Underwater channels are considered challenging
media in communication due to the harsh nature of such
environments. However, dynamic transmission can assist in
finding sub-optimal solutions by adaptively changing the
employed techniques, e.g. the forwarding scheme between nodes
and the transmitted signal intensity control, to compromise for the
instantaneous fluctuations in various underwater environments.
Additionally, Machine Learning (ML) techniques can provide
appropriate solutions for various problems e.g. routing, resource
allocation, and energy-efficiency to further enhance the quality of
the communication systems. In this paper, we propose a novel
dynamical transmission framework for multi-hop Internet of
Underwater Things (IoUT) and underwater networks to fit for
various conditions. The proposed framework employs a heuristic
forwarding scheme selection approach beside an adaptive
transmission signal intensity method. We also propose a decision-
tree based ML-model that adaptively learns the proper
forwarding method beside the appropriate amount of the
transmitted signal intensity for each relay node to minimize the
transmission error rate and the power consumption depending on
numerous parameters e.g. node location, link reliability and
certain water quality metrics such as water temperature, depth,
and pH measurements. The model achieves remarkable accuracy
for training and testing patterns beyond the 99%. |
