Rongxi He

We propose LT-MFA-RMCSA, a multidimensional fragmentation-aware resource allocation scheme for the routing, modulation, core, and spectrum assignment (RMCSA) problem in dynamic space-division multiplexing elastic optical networks (SDM-EONs). Following a filter-then-evaluate architecture, the scheme first removes infeasible allocations according to distance-adaptive modulation and mutual inter-core crosstalk constraints and then ranks the remaining candidates using seven metrics that characterize intra-path resource states, adjacent-link spatial alignment, and release-time temporal coordination. A Likert-scale weighting strategy is integrated with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to balance fragmentation mitigation against path-resource consumption. Simulations on NSFNET and COST239 under both uniform and non-uniform traffic profiles show that LT-MFA-RMCSA consistently achieves the lowest connection and bandwidth blocking probabilities among the evaluated schemes while maintaining favorable residual-spectrum conditions. At the target blocking probability of 10 ⁻³ , it supports approximately 8.08%–10.62% more traffic than the independently generated hop-count-based heuristics across the two topologies under uniform traffic. The scheme also maintains sub-second average processing time under the evaluated implementation, supporting its applicability to online dynamic provisioning.

UAV-based free-space optical (UAV–FSO) communication provides high-rate airborne links for future sixth-generation (6G) and non-terrestrial networks. Reliable initial beam alignment is challenging because UAV motion, pointing disturbances, and optical-channel variations jointly affect whether the received power can reach the acquisition threshold and remain available for protocol confirmation. In practical UAV–FSO scenarios, position-estimation errors, wind-induced drift, platform vibration, transmit-side pointing jitter and turbulence-related atmospheric fading appear as transverse line-of-sight uncertainty and pointing error. Therefore, initial link establishment should be treated as an online optical beam-alignment decision problem under evolving uncertainty rather than a fixed geometric scanning problem. This paper proposes PAME-Net, a physics-aware multi-stream expert framework for UAV–FSO beam alignment. PAME-Net describes optical disturbances and estimation uncertainty using covariance, residual history, Kalman-filter state estimates, and task-stage information. Thesecues guide adaptive pointing, beam-divergence control, scan-region allocation, and stage switching. A multi-stream encoder preserves the physical semantics of these cues, while an expert-decoupled multi-head module separates drift compensation, uncertainty-aware scan control, and stage switching. A two-stage execution mechanism combines rapid blind-shot pointing with covariance-guided scan-assisted alignment, and physics-consistent sample enrichment improves training robustness. In the strong-wind scenario with large position-estimation-induced transverse pointing uncertainty, PAME-Net achieves a 97.0% alignment success rate within 1.0 s and reduces the mean successful alignment time to 0.548 s. It also maintains stable performance under increasing uncertainty and continuous-dwell requirements.

The migration from wavelength-division multiplexing (WDM) to elastic optical networks (EONs) leads to mixed-grid optical networks (MGONs), where fixed-grid and flex-grid nodes coexist. This hybrid infrastructure introduces critical survivability challenges: spectrum granularity mismatches increase protection resource overhead, while frequent optical-electrical-optical (O-E-O) conversions prolong recovery time. To address these issues, we propose a protection efficiency-based failure-independent path protecting (FIPP) p-cycle algorithm with topology partitioning (PE-TP-FIPP). The framework partitions the network into homogeneous domains based on node type and geographical proximity, thereby isolating capacity mismatches and avoiding unnecessary O-E-O conversions. Furthermore, this partitioning strategy balances protection efficiency and computational complexity by preventing excessive partitioning. Within this partitioned architecture, FIPP p-cycles are established for each connection request. A protection efficiency (PE) metric guides the selection of optimal p-cycles to minimize backup resources, while a cost of path (CoP) metric co-optimizes working path and protection allocation. To validate the proposed framework, performance baselines are first established in homogeneous environments, confirming the fundamental sharing efficiency of conventional shared backup path protection (SBPP) and FIPP p-cycles. Building upon these baselines, simulation results confirm that in MGONs, PE-TP-FIPP effectively mitigates the forced capacity expansion inherent in global routing, achieving highly competitive spare capacity redundancy (SCR) and shorter backup paths compared to global schemes. Consequently, the algorithm reduces the bandwidth blocking ratio by up to 55.31% and improves spectrum utilization by up to 26.2% versus benchmark schemes. Finally, extending the framework to accommodate differentiated services demonstrates its flexibility in fulfilling diverse service level agreements (SLAs).

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.

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.

Natural disasters such as earthquakes, floods, and fires often lead to the disruption of wireless communications infrastructure and the deterioration of the electromagnetic environment, which can ultimately lead to disaster relief failure. Therefore, this paper proposes an air–ground cooperative communication-lighting system that integrates autonomous aerial vehicle (AAV) with visible light communication (VLC) technologies. With its flexible deployment and strong environmental adaptability, the system enables rapid deployment of emergency communication networks, simultaneously providing lighting services after disasters. However, the system faces multiple challenges in task scheduling, communication performance assurance, energy management, and service continuity in post-disaster deployment. To address these challenges, we design a cooperative optimization framework based on the twin delayed deep deterministic policy gradient (TD3) and dynamic adaptive particle swarm optimization (DAPSO), and introduce an energy-aware mechanism for joint optimization of AAV trajectory control and hover deployment. The framework enables efficient switching and coordination mechanisms between environmental uncertainties and diversified service objects. It not only provides continuous and stable communication and lighting services but also takes into account energy efficiency. Simulation results verify that the proposed framework significantly improves system performance, achieving 11.2% lower energy consumption compared with traditional RF schemes while improving the average data rate by 2.9% and illumination quality by 7.4%.

Collaborative unmanned vehicles affiliated with terrestrial or shipboard edge-stations with certain spectrums serving as a maritime relay platform have drawn more attentions. This paper focuses on the service migration orienting the costal ship broadband communication requirement supported by base stations and designs a multi-relay system. The multi-relay system is consist of multiple unmanned aerial vehicles (UAVs) and features content buffering and network encoding functions. Thanks to the capability of flexible storage and seamless integration with the flight control system, buffer can better facilitate the role of UAV-relay and -edge computing. And network encoding not only enhances the data-carrying capacity and reduce the forwarding rounds, but also can better slim down the data to be transmitted and alleviate the pressure on buffer. Correspondingly, a maritime communication model is proposed based on network encoding and multi-UAV-associated relay. Subsequently, a container migration algorithm based on UAV geo-position and resource information is proposed to adapt to the arbitrary velocities and navigation directions of ships. Numerous results testify the advantages of our proposed algorithm in terms of transmission rate, service migration efficiency and energy consumption.

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.

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.