Machine-Learning-Assisted Object Monitoring Supported by UWOC for IoUT

The deployment and maintenance of the underwater sensor network are cumbersome and require a significant amount of manual labor, resulting in reduced efficiency and an increased likelihood of errors. This study presents a compact underwater wireless sensor network that use multiple diversity gain-enabled relays to receive and transmit data from sensor nodes to the gateway. The relay is powered by employing combining techniques, such as equal gain combining (EGC), majority logic combining (MLC), and selection combining (SC) to augment its performance. The sensor node is fitted with an underwater optical wireless communication (UWOC) module, which wirelessly transfers the inertial measurement unit (IMU) sensor data to the nearest relay. The EGC, MLC, and SC have achieved packet error rates of 26%, 28%, and 30%, respectively, at a transmission rate of 0.5 Mb/s. The accelerometer and gyroscope data collected from multiple fish, including the sensor readings of roll, pitch, yaw, speed, and dynamic acceleration, are utilized to train long short-term memory (LSTM), spatial attention, recurrent neural network (RNN), Transformer, and gated recurrent unit (GRU) machine learning models. The models are capable of predicting the particular fish’s steady, low, medium, and high acceleration states. The LSTM, spatial attention, RNN, Transformer, and GRU ML models achieved training accuracy rates of 79.06%, 93.86%, 91.07%, 87.38%, and 68.16%, respectively.