A Trust Model with Fitness-Based Clustering Scheme in FANETs

Nowadays, Unmanned Aerial Vehicles (UAVs) play an indispensable role in many industries, exhibiting distinctive value and potential. However, there are lots of common threats in UAV networks, such as black hole attacks and message tampering. Additionally, as the size of UAV Flying Ad hoc Networks (FANETs) gradually increases, the network overhead and latency also increase. To address these issues, we propose a Trust Model with Fitness-Based Clustering Scheme (TMFCS) in FANETs. TMFCS integrates the trust model and clustering scheme, aiming to improve network security and reduce network overhead. Specifically, TMFCS focuses on unintentional abnormal behavior (e.g. packet loss due to low energy) and message characteristics (e.g., timeliness, accuracy, and integrity) in the trust evaluation. In addition, TMFCS integrates the fitness-based clustering scheme. The scheme selects cluster heads based on density, trust, and energy, which can effectively ensure clustering security and reduce the network overhead. Meanwhile, TMFCS introduces a cluster maintenance phase to improve network topology stability, by reducing the clustering times. Extensive experiments have shown that TMFCS has higher detection performance than other baseline models, while ensuring clustering security and topological stability.