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Resource-limited mobile IoT networks are a dynamic, and uncertain wireless communicating system. In such systems, the standard RPL routing protocol cannot select long-lasting communication links due to not employing mobility-aware metrics, e.g., direction and speed of movements. While several classical heuristic approaches exist to improve PDR in RPL-based mobile networks, their solutions cannot adapt to alterations of the mobile topology. Hence, in this paper, by mapping the routing problem in mobile and resource-limited networks into an infinite-time horizon MDP, an energy-aware and reliable RPL-based routing mechanism based on Q-learning is proposed to improve PDR in mobile IoT networks. This routing mechanism, which is called QUERA, utilizes mobility and quality-aware metrics, including Time-to-Reside (TTR), ETX, and RSSI. Furthermore, QUERA probes and maintains stable candidates …