The maritime Internet of Things (MIoT), a maritime version of the Internet of Things (IoT), is envisioned as a promising solution that can provide ubiquitous connectivity over land and sea. Due to the rapid development of maritime activities and the maritime economy, there is a growing demand for computing-intensive and latency-sensitive maritime applications requiring various energy consumption, communication, and computation resources, posing a significant challenge to MIoT devices due to their limited computational ability and battery capacity. Mobile Edge Computing (MEC), which can handle computation tasks at the network’s edge more efficiently and with less latency, is emerging as a paradigm for fulfilling the ever-increasing demands of MIoT applications. However, the exponential increase in the number of MIoT devices has increased the system’s energy consumption, resulting in increased greenhouse gas emissions and a negative impact on the environment. As a result, it is vital for MIoT networks to take traditional energy usage minimization into account. The integration of renewable energy-harvesting capabilities into base stations or MIoT devices possesses the potential to reduce grid energy consumption and carbon emissions. However, making an effective decision regarding task offloading and resource allocation is crucial for maximizing the utilization of the system’s potential resources and minimizing carbon emissions. In this paper, we first propose a green MEC-enabled maritime IoT network architecture to flexibly provide computing-intensive and latency-sensitive applications for MIoT users. Based on the architecture, we formulate the joint task offloading and resource allocation problem by optimizing the total system execution efficiency (including the total size of completed tasks, task execution latency, and the system’s carbon emissions) and then propose a deep-deterministic-policy-gradient-based joint optimization strategy to solve the problem, eventually obtaining an effective resolution through continuous action space learning in the changing environment. Finally, simulation results confirm that our proposal can yield good performance in system execution efficiency compared to other benchmarks; that is, it can significantly reduce the system’s carbon emissions and tasks’ delay and improve the total size of completed tasks.

Mobile edge computing (MEC) powered by renewable energy, is promising to provide green computing for the Internet of Things (IoT). However, the unpredictable renewable energy and computing demands usually cause a mismatch between system requirements and energy supply, resulting in wasted surplus energy or energy supply shortage. Hence, it is crucial to improve energy efficiency and system performance, that is“, make the best use of generated energy” and “make the best use of system’s talents” simultaneously. In this paper, we focus on some islands far from the mainland, with growing computation requirements for environmental monitoring and navigation safety, and propose a device-to-device (D2D) collaboration based SDN-MEC framework in Island IoT employing tidal energy. Following that, we formulate a multi-objective energy scheduling system performance association (MESPA) problem to minimize the long-term average task execution loss (TEL), including energy consumption per bit executed, overall execution latency, and energy waste caused by underutilization of tidal energy, with the constraints of energy queue stability, peak transmission power, and CPU-cycle frequency. To address this challenging problem, we propose a Lyapunov-based multi-dimensional resource allocation and computation offloading (LMDRACO) algorithm and transform the original problem into several individual subproblems in each time slot. These subproblems are then solved using convex decomposition and submodular methods. Theoretical research shows that the LMDRACO algorithm can achieve a O(1/V), O(V) tradeoff between TEL and energy queue length. Numerical results show that the proposed algorithm significantly improves both system performance and energy efficiency compared to baseline schemes.

During the brownfield migration process from wavelength division multiplexing networks to elastic optical networks (EONs), fixed-grid and flex-grid devices coexist in the network, resulting in mixed-grid optical networks (MGONs). To improve the resource utilization of MGONs, we propose an auxiliary-graph-based traffic grooming with hybrid single/multipath routing (ATGHR) algorithm for MGONs. Specifically, we propose a hybrid single/multipath routing (HSMR) scheme to reasonably split the connection request into multiple sub-requests with the aim of satisfying the different transmission capabilities of flex-grid and fixed-grid nodes and avoiding excessive connection division. Based on it, we formulate an integer linear programming (ILP) model using traffic grooming (TG) with the objective of minimizing the spectrum and transponder usage for static MGON scenarios. Moreover, for a large-scale MGON where the ILP model is not tractable, we develop an auxiliary graph (AG) to support better characterization of TG and distance-adaptive modulation in MGONs and present several TG policies for different purposes by adjusting the edge weights of AG. Based on the AG, we propose a heuristic algorithm combined with TG and HSMR to further improve resource utilization. Extensive simulations are conducted to evaluate our proposals. Simulation results show that the ILP model can achieve the best performance in a small-scale network. Moreover, in large-scale networks, different TG policies for the same algorithm can achieve different targets, and our ATGHR has the best bandwidth blocking ratio compared with two benchmark algorithms using the same TG policy.

Federated learning (FL) effectively preserves data privacy but faces challenges such as the significant communication overhead from frequent model updates between user equipment (UE) and mobile edge computing servers (MECSs), performance degradation under nonindependently identically distributed (non-i.i.d.) data distributions, and UE dropouts due to resource constraints. Since MECSs cannot mandate participation, effective incentives are crucial to secure sufficient training data. This article proposes an incentive-driven secure clustered FL (CFL) framework supporting operations across base stations (BSs). The framework incorporates dual-privacy protection through end-to-end encryption for secure cross-BS model exchange and differential privacy during aggregation. To accurately evaluate contributions, we design a dual-component metric combining model distance with local accuracy. Furthermore, we formulate the incentive problem as a two-layer Stackelberg game that integrates virtual rewards with tangible bandwidth allocation, incorporating amplified rewards for cross-BS participation. In this game, MECSs act as leaders aiming to maximize their long-term utility, defined as the weighted improvement in global model accuracy minus incentive costs, while ensuring sustained UE engagement. UEs, as followers, maximize their net utility, calculated as incentives derived from local accuracy, model similarity, and cross-BS reward minus their computational and energy costs, under individual resource constraints. The optimal UE strategy is derived using the bisection method, and a deep reinforcement learning-based algorithm is developed for MECS decision-making under incomplete information. Experimental results demonstrate that our approach effectively screens out low-contributing UEs that hinder convergence while incentivizing high-performing participants, leading to efficient and reliable model training.

The popularity of maritime activities not only exacerbates the carbon footprint (CF) but also places higher demands on Maritime Internet of Things (MIoTs) to support heterogeneous MIoT devices (MIoTDs) with different prioritized tasks. High-priority tasks can be processed cooperatively via local computation, offloading to nearby MIoTDs (helpers), or offloading to edge servers to ensure their timely and successful completion. Due to the differences in energy availability and rechargeability, MIoTDs exhibit distinct energy states, impacting their operational behaviors. We propose the Energy Anxiety Level (EAL) to quantify these states: Higher EAL tends to lead to increased packet dropping and earlier shutdown. Although low-EAL MIoTDs seem preferable as helpers, their scarce residual computational resources after local task completion may cause offloaded high-priority tasks to drop or time out. Therefore, helper selection should jointly consider candidate MIoTDs’ EALs and loads to evaluate their unsuitability. This paper addresses the problem of differentiated task offloading and resource allocation in MIoTs by formulating it as a mixed integer nonlinear programming model. The objective is to minimize system-wide carbon footprint (CF), packet loss, helper unsuitability risk, and high-priority task latency. To solve this complex problem, we decompose it into two subproblems. We then design algorithms to determine optimal offloading patterns, task partitioning factors, MIoTD transmission powers, and computation resource allocation for MIoTDs and edge servers. Simulation results demonstrate that our proposal outperforms benchmarks in reducing CF and EAL, lowering high-priority task latency, and improving task completion ratio.