Machine Learning Based Dynamic Cooperative Transmission Framework for IoUT Networks

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%.