Autonomous underwater vehicle (AUV) system has played an important role in complex Internet of Underwater Things (IoUT). As we know, single AUV is inefficient in collecting data in large-scale IoUT. In order to improve the timeliness of data, this article uses multiple AUVs with specific formation shape to collect sensory data. However, the intermittent and unreliable characteristics of underwater acoustic communication between AUVs seriously affects the performance of formation control. To solve this issue, this article mainly investigates formation control problem of leade–follower structured AUVs with state prediction estimation under the condition of unreliable underwater acoustic channel. First, a novel formation control law derived from backstepping sliding mode method is proposed, and then, a suitable Lyapunov functional is constructed to prove sufficient stability conditions. Second, Gaussian prediction model considering time delay and packet dropout has been designed to estimate the intermittent communication channel. Moreover, a new metric named formation uniform degree (FUD) is proposed to measure the degree of queue uniformity in a quantitative manner. Finally, extensive simulation results compared with traditional methods based on ideal channel model demonstrate the effectiveness of proposed formation control method.

Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with autonomous underwater vehicle (AUV) has been becoming the chief methodology of knowing seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full coverage path planning (CPP) problem. In this article, facing the complete CPP problem of mobile AUV, a new strategy based on bio-inspired neural network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A* path planning-based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved BINN (IBINN in short)-based algorithm.

Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS.

The multiple autonomous underwater vehicle (AUV) formation plays an important role in underwater missions, such as oceanographic sampling and water pollution monitoring. This article presents the mechatronic design, modeling, formation control, and experiments of multiple AUVs. The structure of the AUV and a simplified mathematical model for tracking control are described. To achieve formation control, we formulate a control framework for the multiple AUVs. The upper layer is a formation algorithm based on a novel leader-follower control law. The bottom layer is a dynamic controller based on active disturbance rejection control (ADRC). The formation algorithm is in charge of calculating reference values for the followers to maintain a desired pattern with the leader. The stability and convergence properties of the algorithm have been analyzed using the Lyapunov stability method. Meanwhile, an ADRC approach-based dynamic controller is established to track the reference values. Numerical simulations are carried out to analyze formation control and validate the control framework. The multiple AUVs can switch and maintain the formation between the one-line pattern and the$V$pattern. Finally, extensive formation field experiments involving the one-line pattern and the$V$pattern show the good motion ability of the self-designed AUVs and also verify the feasibility of the proposed control approach. Note to Practitioners—The motivation of the article is to design a practical formation control approach for multiple AUVs and verify the control approach in the field. Although there have been a lot of prior research studies on multiple AUVs, how to design a formation control approach subjected to communication bandwidth constraints and how to develop multiple AUVs and verify the effectiveness of the control method in the field are worthy of intense investigation. Hence, this article builds the mechatronic design and dynamic model of the AUV and proposes a novel leader-follower formation control approach based on the dynamics and kinematic model of AUV. Besides, the stability of formation control for multiple AUVs is proven by the Lyapunov theorem. Multiple AUVs can switch and maintain formation between$V$pattern and one-line pattern with a smaller error. Finally, the performance of the proposed formation control strategy is experimentally verified using three self-made AUVs inside a large reservoir. The proposed method is suitable for multiple AUVs missions involving underwater surveillance, underwater pipeline inspection in the ocean.

A crucial prerequisite for orthogonal matching pursuit (OMP), a widely-used channel estimation method in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communication systems, is the determination of a termination condition. However, the appropriate condition, which is commonly considered equal to the physical sparsity of the UWA channel, actually dramatically varies with the suffered noise, thus possibly leading to extremely unstable estimation performance. Existing OMP-based algorithms attempt to solve this problem by elaborately adjusting iteration numbers to balance the proportion of genuine channel taps and noise in the reconstructed signal based on noise levels, which inevitably increases the dependency on the prior information, i.e., signal-to-noise ratio (SNR). In order to overcome this challenge, an intuitive idea is eliminating the influence of noise to restore the originally sparse signal before implementing the standard OMP, naturally avoiding the variation of termination conditions. Considering the powerful ability of deep learning, we imitate and elegantly modify the feed-forward denoising convolution neural network (DnCNN), one of the most typical neural networks for image denoising, to develop our prior-information-free denoising OMP (DnOMP) algorithm with a constant iteration number. Simulation results validate that, compared to the standard OMP with the dynamic termination condition, the DnOMP can reduce the normalized mean square error (NMSE) by 39.47%.

With the development of robotics, the underwater robot platform has been widely used in the Internet of Underwater Things (IoUT). An underwater robot platform equipped with multiple sensors is used as a mobile collector to build a reliable information collection system for IoUT. This article presents the mechatronic design, fabrication, modeling, control, simulation, and experiments of a robot IoUT platform to enable the smart ocean. Inspired by the design of both fin-actuated swimming of fish and buoyancy-driven gliding of underwater glider, a novel multilink gliding fish robot is proposed. The multilink gliding fish robot, which is called FishBot in this article, can swim flexibly and glide energy efficiently in three dimensions. In the FishBot, the body and/or caudal fin (BCF) with three degrees of freedom and buoyancy-driven system was equipped as the main propulsion device. Besides, a pair of pectoral fins was equipped to assist in regulating the gliding attitude and enhance the FishBot maneuverability in the vertical plane. The dynamic model that consists of cruise swimming motion, pure-pitching swimming motion, and 3-D swimming motion for control is established. Moreover, a behavioral control framework is developed to achieve a variety of fish-like swimming behaviors and gliding motion. Meanwhile, we proved the stability of the linear quadratic regulation controller and the locomotion controller is provided with exponential stability. The validity of the proposed model and the designed controller is demonstrated by numerical simulations. Finally, a series of experiments involving different fish-like behaviors and gliding motion elucidates the powerful locomotion ability of the FishBot.