AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
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.
This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.
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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