Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

Abstract

Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems

“…In particular, the decision making selection strategy is achieved through reinforcement learning. Authors in paper [35] propose a comparison among machine learning techniques, properly tested on real underwater measurement gathered near the Gulf of Incheon, South Korea, to predict the most adequate communication parameters to mitigate the high propagation loss and drastic channel fluctuation problems.…”

TANVEER: Tri-Angular Nearest Vector-Based Energy Efficient Routing for IoT-Enabled Acoustic Sensor and Actor Networks (I-ASANs)

“…Ultimately data packet delivery and throughput is decreased and an extra energy tax is used for rescue the network especially in a large-scale area network like under water. Many secondary and rescue nodes are introduced inside the sink to recover the data [ 7 , 8 ].…”

“…In [ 8 ], the watchman nodes are introduced to the recovery of lost data from the empty region of the underwater network in which the watchman was continuously monitoring the node’s location and its energy status. However, these two approaches follow the monitoring approach, where the status of every node is checked by watchman nodes and corresponding angles of forwarding.…”