A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

Abstract

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.

Topology Duration Optimization for UAV Swarm Network under the System Performance Constraint

“…Representative Algorithm Advantages convergence speed lowest ID clustering [14] K-means clustering [15,16] Low algorithm complexity and fast conversion speed energy consumption EALC [17] SIC [18] BIC [19] IWLC [20] CSBWOA [21] Energy-efficient oriented: reduce energy consumption of key nodes and consequently improve the network performance network performance DOCG [22] CDSTC-JO [23] DCM [24] Directly adopted the network performance as optimization object Lowest ID method [14] is a typical low-complexity algorithm that can be used to rapidly construct the swarm topology. In Lowest ID method, ID numbers are randomly assigned to all network nodes and several nodes with the lowest ID number are selected as the cluster head.…”

“…Reference [21] proposes a Binary Whale Optimization (BWOA) algorithm to select optimal cluster heads by jointly considering node residual energy, intra-cluster node distance, inter-cluster node distance and the cluster size balance. The scheme of cluster size balance is used for achieving a balance number of cluster member for different clusters, which will consequently balance the energy consumption and data transmission for all cluster head nodes.…”

“…Many researches on the construction methods of swarm network topology have been proposed. Generally, according to different construction criteria, we can classify the construction methods of network topology of swarm system into three categories: (i) under the criterion of topology construction speed [14][15][16]: the first category is the methods aiming at topology construction speed, which mainly focuses on the low complexity and fast convergence of algorithms, but considers less about the system performance after construction; (ii) under the criterion of energy consumption [17][18][19][20][21]: based on the consideration that UAV swarm is sensitive to energy consumption, the second category takes node energy consumption as the optimization target in the step of topology construction; (iii) under the criterion of network performance [22][23][24]: the network topology is essentially constructed to serve the network interaction of UAV nodes, so the third category of methods perform the topology construction in terms of the network performance (network throughput, delay, etc.) under the constructed topology.…”

“…Therefore, a reliable swarm network topology has a vital influence on the performance of the swarm network and even the operation of whole swarm system. It is worth mentioning that the network topology stability is considered in reference [17][18][19][20][21]. To be more precise, most of researches considered enhancing topology stability from the view of reducing the energy consumption of nodes (improving the node survival time).…”