This paper addresses the challenges of topology control, multi-hop routing, and link mode selection in optical-acoustic hybrid underwater wireless sensor networks (OA-UWSNs) by proposing a joint optimization framework that integrates hierarchical reinforcement learning (HRL) with an improved non-dominated sorting genetic algorithm (NSGA-III+). The OA-UWSN is formulated as a multi-objective graph optimization problem subject to connectivity, mode allocation, and reliability constraints. At the topology and link layers, the HRL-based approach employs a deep deterministic policy gradient (DDPG) algorithm at the upper level to assign connectivity weights to network links and incorporates a minimal connectivity restoration mechanism to construct feasible subgraphs. At the lower level, Q-learning is adopted to enable adaptive selection between acoustic and optical transmission modes for each link. For the routing layer, NSGA-III+ is developed to achieve multi-objective route optimization, leveraging segmented path encoding, a dynamically weighted fitness function, and a multidimensional crowding adjustment strategy to enhance both the convergence rate and the diversity of the solution set. Simulation results demonstrate that the proposed HRL+NSGA-III+ framework consistently outperforms existing baseline methods in terms of convergence speed, energy efficiency, latency reduction, and link quality, and exhibits robust performance across various network scales.

With the increasing number of terminal devices in the Ocean of Things (OoT), it is necessary to apply the OoT mobile edge computing (MEC) paradigm to low-Earth orbit (LEO) satellites. The aim is to support the operation of compute-intensive OoT services with LEO satellite assistance. To address the proliferation of computing services in OoT, this article proposes a satellite-assisted task offloading and resource allocation (STORA) approach for OoT edge computing, which includes a generalized framework for three-layer MEC systems in space, on the surface, and underwater. First, the MEC system energy minimization problem is described as mixed integer-nonlinear programming (MINLP) and divided into two subproblems: 1) task offloading and 2) resource allocation. Second, the task offloading subproblem is modeled as a Markov decision process (MDP). The proposed adaptive deep deterministic policy gradient (A-DDPG) algorithm jointly optimizes the offloading policy and offloading volume. In A-DDPG, a soft network update method with an adaptive updating coefficient ensures stable network updates while achieving fast convergence. Finally, the resource allocation is decomposed into a joint optimization problem involving buoy and satellite computational resources, which is shown to be convex. The Lagrange multiplier method is used to optimize the buoy-satellite resource allocation problem while also balancing edge computational load across servers. The experimental results show that STORA can reduce network energy consumption by 17.8%, increase network lifetime by 24.4%, and lower network latency by 11.5%.

As a result of the development of the Internet of Underwater Things (IoUT), underwater connected devices generate a large volume of data with varying values and time sensitivity. Previous data collection strategies cannot accommodate the varying time requirements of various data types. To address the aforementioned issues, this article proposes a cooperative data collection method (MADC-DV) for multiple autonomous underwater vehicles (AUVs) based on local global deep$Q$learning (LG-DQN) and data value, which divides data into emergency and nonemergency and achieves hybrid data collection. First, the MAC protocol for communication between AUVs and clusters is designed to divide nonemergency data into high-value data and low-value data, with low-value data not needing to reply to ACK acknowledgment packets, thereby reducing the nonemergency data collection delay. Second, nonemergency data are collected cooperatively using multiple AUVs, and the LG-DQN approach is used to plan the paths for multiple AUV data collection in order to reduce the overall energy consumption of underwater wireless sensor networks (UWSNs). Finally, emergency data are collected using a multihop routing approach to assist in the collection. A routing method is proposed to compensate for the inability of AUVs to be applied to emergency data collection. The experimental results indicate that the method can improve the network life cycle by 18.7%, reduce the delay in the collection of nonemergency data by 40%, and reduce the delay in the collection of emergency data by 26.3%, thereby meeting the varying time requirements for different types of data.

With little or no human intervention, almost every object in Internet of Things (IoT) has ability to communicate, sense, and process information to make everything connected. Noise in complex environment has a great impact on machine-to-machine (M2M) interaction in IoT. Adaptive wavelet threshold function (AWTF)-based M2M Gaussian Noise Removal Method is proposed in this article. First, a bilateral enhanced wavelet threshold function is derived based on the adjustable zeroing window. Further, threshold is used to construct a bilateral enhanced wavelet threshold function, which can eliminate the oscillations in the existing wavelet threshold function. This ensures that there is no break in wavelet coefficients during the reconstruction process and that stable wavelet decomposition and reconstruction can be achieved. The signal-to-noise ratio (SNR) of a signal is estimated based on the variance of the noise-containing signal, and the zeroing window parameters are adjusted adaptively according to the SNR value to eliminate the noisy wavelet coefficients and improve the denoising performance. In addition, when the wavelet coefficients are reconstructed, the proposed algorithm can select a suitable threshold function according to a particular threshold value, which improves the robustness of the algorithm. “Doppler” and “Bumps” standard test signals are used as interactive signals to simulate the proposed algorithms. For “Doppler” signals, the SNR, root mean square error (RMSE), and noise suppression ratio (NSR) of AWTF increased by 8.48%, 42.59%, and 1.69%, respectively, compared with the GDES+ABC algorithm. For “Bumps” signal, the SNR, RMSE, and NSR of AWTF are improved by 11.67%, 24.46%, and 2.99%, respectively, compared with GDES+ABC algorithm. In addition, we also use different modulated signals to carry out real field experiments in Qingdao cruise ship home port, which proves the effectiveness of the proposed algorithm.