Mobile edge computing (MEC) has emerged as a promising technology for marine Internet of Things (IoT) networks, supporting diverse application requirements that could be both computationally intensive and delay-sensitive. However, most existing studies assume access to pre-existing network information and rely on single-layer MEC frameworks to provide services from an offline perspective, struggling to ensure low latency. To overcome the related issues, we first consider an online hierarchical computation offloading framework in this paper for marine IoT networks with aerial, offshore, and onshore devices. We further develop a hybrid transmission strategy combining non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) to enhance the computation offloading efficiency within the framework. Considering the time-varying capacity of wireless channels, we thus minimize the hierarchical computation offloading delay by jointly optimizing the offloading strategy and network resource allocation in the marine IoT networks online. To solve the formulated mixed-integer nonlinear programming (MINLP) problem, we design a problem-solving framework based on a decomposition structure. Specifically, we decompose the formulated MINLP problem into two subproblems. For the bottom subproblem, we design a successive convex approximation (SCA)-based algorithm to optimize the hierarchical transmission durations and the offloaded workload with a given user association scheme. For the top subproblem, we propose a deep reinforcement learning (DRL)-based algorithm to realize online optimization of the user association scheme under the time-varying channels. Finally, numerical results demonstrate that the proposed algorithms, including the SCA-based algorithm and the DRL-based algorithm, can reach near-optimal results. Furthermore, the proposed hierarchical computation offloading framework significantly outperforms traditional benchmarks.

In the Industrial Internet of Things (IIoT), the latency-sensitive task can be efficiently performed based on real-time data collection, transmission, and computation. In this article, we thus propose a hybrid nonorthogonal multiple access-rate splitting multiple access (NOMA-RSMA) edge-cloud service computing framework for the IIoT consisting of end devices (EDs), edge servers (ESs), and cloud servers (CSs), in which the tasks are allowed to be computed at the EDs, the ESs, or a CS. When the task computation takes place at the ESs or the CS, tasks would be first compressed at EDs, and then be offloaded to the ESs via nonorthogonal multiple access (NOMA). After that, the ESs offload tasks to the CS via rate splitting multiple access (RSMA) if tasks are intended to be processed at the CS. Otherwise, the ESs perform computation locally. Specifically, under the delay constraints, we aim to minimize the total system energy consumption by jointly optimizing the transmit power of the EDs and the ESs, the transmission delays of each NOMA group and RSMA, the signal splitting ratio of the ESs, the computation power of the EDs, the ESs, and the CS, the compression delay, the offloading decisions of the EDs and the ESs. To address the formulated nonconvex problem, we employ a hierarchical decomposition approach to layer it into a top-level offloading decision problem and a bottom-level resource allocation problem. We design a block coordinate descent (BCD)-based method to solve the bottom-level problem. Additionally, we propose an algorithm based on deep reinforcement learning and online offloading (DROO) to obtain the suboptimal offloading decisions for EDs and ESs in the top-level problem. Numerical results validate the accuracy and effectiveness of our algorithms in terms of the total energy consumption, compared with three heuristic algorithms, i.e., the genetic algorithm, and the cross-entropy algorithm, the Deep Q-Network algorithm.

Mobile edge computing (MEC) is emerging as a critical paradigm to meet the growing computational demands of wireless devices. However, edge servers, wireless devices, and service types in MEC networks are usually time-varying due to configurations, traffic patterns, and operational status, which results in inaccurate state estimations. Therefore, existing Deep Reinforcement Learning (DRL)-based offloading algorithms often fail to effectively handle dependency-aware applications. Furthermore, traditional reward functions adopted in DRL-based algorithms fail to decouple historical dependencies among offloading decisions for subtasks, hindering accurate state updates. To address these challenges, we propose a Status Correction-empowered Deep Reinforcement Learning (SC-DRL) algorithm for making the dependency-aware application offloading decisions in this paper. Specifically, we first adopt the State-Adjusted Bellman Equation to ensure accurate updates of DRL state values. Then, we introduce the dynamic estimate equation to enable DRL agents to estimate system states accurately. Furthermore, we mathematically model device load to extend the dynamic estimate equation to handle real-world complexities. Finally, we propose the Reapplying Reward Technology to reduce reward inaccuracy due to historical dependencies. Both simulations and real-world tests show that the SC-DRL improves the ratio of applications completed within their deadlines by an average of 3.36% and 41.94% compared to the state-of-the-art algorithms, such as Advantage Actor-Critic (A2C), Deep Q-Learning (DQN), and Proximal Policy Optimization (PPO).

Satellite communication has emerged as a promising technology for achieving a wide range of communication coverage and providing a variety of services in the marine Internet of Things. This paper investigates the efficient data collection scheme of the low earth orbit (LEO) satellite from different sensing devices (SDs) deployed in the offshore areas. To be specific, these marine SDs in each time slot utilize the non-orthogonal multiple access (NOMA) to upload their respective sensing data to the LEO satellite passing over the relative areas. To ensure efficient data collection, we then aim to minimize the overall energy consumption needed to upload all sensing data from SDs to the LEO satellite subject to the minimum transmission latency. To tackle the proposed non-convex joint optimization problem, we designed an efficient algorithm based on successive convex approximation (SCA) to approach the optimal solutions. Finally, numerous results are presented to illustrate the convergence performance of the proposed SCA-based algorithm as well as the performance gains of the proposed scheme.

