In Flying Ad hoc Networks (FANETs), coordination and cooperation among nodes are important for efficient data transmission. Cooperation among the nodes hinges on the node behavior and the behavior of the node can be quantified using the concept of trust. Trust helps in segregation of non-cooperative and malicious network nodes, thus increasing the reliability of information exchanged among nodes. In this paper, a Trust Based Clustering Scheme (TBCS) has been proposed for FANETs. TBCS use a multi-criteria fuzzy method for the classification based on the node’s behavior in the fuzzy and complex environment. The proposed scheme makes use of Takagi–Sugeno–Kang fuzzy inference method. The reward and punishment mechanism has been introduced to convert the node’s behavior into trust, and to segregate malicious and misbehaving nodes in the FANET. Furthermore, a secure Cluster Head has been selected based on calculated trust values that is responsible for communication with ground control station and inter-cluster communication. TBCS is compared with existing trust models and the experiment results revealed that the proposed TBCS model has high accuracy, better performance, and adaptability in FANETs.

在飞行自组织网络（FANET）中，节点之间的协调与协作对于有效的数据传输非常重要。节点之间的协作取决于节点行为，并且可以使用信任的概念来量化节点的行为。信任有助于隔离非合作和恶意网络节点，从而提高了节点之间交换信息的可靠性。在本文中，已经提出了用于FANET的基于信任的群集方案（TBCS）。TBCS根据模糊和复杂环境中节点的行为，使用多准则模糊方法进行分类。提出的方案利用了Takagi–Sugeno–Kang模糊推理方法。引入了奖惩机制，以将节点的行为转换为信任，并隔离FANET中的恶意节点和行为异常的节点。此外，已基于计算出的信任值选择了安全的簇头，该信任值负责与地面控制站进行通信以及集群间通信。将TBCS与现有的信任模型进行比较，实验结果表明，所提出的TBCS模型具有较高的准确性，更好的性能以及在FANET中的适应性。