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This study proposes a Q-learning-based mobility-aware tree routing protocol (QMTR) for WBANs integrated with vehicular ad hoc networks (VANETs) to support reliable transmission of passengers’ health data to remote medical centers.
The performance of AxisGeo was evaluated against A-Geo, UF-GPSR, and GPSR under three simulation scenarios involving variations in UAV density, UAV speed, and network traffic and indicates that AxisGeo provides improved routing performance across the evaluated FANET scenarios.
A comprehensive review of published RL-based control studies using the Quanser Aero, Aero 2, 3-DOF Helicopter, and Autonomous Vehicles Research Studio (AVRS) platforms identifies standardised evaluation procedures, reproducible reporting, safety-aware RL, interoperable software interfaces, and higher-fidelity digital t...
Simulation results under varying UAV density, mobility, and traffic load demonstrate that 3D-VRTGR consistently outperforms GPSR, UF-GPSR, and A-Geo and confirm the robustness and efficiency of 3D-VRTGR in highly dynamic FANET environments.
A two-level Q-learning-based geographic routing protocol called TLQ-Geo for FANETs, which significantly reduces convergence time and computational overhead and integrates hierarchical decision-making with adaptive reinforcement learning.
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