The proliferation of maritime activities has spurred the emergence of numerous computation-intensive and delay-sensitive marine applications and services. Given the inherent rationality, selfish nature, and limited computational abilities of marine devices, devising effective strategies to incentivize their participation in task processing become a critical challenge. In this article, we investigate the satellite-assisted marine multiaccess edge computing (MEC) and propose a two-tier task offloading scheme through a hybrid Stackelberg-Bargaining game approach to enhance offloading efficiency and maximize the utility of marine devices. Specifically, for the underwater acoustic communication, we consider the scenario where multiple autonomous underwater vehicles (AUVs), managed by maritime autonomous surface ships (MASSs), upload their collected data using nonorthogonal multiple access (NOMA) to optimize channel utilization. For the data transmission above the sea surface, we consider the scenario where a low-Earth orbit satellite (LEOS) functions as a space edge server to provide computing services, and MASS offloads workloads to LEOS through frequency division multiple access (FDMA) to prevent co-channel interference. we define the utility of AUVs, MASSs and LEOSs, and model the offloading process between AUVs and MASSs as a Stackelberg game, while representing the offloading interaction between MASSs and LEOSs as a Bargaining game. Additionally, we propose efficient algorithms to optimize AUV offloading strategies and MASS pricing strategies, while refining the bidding strategies for both MASSs and LEOSs. Simulation results demonstrate that the proposed algorithms significantly outperform benchmark schemes in achieving optimal solutions.

In this paper, we establish a multi-access edge computing (MEC)-enabled sea lane monitoring network (MSLMN) architecture with energy harvesting (EH) to support dynamic ship tracking, accident forensics, and anti-fouling through real-time maritime traffic scene monitoring. Under this architecture, the computation offloading and resource allocation are jointly optimized to maximize the long-term average throughput of MSLMN. Due to the dynamic environment and unavailable future network information, we employ the Lyapunov optimization technique to tackle the optimization problem with large state and action spaces and formulate a stochastic optimization program subject to queue stability and energy consumption constraints. We transform the formulated problem into a deterministic one and decouple the temporal and spatial variables to obtain asymptotically optimal solutions. Under the premise of queue stability, we develop a joint computation offloading and resource allocation (JCORA) algorithm to maximize the long-term average throughput by optimizing task offloading, subchannel allocation, computing resource allocation, and task migration decisions. Simulation results demonstrate the effectiveness of the proposed scheme over existing approaches.

With the continuous increase of maritime activities and services, it is imperative to establish the “Smart Ocean” architecture, to promote more efficient and safe marine information dissemination with high quality of service provisioning. In this paper, we propose a digital twin (DT) assisted maritime networking architecture based on a comprehensive review of the maritime networks, where the roles and superiority of DT are emphasized with the consideration of the network heterogeneity and dynamics. We utilize the interaction between virtual and physical entities to improve the flexibility and adaptability of the network, aiming at more efficient maritime network resource management and optimization. We then discuss the main challenges and some potential research issues in DT-assisted marine network design. The proposed maritime networking architecture based on DT can provide a good reference for network planning and deployment of maritime networking architecture to adapt to different marine environments.

In recent years, maritime accidents increase with the frequent maritime activities. Real-time transmission of search and rescue video can help decision makers make better search and rescue arrangements. To improve the efficiency of the maritime search and rescue tasks, we propose a Q-learning based delay sensitive routing protocol (QDSR) for maritime search and rescue networks. QDSR can dynamically adapt to the changes of the topology of maritime communication networks and optimize the end-to-end delay and packet delivery ratio through iterative learning. Simulation results demonstrate that the proposed QDSR can select an optimal path, thereby improve the reliability and timeliness of maritime emergency video transmission.

Voice capacity over wireless local area networks (WLANs) can be increased by the statistical multiplexing among on/off voice calls. However, in previously proposed schemes, the admitted voice calls that transit from silence state to talkspurt state are mixed up with the new voice calls to contend for the channel. The increase in the traffic load of new voice users could degrade the performance of ongoing voice calls. In this paper, we propose a novel MAC scheme for VoIP over WLANs, referred to as PAMA (Prioritized Adaptive Multiple Access). The key features of the proposed scheme are that 1) the admitted voice calls have higher priority access to the channel than the new voice calls, based on the fact that maintaining the required QoS of ongoing calls is more important than admitting new calls; 2) the dedicated contention window for admitted voice calls is dynamically adjusted according to the current estimation of the number of active ongoing voice calls to guarantee the QoS requirements; 3) a two-state Markov model is established to evaluate the system performance. Analytical and simulation results demonstrate that PAMA can increase the voice capacity while still satisfying the QoS of admitted voice calls.