Accurate localization of underwater sensor nodes is a fundamental yet challenging problem in underwater wireless sensor networks. Localization performance is severely affected by complex environmental factors, including measurement noise, Non-Line-Of-Sight (NLOS) propagation, absorption loss, and path attenuation. To address these challenges, this paper proposes an efficient and scalable localization mechanism based on Received Signal Strength Difference (RSSD). First, the Expectation–Maximization (EM) algorithm is applied to identify NLOS links and estimate their corresponding biases. Then, a weighted Huber loss function combined with an adaptive weighting strategy is introduced to construct an Adaptive Weighted Loss Function, which suppresses noise, adaptively reduces the influence of NLOS paths, and compensates residual biases. Based on this formulation, the localization task is transformed into an optimization problem. Furthermore, an improved Grey Wolf Optimizer (IGWO) incorporating an Adaptive Convergence Factor (ACF) and an Elite-Guided (EG) strategy is developed to enhance global search capability and convergence stability. Extensive simulation results demonstrate that the proposed method achieves significantly higher localization accuracy than existing approaches.

The underwater sonar image target detection is a significant research direction in marine exploration tasks. The noise interference and blurred feature details of sonar images pose challenges to the extraction of effective feature information. And the similarity between the target shadow and the real target in the sonar image also increases the difficulty of the model to accurately identify the target. The previous sonar image target detection models have the issue of being sensitive to the threshold in Non-Maximum Suppression (NMS). Given the existing issues, this paper proposes UTD-SCnet. Firstly, the proposed Dual Multi-scale Spatial Attention module (DMSA) enhances the spatial relationship between features and increases the model’s distinguishability between real targets and shadows. Subsequently, the sensitivity of the threshold of NMS in sonar image target detection is analyzed. A transformer decoder is introduced to eliminate this influence and improve the detection accuracy of the model. Finally the Cross stage partial Orthogonal channel attention Network (CON) strengthens the model’s ability to extract effective feature information of sonar images. Experiments demonstrate that the proposed model achieved an mAP50:95 of 0.588 on the URPC2022 (Underwater Robot Picking Contest), which is 3.6% higher than the baseline model. The detection performance (mAP50:95 reached 0.580) on the URPC2021 and the noise addition experiment both demonstrated the superiority of our proposed UTD-SCnet.

Underwater acoustic sensor networks (UASNs) have emerged as a promising technology for marine exploitation. However, due to the challenging characteristics of the underwater communication, including propagation delay and unstable link quality, UASNs face numerous issues. Furthermore, the open nature of the underwater environment poses significant security threats, making secure routing in UASNs a crucial requirement. In this paper, we propose a novel and secure routing approach, named GHL-SAR, to address these challenges. GHL-SAR models and measures the trust level of nodes by considering energy trust, communication trust, and node trust. Specifically, Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) is employed to predict the most likely hidden state sequence for a given observation sequence, and then Long Short-Term Memory (LSTM) is used to determine the relationship between the hidden state and the trust level. Moreover, GHL-SAR utilizes an adaptive routing method based on the Particle Swarm Optimization Algorithm (PSOA), which takes into account the accurate link quality to determine the routing strategy. The simulation results demonstrate that GHL-SAR significantly reduces packet loss and energy consumption while ensuring network security compared to other existing routing algorithms.

Bionic communication offers advantages over traditional covert hydroacoustic communication in terms of communication distance and security. However, there is still room for further development in the area of sound signal feature extraction. In this paper, we propose a signal contour extraction algorithm with the dolphin whistle as the research subject: the Edge Matrix Merging Extraction Method based on the Canny algorithm (EMMEC). EMMEC better utilizes the structural characteristics of the signal's time-frequency spectrum and enhances the accuracy of whistle time-frequency curve extraction compared to traditional methods. EMMEC initially consolidates and processes whistle information using edge detection to generate a merged edge matrix. It then extracts the time spectrum profile curve of dolphin whistles by estimating curve parameters. Simulation experiments demonstrate that our proposed method can more accurately and smoothly fit the whistle time-frequency spectrum curve by effectively leveraging the structural characteristics of the signal's time-frequency spectrum, ultimately improving the accuracy of curve extraction.

The massive demand for marine exploitation has promoted the thriving Internet of Underwater Things (IoUT). The volume, velocity, and variety (3V) of data produced by sensors, hydrophones, and cameras in IoUT are enormous, which challenges the network in achieving load balancing and Quality-of-Service (QoS) provisioning. This article adopts the “SDN+AI” paradigm to realize a load-balanced and QoS-aware software-defined IoUT from a framework design. We first introduce SDN technology to separate the data plane from the control plane to enhance the network’s scalability and flexibility. Then, a multicontroller load-balancing strategy based on switch migration called CASM is proposed to improve the network’s performance further. With the global view provided by SDN controllers, we proposed a QoS-aware adaptive routing protocol (SQAR) based on reinforcement learning, which can intelligently select route paths to satisfy the QoS requirements of multiple IoUT services. The results show that CASM achieves an efficient load balance while shortening the response time and average control path latency of the switch migration process, which significantly benefits our routing protocol. SQAR outperforms the existing QoS-aware routing protocols regarding QoS satisfaction probability, energy consumption, and convergence rate. Overall, our framework maintains a QoS violation rate below 5% and a load-balancing rate above 90% in a timely manner.