# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 2 Related Work  
Although a FANET is a sub-domain of ad-hoc networks, however, the unique characteristics of FANETs do not allow one to use MANET and VANET clustering techniques directly. Rather new techniques are to be developed or variants of existing techniques are required which take into account the unique attributes of a UAV network. [PERSON] et al. [10] used the multi cluster-based approach, in which each cluster consists of a fixed number of UAVs and one UAV is elected as a CH. At the start, a node information message is exchanged among neighboring UAVs. UAVs are grouped into different clusters based on the \"zone ID\" field of the node information message. Each node in the cluster maintains a link quality table which contains the distance, SNR and delays to the neighbors. CH election is based on link quality information and the node with the best link quality is elected as a CH. [PERSON] et al. proposed a novel routing protocol called Cluster-based Location Aided Dynamic Source Routing (CBLADSR) [11]. CBLADSR detects the CHs based on three parameters, i.e., relative velocity, energy level, and degree of connectivity. A cluster member having low relative speed, higher energy level and a large number of neighbor nodes will be elected as a CH. Member nodes maintain a neighbor table, listing all nodes in that particular cluster. CBLADSR uses short range and long range transmission for intra- and inter-cluster communication, respectively. [PERSON] and [PERSON] proposed a mobility prediction clustering algorithm [12] for UAVs. Each node maintains a neighbor table for its one-hop neighbors. The neighbor table also contains the probability that a node will remain in its table. This probability is computed using a dictionary tree structure. Link expiration time (LET) is predicted using this probability and moment of the neighbor node. A weight is calculated for each node using the neighbor's LET probability and degree. A node with the highest weight will be elected as a CH. [PERSON] et al. [13] and [PERSON] et al. [14] also discussed the future direction of ad-hoc networks by using the different nature of algorithms. These suggestions are also based on the mobility pattern of nodes. [PERSON] et al. [15] discussed the routing method by using the fuzziness in wireless multi hop networks. [PERSON] et al. [16] proposed a clustering method for the selection of centroid used in the domain of recommendation system by using the different datasets. Artificial intelligence techniques, such as Ant Colony Optimization (ACO) [17], Particle Swarm Optimization (PSO) [18] and Grey Wolf Optimization (GWO) [19], have also been used to perform clustering in ad-hoc networks. These techniques generate a population of solutions. Each solution contains the all possible nodes that can be elected as a CH. These solutions are updated iteratively based on distance from local best and global best. [PERSON] el al. proposed an ACO-based clustering algorithm (CACONET) [20], which uses the ACO to find the optimal cluster. ACO uses the social behavior of ants in finding some foods or solution to some common problem. Vertices in the search space are the CH and each complete round gives the collection of CHs from a given environment. CACONET selects some vertices randomly and adds them into the cycle. Later it will add more vertices to the round, subject to some evaluation criteria and heuristic value. CACONET calculates the probability of selection for each node and uses the Roulette Wheel selection method to select a node to become vertices in the route. After selecting vertices, ants move over the edge and evaluate the next vertices. This process is repeated until there are no vertices left to be added to the tour. CACONET use the two objective functions: delta difference and Euclidean distance, to evaluate the fitness of each cycle. Another ACO-based algorithm called ACONET [21] has also been used for the same problem. GWOCNETs [22; 23] mimics the leadership hierarchy and hunting mechanism of grey wolves.