  In this paper, we present Context-Adaptive PARRoT (CA-PARRoT) as an extension
of our previous work Predictive Ad-hoc Routing fueled by Reinforcement learning
and Trajectory knowledge (PARRoT). Short-term effects, as occurring in urban
surroundings, have shown to have a negative impact on the Reinforcement
Learning (RL)-based routing process. Therefore, we add a timer-based
compensation mechanism to the update process and introduce a hybrid Machine
Learning (ML) approach to classify Radio Environment Prototypes (REPs) with a
dedicated ML component and enable the protocol for autonomous context adaption.
The performance of the novel protocol is evaluated in comprehensive network
simulations considering different REPs and is compared to well-known
established routing protocols for Mobile Ad-hoc Networks (MANETs). The results
show, that CA-PARRoT is capable to compensate the challenges confronted with in
different REPs and to improve its Key Performance Indicators (KPIs) up to 23%
compared to PARRoT, and outperform established routing protocols by up to 50 %.
