Underwater acoustic networks (UWANs) play a vital role in the Internet of Underwater Things (IoUT), enabling critical functions, such as communication, data collection, and navigation. Among the applications of the IoUT, localizing a mobile node (MN) via a UWAN is particularly promising. However, the existing localization algorithms are ineffective when faced with an insufficient number of time of arrival (TOA) measurements for an unsynchronized MN due to the presence of sparsely deployed anchor nodes and signal reception issues. Although tracking methods can offer position predictions, their accuracies are compromised over time due to the movement of MNs. To overcome these challenges, we propose a methodology that combines the departure time of a beacon signal (DOB) with limited TOA measurements. This methodology enables an MN to be localized using a TOA-based method, which typically requires fewer measurements than a time difference of arrival (TDOA)-based method. Based on this methodology, we introduce an algorithm called localization with insufficient TOA measurements (LITM), which comprises two subalgorithms: one for estimating and tracking the DOBs and the other for localizing MNs through a closed-form solution. Together, these subalgorithms provide accurate MN position estimates under the constraint of insufficient TOA measurements. To validate the performance of our proposed algorithm, we conduct both simulation studies and sea experiments. The results demonstrate the superior effectiveness and position estimation accuracy of our algorithm compared to those of the existing methods.

The development of underwater acoustic networks is a significant expansion of Internet-of-Things technology to underwater environments. These networks are essential for a variety of marine applications. For many practical uses, it is more efficient to collect marine data from a remote location over multiple hops, rather than direct point-to-point communications. In this article, we will focus on the underwater acoustic string network (UA-SN) designed for this type of application. We propose a Robust and Adaptive Pipeline Medium Access Control (RAP-MAC) protocol to enhance the network’s transmission efficiency, adaptability, and robustness. The protocol includes a scheduling-based concurrent algorithm, online real-time configuration adjustment function, a rate mode adaptive algorithm, and a fault recovery algorithm. We conducted simulations to compare the new protocol with another representative protocol, validating the RAP-MAC protocol’s adaptability and fault recovery ability. Additionally, we carried out two large-scale sea trials. The results of these experiments indicate that the RAP-MAC protocol ensures effectiveness and reliability in large-scale multihop UA-SNs. In the South China Sea, we were able to achieve a communication distance of 87 km with a throughput of 601.6 bps, exceeding the recognized upper bound of underwater acoustic communication experiment performance by 40 km·kbps.

Ultra-Short Baseline (USBL) systems are susceptible to installation errors that necessitate accurate calibration before deployment. In this paper, we propose an innovative calibration method that does not require time synchronization, enhancing calibration accuracy and mitigating the effects of acoustic array motion. First, we construct a novel installation error model based on the slant range difference to estimate signal sending time. Then, we designed an improved filtering algorithm—variational Bayesian unscented Kalman filter-range (VBUKF-r). In VBUKFr, to address challenges related to unknown and time-varying measurement noise covariance matrix (MNCM), we integrate variational Bayesian methods with the unscented Kalman filter (UKF) and enhance the accuracy of MNCM estimation by analyzing the relationship between ranging noise and distance. Furthermore, we utilize matrix diagonalization instead of Cholesky decomposition to resolve computational issues associated with non-positive definite state covariance matrixs. Finally, we propose a K-means clustering iterative optimization strategy for VBUKF-r to mitigate the influence of initial measurement vectors on parameter estimation. Simulation analysis and sea trials demonstrate that our algorithm significantly outperforms existing methods.

