In this article, we show the development and implementation of the Internet of Maritime Things (IoMT) platform that supports long-range and high-rate communication for remote and online marine water quality monitoring. We first develop the IoMT device and gateway that interwork with international standard Internet-of-Things (IoT) software platforms for universal utilization in the field of marine monitoring. The proposed IoMT device and gateway play a role similar to the IoT device and gateway on land, but it is optimized for marine use with consideration of the practical characteristics of using sensors in the ocean. Furthermore, to realize long-range and high-rate communication between marine sensors and a control center (CC) on land, an automatic beam adjustment system for directional antennas is developed. This enables the use of directional antennas, which can support long-range and high-rate communication by automatically adjusting the beam angle between the antennas while exchanging their control information, despite the antennas being shifted by undulation from the sea. Finally, we conduct real sea experiments to identify the performances of the proposed IoMT platform in various environments. The results show that the proposed IoMT platform can collect data from marine sensors as well as high definition camera video from distances of 25 and 40 km to the CC on land under one-hop and two-hop transmissions, respectively. These results indicate that the proposed IoMT platform provides at least twofold performance improvement in terms of communication coverage under a similar data rate condition, compared with existing commercial wireless networks (e.g., LTE in the ocean) not utilizing satellite communications.

In smart grid, an advanced metering infrastructure (AMI) facilitates two-way communications between smart meters (SMs) and a meter data management unit (MDMU). We use cellular-assisted device-to-device (D2D) communications to connect a large number of SMs to the MDMU via cluster heads (CHs). Improper clustering can affect spectral efficiency. In addition, each SM and CH must be provided with a throughput guarantee for reliable smart grid operations. In this paper, we formulate a joint D2D cluster formation and radio resource allocation problem to maximize spectral efficiency in terms of uplink sum-rate, subject to constraints of guaranteeing a minimum throughput to each AMI entity. The optimization problem is a mixed-integer non-linear program, which is non-deterministic polynomial-time hard. For practical implementation and real-time decision making, we divide the original problem into four sub-problems: cluster formation, initial power allocation, channel allocation, and power allocation improvement, respectively. In the sub-problem for power allocation improvement, we exploit the properties of the difference of convex functions in finding the optimal transmission power. Simulation results confirm superiority of the proposed algorithm compared with other existing algorithms.

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g. Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode--forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs.

In this work, we study learning-aided adaptive control of optical beam alignment to maintain a seamless connection with high communication performance in a point-to-point (P2P) underwater optical wireless communication (UOWC). To this end, we propose a two-step two-agent deep reinforcement learning (TSTA-DRL) algorithm that enables an underwater sensor (US) installed on the seabed to sequentially determine the beam orientation (BO) and beam divergence (BD) angles for transmitting its sensing data to an unmanned surface vehicle (USV) that may irregularly shake above the sea level. Specifically, the proposed TSTA-DRL algorithm includes two DRL agents: BO and BD. The BO agent selects the BO angle to point the optical beam of the US toward the USV to perform beam alignment between the US and USV. Moreover, given the BO angle determined by the BO agent, the BD agent chooses the BD angle to maximize the signal-to-noise ratio (SNR) while maintaining the seamless optical link between the two nodes. For the practical application of the proposed algorithm, movement data of the USV measured in the South Sea of Korea are utilized for training the proposed algorithm. The simulation results demonstrate that the proposed TSTA-DRL algorithm achieves the highest SNR while maintaining a stable UOWC link compared with the existing algorithms.

Compact radar sensors for Internet of Things applications can be used to analyze indoor human gait characteristics. Conventional human gait analysis methods typically involve generating two-dimensional (2D) high-resolution time-frequency images and employing image-processing techniques to estimate gait parameters of a walking human. However, these computations can be resource-intensive for the compact radar sensors. To address this problem, we propose a new scheme for estimating gait parameters. Our method has four significant contributions: 1) utilization of one-dimensional phase modulation in a radar echo for efficient gait-parameters estimation, as opposed to relying on 2D time-frequency images; 2) decomposition of micro phase modulations corresponding to the torso or pelvis and lower body parts (e.g., knee, tibia, and ankle) using dedicated filtering techniques to mitigate the interference between body components; 3) compensation for effects of nonlinear macro phase modulation caused by whole-body movements; and 4) robust estimation of gait parameters including time-varying radial velocity, gait rate, step length, and the height of the lower body. In experiments performed using a 5.8 GHz continuous-wave Doppler radar, we observed that the proposed scheme can perform efficient and robust gait parameter estimation of an indoor human walking.