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ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks
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.
TLQ-Geo: a two-level q-learning-based geographic routing protocol for flying ad hoc networks
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.