Adaptive Routing Design for Flying Ad Hoc Networks: A Joint Prediction Approach.

Flying Ad Hoc Networks (FANETs) are essential for a wide range of military and civil applications as they significantly reduce mission duration and improve coverage compared to using a single Unmanned Aerial Vehicle (UAV). Nevertheless, the dynamic and mobile nature of FANETs, coupled with fluctuating data traffic patterns, pose significant challenges for adaptive routing and efficient packet delivery. At present, most of the existing routing protocols for FANETs are designed based on mobility or topology information without taking into account data traffic and the associated joint decision-making approach. This narrow focus can result in poor network performance, characterized by longer delivery delay and lower delivery ratio, which can significantly impair the overall Quality of Service (QoS) in FANETs. To address these challenges, this paper proposes the Joint Prediction and Entropy weight-based (JPE) routing protocol by considering both current and future network conditions. The protocol uses a Long Short-Term Memory (LSTM) model to predict and obtain the mobility, buffer available size, and Link Expiration Time (LET) of each Neighbouring UAV (NU) in order to avoid high-mobility, high-traffic, and weak-link UAVs and establish an appropriate path. The routing decision problem is then formulated as an optimization problem and solved using the proposed Entropy Weight-based Multi-Metric (EWMM) approach to make fast and joint routing decisions. The integrated prediction and decision process considers both current and future multi-metric factors that can lead to packet loss or delay. Simulation results demonstrate the effectiveness of the LSTM-based Joint Prediction (JP) model and show that the JPE protocol outperforms the PAP and SPA protocols, improving Packet Delivery Ratio (PDR) and delay performance by 30.13% and 31.24% respectively.