Adaptive FANET Clustering Integrating PSO-Optimized DBSCAN for 3D Dynamic Topologies

In Flying Ad Hoc Networks (FANETs), building a stable and efficient communication backbone faces significant challenges due to the highly dynamic three-dimensional topology, rapid node mobility, and frequent link interruptions. To overcome the inherent limitations of density-based spatial clustering algorithms like Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which heavily rely on two static parameters, the neighborhood radius ɛ and the minimum number of points MinPts, proposes PSO-DBSCAN, a novel adaptive clustering protocol that integrates the Particle Swarm Optimization (PSO) algorithm with DBSCAN. The proposed method consists of three stages: Firstly, PSO dynamically optimizes DBSCAN parameters based on the current network topology before each clustering period. Secondly, DBSCAN is executed using the optimized parameters to form densitybased clusters. Finally, a second round of PSO is run within each cluster to elect the optimal cluster head based on a multi-objective fitness function that comprehensively considers residual energy, mobility, node degree, and distance to the cluster center. Simulations conducted on the NS-3 platform demonstrate that PSO-DBSCAN offers significant advantages over benchmark algorithms such as Weighted Clustering Algorithm (WCA) and DBSCAN in terms of clustering quality, cluster stability, control overhead, energy efficiency, and network lifetime. More importantly, the proposed scheme exhibits excellent adaptability to sudden topological changes, providing a robust and scalable solution for dynamic three-dimensional FANET environments.