MARLIN-Q: multi-modal communications for reliable and low-latency underwater data delivery
This paper explores the smart exploitation of multi-modal communication capabilities of underwater nodes to enable reliable and swift underwater networking. Following a model-based reinforcement learning approach, we define a framework allowing senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. The choice is also driven by the quality of the communication to neighboring nodes over time, thus allowing nodes to adapt to the highly adverse and swiftly varying conditions of the underwater channel.