Discrete Reptile Search Algorithm-Based Clustering Technique for Flying Ad Hoc Networks

Given the continually evolving applications of Flying Ad Hoc Networks (FANETs), sophisticated clustering methods become more crucial for preserving network stability despite the dynamic flight characteristics of Unmanned Aerial Vehicles (UAVs). This guarantees stable communication for the seamless exchange of critical data and collaboration within FANETs. Motivated by this, we introduce a novel Discrete Reptile Search Algorithm-Based Clustering method (DRSAC) for FANETs. The Reptile Search Algorithm (RSA) is a recent meta-heuristic optimizer that yields superior results in a variety of optimization problems, with properties like minimal parameter tweaks, strong optimization stability, and ease of implementation. DRSAC efficiently organizes nodes into clusters by employing the discrete version of RSA, enhancing communication efficiency and adaptability in dynamic airborne environments. It serves as a robust solution for cluster formation and maintenance by avoiding frequent cluster reconfigurations. The best count of clusters in the FANET is decided by taking into account the constraints related to network bandwidth and node coverage. By incorporating a novel mechanism for determining more stable and efficient Cluster Heads (CHs), DRSAC extends the cluster lifetime, decreases latency, and maximizes the data delivery. This research contributes to advancing the reliability and effectiveness of FANETs, positioning DRSAC as an effective clustering solution for FANET applications. The simulation results demonstrate that DRSAC exhibits efficient performance across various metrics, including cluster lifetime, data transmission rate, and energy efficiency.