Sine-chaotic Starfish optimization based dynamic clustering framework for energy-efficient flying Ad-Hoc networks communication

To ensure dependable communication between multiple unmanned aerial vehicles (UAVs), the concept of Flying Ad-Hoc Network (FANET) emerged, allowing these autonomous aerial vehicles to form flexible, self-configuring, and resilient networks without central control, creating higher mobility, rapid topology changes, and limited battery resources, which pose serious challenges for effective communication. Clustering is a recognized and efficient approach to increase network scalability, minimize routing overhead, and preserve energy in FANETs. Recently, bio-inspired optimization algorithms have demonstrated significant attention in managing the challenges of cluster formation and cluster head (CH) selection within rapidly changing scenarios. This study presents a novel Sine-Chaotic Starfish Optimization-based Dynamic Clustering Framework for Energy-Efficient Flying Ad-Hoc Networks Communication (SSODC-FANET), aiming at improving CH selection and overall network stability in highly dynamic FANET environments. The proposed SSODC-FANET model operates in two stages: cluster formation and cluster maintenance. During cluster formation, cluster heads (CHs) are selected using the sine-chaotic starfish optimization algorithm (SC-SFOA) by considering intra-cluster distance, residual energy, number of one-hop neighbors, relative mobility stability index (RMSI), and predictive link sustainability factor (PLSF). Integrating an SC-SFOA into the clustering process enhances CH selection by improving stability and energy efficiency compared to conventional methods. In the cluster maintenance phase, the model updates backup CHs and applies damage detection along with cluster merging to handle link failures and mobility changes effectively. This adaptive design reduces frequent re-clustering, improves energy efficiency, and extends network lifetime. A wide range of simulation analyses was executed to ensure an enhanced performance of the SSODC-FANET model. The comparative result analysis reported the betterment of the SSODC-FANET method on recent approaches in terms of different evaluation metrics, demonstrating its efficiency for FANET communication scenarios.