With the extensive application of underwater acoustic sensor networks (UANs) in various fields such as commerce, marine environmental research, and national defense, the need for an autonomous and well-organized underwater acoustic network has been increasing. Topology discovery is a crucial step in constructing an underwater acoustic network, and node discovery and topology establishment are the essential components of the topology discovery process in UANs. This paper introduces the characteristics of underwater acoustic channels and networks and highlights their influences on topology discovery. We discuss the topology discovery protocol development in terrestrial networks (i.e., duty-cycle ad hoc network, Internet of things). The main focus of this paper is to study the topology discovery protocols of UANs. This paper also classifies and introduces the existing topology discovery protocols and compared their advantages and disadvantages to understand the current topology discovery methods. Furthermore, we also discuss the topology discovery protocol’s influence on different layers’ functions in the UAN protocol stack. Analyze the current research challenges in this field, followed by important open issues in UAN protocol development, which provide new opportunities for further research.

An efficient routing protocol is critical for the data transmission of Underwater Wireless Sensor Networks (UWSNs). Aiming to the problem of void region in UWSNs, this paper proposes a reinforcement learning-based opportunistic routing protocol (DROR). By considering the limited energy and underwater environment, DROR is a receiver-based routing protocol, and combines reinforcement learning with opportunistic routing to ensure real-time performance of data transmission as well as energy efficiency. To ensure reliable transmission when encountering void regions, a void recovery mechanism is designed to enable packets to bypass void nodes and continue forwarding. Furthermore, a relative Q-based dynamic scheduling strategy is proposed to ensure that packets can efficiently forward along the global optimal routing path. Simulation results show that the proposed protocol performs well in terms of end-to-end delay, reliability, and energy efficiency in UWSNs.

Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods.

Constrained-energy underwater acoustic nodes are typically connected via a multi-hop underwater acoustic network (MHUAN) to cover a broad marine region. Recently, protocols for efficiently connecting such nodes have received considerable attention. In this paper, we show that the time reversal (TR) process plays an important role in the medium access control (MAC) because of its physical capability to exploit the multi-path energy from the richly scattering underwater environment, as well as to focus the signal energy in both spatial and temporal domains. In MHUANs, with severe multi-path propagation at the physical layer, the active TR process spatially focuses the signals to the location of the intended receiver; this significantly diminishes the interference among parallel links. We propose an active TR-based MAC (TRMAC) protocol for MHUANs, with the aim of minimizing collision and maximizing channel utilization simultaneously. Furthermore, by considering the impact of the cross correlation between different links on the TR-based medium access, we derive the threshold of the link cross correlation to resolve collision caused by the high cross correlation between realistic links. We perform simulations using the OPNET and BELLHOP environments, and show that the proposed TRMAC results in significantly improved throughput, decreased delay, and reduced data drop ratio in MHUANs.

Underwater acoustic (UWA) adaptive modulation (AM) requires feedback about channel state information (CSI) but the long propagation delays and time-varying features of UWA channels can cause the CSI feedback to be outdated. When the AM mode is selected by outdated CSI, the mismatch between the outdated CSI and the actual CSI during transmission degrades the performance and can even lead to communication failure. Reinforcement learning has the ability to learn the relationships between adaptive systems and the environment. This paper proposes a deep Q-network (DQN)-based AM method for UWA communication that uses a series of outdated CSI as the system input. Our study showed that it could extract channel information and select appropriate modulation modes in the expected channels more effectively than single Q-learning (QL) without needing a deep neural network structure. Furthermore, to mitigate any decision bias that was caused by partial observations of UWA channels, we improved the DQN-based AM by integrating a long short-term memory (LSTM) neural network, named LSTM-DQN-AM. The proposed scheme could enhance the DQN’s ability to remember and process historical input channel information, thus strengthening its relationship mapping ability for state-action pairs and rewards. The pool and sea experimental results demonstrated that the proposed LSTM-DQN-AM outperformed DQN-, QL- and threshold-based AM methods.

In this paper, we consider a practical underwater data collection network, where one destination needs to collect data from multiple underwater nodes. With the conventional automatic-repeat-request (ARQ) protocol, the destination requests retransmission from each node individually without any node cooperation. We propose two protocols, selective relay cooperation and dynamic network coded cooperation, utilizing the fact that underwater nodes can overhear the transmission of the others. In the selective relay cooperation, one node can be selected as a relay to transmit the data from another undecoded node in the retransmission phase. In the network coded cooperation, the selected relay nodes transmit network coded packets to the destination. The relay nodes participating the cooperation are selected by the destination based on the channel quality, as measured by the effective signal-to-noise ratio. In addition to simulation results, we have carried out several lake tests based on a full protocol implementation. Simulation and field testing results demonstrate that the proposed schemes can gain significant performance improvement compared with the conventional ARQ scheme.