A TRUSTED LIGHTWEIGHT COMMUNICATION STRATEGY AND A LOCATION AND VELOCITY PREDICTION-ASSISTED FOR FANETS

Unmanned aerial vehicles (UAVs) are used as communication nodes in the Flying Ad hoc Network (FANET), a new breed of resource-constrained Mobile Ad hoc Network (MANET). These latter offer a broad range of applications and services by adhering to a predetermined route known as their "mission." Without losing any generality, the FANET's services and applications are built around the distribution of data and information in a variety of formats, including but not restricted to images, videos, status updates, warnings, and so forth. FANET therefore requires a content-centric communication method, such as information-centric networking. The issues with the traditional TCP/IP-based Internet are addressed by ICN. Because of their inherent security features and interest/data-based communication, Content-centric networking and Named Data Networking are two of the most well-known and extensively used ICN implementations. Simultaneously, the intermediate UAVs choose whether to check the data authenticity or not, following their subjective belief on its producer’s behavior and thus-forth reducing the computation complexity and delay. Simulation results show that our proposal can sustain the vanilla NDN security levels exceeding the 80% dishonesty detection ratio while reducing the generated end-to-end delay to less than 1 s in the worst case and reducing the average consumed energy by more than two times. Later, we will implement the location and velocity information predicted by the Kalman-filter, we offer a unique FANET clustering algorithm that can effectively create and maintain a hierarchical structure in FANETs when mobile nodes move at high speeds. The number of clusters is first calculated using the Silhouette coefficient, and nodes are then grouped into clusters using the k-means++ technique. In highly mobile environments, a Kalman filter is used to anticipate the locations and velocities of each node with respect to external disturbances. Relative distances and speeds are taken into account for clustering, and the previously chosen cluster heads (CHs) are used to initialize the current centroids.

Keywords: FANET Communications, Network Clustering, Kalman Filter Prediction, K-means Algorithm, Cluster Stability Metric, NDN, VANET, Trust, Energy Efficiency.