Underwater vehicles play a crucial role in various underwater applications, such as data collection in underwater sensor networks, target detection and tracking, and underwater pipeline monitoring. Real-time acquisition of their states, particularly their location and velocity, is vital for their operation and navigation. Consequently, the development of a remote tracking system to monitor these states is essential. In this paper, we propose a system that can track the underwater vehicle’s location and velocity. We take a systematic approach that encompasses the system architecture, system composition, signal processing, and mobility state estimation. We present the system architecture and define its components, along with their relationships and interfaces. The beacon signal employed in the system features dual-hyperbolic-frequency-modulated (HFM) waveform and an OFDM symbol with cyclic prefix (CP). Based on this beacon signal, we demonstrate how signal processing techniques are utilized to precisely determine the time of arrival and reduce false alarm rates in underwater acoustic channels affected by impulsive noise. Additionally, we explain how the CP-OFDM symbol is used to measure the Doppler scaling factor and transmit essential information for localization and velocity estimation purposes. Utilizing the measurements obtained through signal processing, least squares estimators are used for estimating both the location and velocity. To validate the effectiveness of our approach, we implement the system and conduct field trials. Two separate experiments were conducted in which the diagonal lengths of the square topology were designed to be 1000 m and 800 m. The minimum/maximum root mean square error of localization in the first and second experiment is 2.36/2.91 m and 1.47/2.49 m, respectively. And the minimum/maximum root mean square error of velocity estimation in the first and second experiment is 0.16/0.47 m/s and 0.21/0.76 m/s, respectively. Results confirm the effectiveness of the proposed method in estimating the location and velocity of the underwater vehicle. Overall, this paper provides a practical and effective design of a system to track the location and velocity of underwater vehicles. By leveraging the proposed system, signal processing, and mobility state estimation methods, our work offers a systematic solution. And, the successful field experiment serves as evidence of the feasibility and effectiveness of the proposed system, making it a valuable contribution to the field of tracking underwater vehicles.

Underwater Internet-of-Things (UIoT) is an extension of Internet-of-Things technology in underwater. The underwater acoustic network with WiFi architecture (UW-WiFi), as a specific deployment of UIoT, has been proved to be a promising technique for wide-ranging marine applications. However, due to the unique features of underwater acoustic channel, such as long and variable propagation delay, low available bandwidth and high bit error rate, conventional medium access control (MAC) protocols designed for terrestrial WiFi networks need an overhaul to work efficiently for UW-WiFi networks. In consideration of the aforementioned channel features, different demands of nodes to occupy channel resources and the reliability of data transmission, a time sequence-based dynamic on-demand assignment (SDDA) MAC protocol for UW-WiFi networks is proposed in this paper. In SDDA, the collision-free scheduling is integrated with the reservation mechanism, aiming to address the issue of various access requirements of diverse task nodes on channel resources in the UW-WiFi network system. The designed protocol employs propagation delays and the amount of data to be sent by terminal nodes to achieve non-conflict transmissions of control packets and on-demand scheduling of data packets. Additionally, the scheme does not require extra overhead for time synchronization. Comparison simulations demonstrate that SDDA provides considerable benefits in terms of channel utilization, end-to-end delay and packet delivery ratio. At last, the SDDA protocol is implemented and a UW-WiFi network system is set up in the marine environment. The ocean field experiment results agree well with the simulated ones and also verify that the proposed protocol achieve conflict-free on-demand scheduling, fairness among terminal nodes at different ranges and the practicability in actual environments.

The establishment of the Underwater Internet of Things (UIoT) and the realization of interconnection between heterogeneous underwater intelligent devices are urgent global challenges. Underwater acoustic networking is the most suitable technology to achieve UIoT for medium to long ranges. This paper presents an underwater Wi-Fi network, called uw-WiFi, that utilizes a master–slave mode architecture. uw-WiFi is dedicated to solving the problem of underwater acoustic networking with limited coverage range and number of nodes. To ensure the reliability of different types of data in the network, a reliable segmentation transmission protocol based on data type is designed. Additionally, on-demand scheduling based on the reservation MAC protocol is developed to solve the channel resource sharing problem. The uw-WiFi system has undergone shallow sea tests, and the experimental results demonstrate that the uw-WiFi network is capable of achieving a network throughput of 500 bps or higher, indicating superior network performance.