM responds with its current status so that any changes to the cluster structure may be tracked. The radio resources of the network are also used for this cluster maintenance signaling. Overhead for cluster creation and  
Figure 1: FANETS Network. maintenance is defined as transposing of control messages. In addition to using radio resources (such as channel bandwidth), this overhead is also a drain on the UAVs' energy. A clustering model's performance is evaluated using several parameters, including the cluster's lifespan and building time [4; 9].