UDARMF: An Underwater Distributed and Adaptive Resource Management Framework

Abstract

Providing qualified and sustainable communications is one of the key challenges for the Internet of Underwater Things (IoUTs) facing constrained energy supplements, non-stationary environments, and severe communication interference. Owing to spatial separation, several nodes can (and are often required to) make transmissions simultaneously to maximize network capacity. However, existing transmission solutions often face the dilemma between maximizing local capacity and global concurrency. We break this dilemma via UDARMF, an underwater distributed and adaptive resource management framework, which maximizes network capacity by supporting an increased number of communications in the network. It is a distributed deep multi-agent reinforcement learning framework that uses an observation encoder and a local utility network to coordinate the collaboration among underwater nodes by adaptively tuning its transmit parameters. We designed experiments to compare UDARMF with baselines in network capacity, concurrency, and energy efficiency. Extensive experiments were conducted to find the appropriate hyperparameters to achieve the optimal network performances. We also analyze the performance of UDARMF and baselines over diverse communication and lifetime requirements, communication environment, and energy storage. Simple closed-form approximations of UDARMF are given to reveal that an energy-constrained network’s capacity increases with available energy, following a linear trend on the logarithmic scale. Experimental results demonstrate that compared with other methods, UDARMF achieves a much better trade-off between network capacity and concurrency, at which the lifetime requirements are satisfied. The proposed framework and the closed-form approximations are likely to become valuable tools in designing and analyzing IoUTs.