  Life on earth depends on healthy oceans, which supply a large percentage of
the planet's oxygen, food, and energy. However, the oceans are under threat
from climate change, which is devastating the marine ecosystem and the economic
and social systems that depend on it. The Internet-of-underwater-things
(IoUTs), a global interconnection of underwater objects, enables
round-the-clock monitoring of the oceans. It provides high-resolution data for
training machine learning (ML) algorithms for rapidly evaluating potential
climate change solutions and speeding up decision-making. The sensors in
conventional IoUTs are battery-powered, which limits their lifetime, and
constitutes environmental hazards when they die. In this paper, we propose a
sustainable scheme to improve the throughput and lifetime of underwater
networks, enabling them to potentially operate indefinitely. The scheme is
based on simultaneous wireless information and power transfer (SWIPT) from an
autonomous underwater vehicle (AUV) used for data collection. We model the
problem of jointly maximising throughput and harvested power as a Markov
Decision Process (MDP), and develop a model-free reinforcement learning (RL)
algorithm as a solution. The model's reward function incentivises the AUV to
find optimal trajectories that maximise throughput and power transfer to the
underwater nodes while minimising energy consumption. To the best of our
knowledge, this is the first attempt at using RL to ensure sustainable
underwater networks via SWIPT. The scheme is implemented in an open 3D RL
environment specifically developed in MATLAB for this study. The performance
results show up 207% improvement in energy efficiency compared to those of a
random trajectory scheme used as a baseline model.
