Underwater acoustic sensor networks (UASNs) have become a popular research topic, with research challenges focused on underwater communication techniques. By incorporating long end-to-end latency, high energy consumption and dynamic network topology in UASNs, many intelligent routing protocols have been proposed to solve the problem. However, shortcomings still exist, and comprehensive routing protocols are urgently needed. In this paper, we propose an adaptive Deep Q-Network-based energy- and latency-aware routing protocol (DQELR) to prolong network lifetimes in UASNs. In the DQELR, a Deep Q-Network algorithm with both off-policy and on-policy methods is adopted to make globally optimal routing decisions. Based on both the energy and depth states of nodes at different communication stages, nodes with the maximum Q-value can be selected as forwarders adaptively considering both energy and latency. A hybrid of the broadcast and unicast communication mechanisms is also designed to reduce network overhead. In addition, network topology changes can be addressed through an on-policy method that makes a new routing decision when the current route becomes corrupted. With less energy consumption and strict latency limitations, the DQELR can prolong network lifetimes in UASNs. Simulation results show that the DQELR can achieve a superior network lifetime with better latency and energy efficiency performances relative to other general schemes applied in UASNs.

The accurate localization of nodes is one of the most basic tasks in Underwater Acoustic Sensor Networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received, and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this paper, we propose a Mobile-beacon Based Iterative Localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multi-hop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism firstly uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network’s lifetime.

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20–25% compared with a classic lifetime-extended routing protocol (QELAR).

Moiré artifacts are generally caused by the interference between the overlap of the sensor's sampling grid and high-frequency (nearly) periodic textures, and heavily affect the image quality. However, it is difficult to effectively remove moiré artifacts from textured images as the structure of moiré patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoiréing method by signal decomposition and guided filtering. Given a textured image with moiré artifacts, we first remove moiré artifacts in the green (G) channel using the proposed low-rank and sparse matrix decomposition model. This model regularizes the texture layer by the low-rank prior in spatial domain and the moiré layer by sparse representation in frequency domain. An alternating direction method under the augmented Lagrangian multiplier framework is used to solve the matrix decomposition model. Then, since the red (R) and blue (B) channels are more heavily polluted by moiré artifacts than the G channel, we propose to remove moiré artifacts in its R and B channels via guided filtering by the obtained texture layer of the G channel. Experimental results demonstrate that our method outperforms the state-of-the-art methods for both synthetic and real images.

Due to the characteristics of the underwater acoustic (UWA) channel, the process at the receiver is complicated to match the channel. To simplify receiver design and match UWA channel better, this study proposes a deep neural network‐based orthogonal frequency division multiplexing receiver for UWA communication. Different from existing receivers needing a neural network and several other processing parts, the proposed receiver only uses a single neural network to implement the whole signal processing. Moreover, it is a general receiver which is suitable for other modulation schemes. Simulation results show that the proposed receiver offers better bit error rate performance over traditional ones.