Digital twin has been emerging as a promising paradigm that connects physical entities and digital space, and continuously evolves to optimize the physical systems. In this article, we focus on studying efficient communication and computation scheme when constructing the Marine Internet of Things (M-IoT)’s digital twin with secrecy provisioning. Specifically, the digital twin model is trained based on federated learning, in which all the unmanned surface vehicles deliver the trained models with nonorthogonal multiple access (NOMA) to the high-altitude platform (HAP) for global model aggregation. Considering the potential eavesdropping on the radio signals of HAP, we utilize the chaotic sequences to spread the model information before the global model broadcasting. In this framework, we aim to minimize the total energy consumption for constructing the digital twin of M-IoT by jointly optimizing the global accuracy, the local accuracy, the HAP’s transmission power and NOMA transmission duration, subject to the secrecy provisioning and latency constraint. An effective low-complexity algorithm is proposed to tackle this joint optimization problem with the use of a layered feature. Finally, numerical results are given to validate the performance gain of the proposed scheme, in comparison with the fixed accuracy scheme, the nonspread spectrum scheme and the time division multiple access transmission scheme.

Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.

Internet of Things (IoT) is one of the key applications of 5G, and the data transmission is the basis of IoT networks. In this paper, we investigate the data transmission scheme in non-orthogonal multiple access (NOMA) for IoT networks to minimize the transmission latency. In order to improve the communication efficiency between devices and the base station (BS) without more energy consumption, we deploy an energy harvesting (EH) relay node between devices and the BS for data transmitting and forwarding. Based on this networking model, we first aim at minimizing the transmission latency by jointly optimizing the transmit power of devices and the relay, forwarding ratios among devices, and forwarding time fraction when transmitting a fixed data bits from devices to the BS via the relay under the constraints of energy buffer and data buffer. Noted that the formulated problem is discrete-continuous mixed and non-convex, we apply the deep deterministic policy gradient (DDPG) algorithm in the framework of bisection searching to obtain the optimal solution. Specifically, the bisection searching is used to seek the possible transmission latency, and the DDPG is to check the feasibility of the chosen transmission latency. Finally, the effectiveness of the proposed model-data-driven algorithm is verified by comparing it with other benchmark algorithms, such as LINGO.

Digital twin has been emerging as a promising paradigm that connects the physical entities and digital space, and continuously evolves and optimizes the physical systems. In this paper, we focus on studying the efficient data communication and computation when constructing the marine digital twin network with secrecy provisioning. Specifically, we leverage the federated learning (FL) to train the digital twin model. In the process of FL, all unmanned surface vehicles (USVs) deliver the trained models with non-orthogonal multiple access (NOMA) to the high altitude platform (HAP) for the global model aggregation. Considering the possible eavesdropping on the HAP, we utilize the chaotic sequences to spread the model information during the global model broadcasting. In this framework, we further want to minimize the total energy consumption of completing the digital twin training by jointly optimizing the global accuracy, local accuracy, HAP's transmission power, and model uploading duration subject to the secrecy provisioning and latency constraint. Despite the non-convexity, we propose a low-complexity search algorithm (LCS-Algorithm) to solve this joint optimization problem. Finally, the numerical results validate the performance of the proposed algorithm in terms of optimality and time efficiency.

In this paper, the non-orthogonal multiple access (NOMA) technology is applied in a vehicular edge computing network, in which mobile vehicles can offload partial computation tasks to the MEC server for remote execution. In this network, a long-term energy consumption minimization problem is presented by jointly optimizing the successive interference cancellation (SIC) order, transmit power, and computation resource allocation. To deal with the formulated problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC order subproblems. For the resource allocation subproblem, we exploit its convexity through the transformation and reparameterization and then derive the optimal solution by the Karush-Kuhn-Tucker (KKT) conditions. After that, we propose a low-complexity algorithm by leveraging tabu search to obtain the suboptimal SIC order. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to frequency division multiple access (FDMA).

Edge computing has been envisioned as a promising approach to enable the computation-intensive yet latencysensitive marine mobile services in the fifth generation and beyond wireless networks. In this paper, we investigate the edge computing in Marine Internet of Things (M-IoT) via the assistance of unmanned surface vehicles (USVs) subject to the eavesdropping attack. In particular, we consider a scenario in which USVs are exploited to provide cooperative jamming for the communication security at the physical layer when the high altitude platform (HAP) is performing task offloading transmission. We jointly optimize the workload offloaded by HAP, the HAP's transmission power as well as each USV's interfering signal power with the objective of minimizing the total energy consumption for completing the total workloads under the latency constraint. The bisection search method is first adopted to obtain the optimal solutions to the offloaded workload and each USV's interfering signal power. Further, by exploiting the monotonicity, the polyblock outer approximation based algorithm (POA-Algorithm) is designed to obtain the HAP's optimal transmission power. Finally, numerical results validate the optimality and effectiveness of our proposed algorithm by comparing it with the results of LINGO and different jamming schemes.