Intégration de l’Apprentissage Profond pour l’Optimisation du Routage dans les Réseaux FANETs

Abstract

This graduation thesis falls within the domain of mobile wireless networks, spe cifically Flying Ad Hoc Networks (FANETs), and proposes an intelligent approach to improve the stability of drone communications. The main objective is to design, mo del, and evaluate a predictive routing protocol capable of distinguishing stable links from unstable ones, in order to ensure better Quality of Service (QoS).The work be gins with an in-depth study of drone architectures in FANETs, followed by a critical analysis of conventional routing protocols and QoS management methods. Based on this analysis, we proposed an enhancement of the P-OLSR protocol by integrating a model based on Machine Learning and Deep Learning techniques to predict the Ex pected Transmission Count (ETX) metric, a key indicator of link quality.The proposed model was trained and validated using a dataset simulating FANET link characteris tics. A series of simulations was conducted to evaluate the performance of our protocol in terms of connection stability, throughput, latency, and routing reliability.The final performance of the model on the test set shows an accuracy of 98.58%, with a pre cision of 98.37%, a recall of 98.38%, and a low loss of 0.0612, demonstrating strong generalization ability.Simulation results also confirm the superiority of the proposed protocol, with a stability rate of 98.8%%, a reduced loss of 3%, a useful transmission rate of 97%, a resilience of 6%, and 92% active links, thus outperforming OLSR and Predictive-Optimized Link State Routing (P-OLSR).