Underwater wireless optical communication (UWOC) performance is negatively impacted by challenges such as water turbulence, restricted range, and misalignment. These challenges can impact the feasibility of large-scale deployment. In comparison to conventional acoustic and radio frequency (RF) communication, UWOC can play a pivotal role in connecting various Internet of Underwater Things (IoUT) devices. This research presents the design and evaluation of “aqua-sense”, a relay-based UWOC system aimed at enhancing communication link performance and expanding the reception area of the optical receiver. To harness diversity gain, the optical relay incorporates combining techniques such as Equal Gain Combining (EGC), Majority Logic Combining (MLC), and Selection Combining (SC) to enhance its performance. Additionally, the optical relay, referred to as the “opto-relay”, is empowered by a channel-aware algorithm to further improve communication link performance. In moderately turbid water conditions with a turbidity level of 25 NTU, the aqua-sense system achieved a packet success rate of 68% at a communication link distance of up to 7.5 meters, transmitting at a rate of 0.2 Mbps. Additionally, the sensor node“, opto-sense”, achieved a consistent transmission rate of 0.5 Mbps in clear water with a turbidity of 0.01 NTU. These results remained stable even when moderate water waves with a displacement rate of 5 liters/min and air bubbles with an airflow rate of 5 liters/min were present within a 2-meter communication link range.

Recent research in the wireless communications field has focused on the reconfigurable intelligent surface (RIS), which can enhance energy and spectrum efficiency by reconfiguring radio waves to propagate in a specific direction. In this study, we applied the simultaneous transmit and reflect (STAR)-RIS to an underwater wireless optical communication (UWOC) system. In contrast to a conventional RIS, a STAR-RIS allows multiple users to transfer data simultaneously in all directions by obtaining a UWOC channel despite underwater turbulence, beam attenuation, blockage, and pointing errors. Underwater turbulence-induced fading was obtained by using exponential and generalized Gamma distributions, and numerical analysis were performed to evaluate the outage probability, bit error rate (BER), and channel capacity of a direct UWOC channel, conventional RIS assisted UWOC channel, and STAR-RIS assisted UWOC channel. The BER performance was also evaluated according to the number of reflective elements, type of modulation scheme, impact of pointing errors, blockage levels, and varying the transmit-reflection coefficient. Monte-Carlo simulations were performed to validate the analytical results for the BER and outage probability with respect to the average signal-to-noise ratio. Experiments were performed to demonstrate the received luminous intensity performance with respect to the incident beam luminous intensity of the proposed STAR-RIS assisted UWOC channel.

Long-range wide-area network (LoRaWAN) offers reliable connectivity for terrestrial applications, but its use in underwater wireless communication faces challenges, particularly because of the effects of the air–water interface and signal attenuation in water. To address this issue, we design a hybrid LoRa/optical relay node (LoOp) suitable for LoRaWAN. This design utilizes optical wireless communication and LoRa technology for energy-efficient, real-time, and reliable underwater communication. Depending on the situation, either the LoRa side or optical side of LoOp can serve as a transmitter or receiver. For each design phase, we conducted experimental and theoretical investigations to explore the challenges associated with underwater LoRa applications. Based on the experimental results, we developed a link performance calibration strategy to ensure efficient connectivity. To accommodate the integrated nature of LoOp, we introduced a mechanism for data rate flow control. We compared LoOp with another type of relay node to evaluate its performance in terms of delay and energy consumption. The experimental results show LoOp’s capabilities for underwater applications.

A modified Wilkinson Power Divider is proposed in this paper that utilizes defected ground structure (DGS) in parallel with an isolation resistor. The proposed DGS section is incorporated between the output ports, and the isolation resistor is soldered in parallel with the DGS in the ground plane, instead of on the top plane as in a conventional Wilkinson power divider, to achieve improved or preferable isolation. The proposed design is comprised of two pairs of microstrip transmission lines with equal impedances and varied electrical lengths. The parameters of the main circuit and the DGS section are acquired separately. The parameters of the proposed main circuit are derived by applying conjugate matching theory. Dumbbell-shaped DGS is introduced in the ground plane between the output ports, which acts as a parallel resonator, yielding an attenuation pole at the resonant frequency that contributes to improved isolation. By applying the previous well-known circuit theory, the lumped elements of the equivalent circuit of the DGS were achieved. The physical dimensions of the equivalent circuit for the DGS section were obtained by three-dimensional EM simulation. The measured results show improved isolation, return loss and better bandwidth as compared with other similar works. Furthermore, the proposed circuits designed at resonating frequencies of 3 and 2 GHz presented comparatively good return losses, $S_{11}$ of about −25.54 and −31.24 dB, respectively, and achieved improved isolations, $S_{32}$ between the output ports, in an order of about −40.83 and −36.05 dB, respectively, which is rather exceptional and desirable.

A low-power, low-latency, and long-range (LoRa) relay is developed using mixed-frequency underwater wireless communication systems (UWCSs) for LoRa wide-area network. First, the feasibility of utilizing LoRa in an underwater-to-above-water communication link is examined through theoretical and experimental approaches. In addition, the communication link quality is evaluated using the received signal strength indicator, signal-to-noise ratio, and packet delivery ratio at different depths, horizontal distances, and LoRa physical layer parameters. Finally, considering the experimentally observed challenges of UWCSs, the LoRa-based relay is designed, and its usability is validated by comparing the communication systems without and with the relay (from underwater to the water surface to the above-water) in terms of LoRa physical layer parameters, transmission range, and link quality indices. Moreover, the network latency and battery consumption of the whole system are studied without and with the relay using different LoRa physical layer parameters, and the optimum parameters are presented. The results show that the designed relay can improve communication performance without delay and power consumption concerns.

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 Mbps. 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 LSTM, spatial attention, RNN, Transformer, and 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.