Search Results (170)

Timely vehicular sensing is important for traffic monitoring, cooperative driving, and road-safety management. High mobility, time-varying wireless conditions, and limited edge resources nevertheless make information freshness difficult to maintain. This paper studies age of information (AoI) minimization in a three-layer UAV-assisted edge network comprising vehicle devices (VDs), unmanned aerial vehicles (UAVs), and a cloud center (CC). VDs periodically generate sensor-data packets, UAVs provide mobile edge processing and data-relaying services, and the CC coordinates system-wide resource allocation. The joint optimization of sensor-data transmission, UAV movement, packet processing, computation offloading, and bandwidth allocation is formulated within a cooperative multi-agent framework. To solve this problem, we propose a collaborative heterogeneous federated actor–critic (CHFAC) framework. Its significance-aware federated learning mechanism evaluates local model updates according to update significance, alignment with the global learning direction, and training stability and uses the resulting contribution scores for non-uniform agent selection and contribution-weighted aggregation. In the considered simulation setting, evaluation over 1000 test episodes yields an average AoI of $7.45\pm 1.65$ and a worst-case AoI of $38.72\pm 24.06$. The average AoI is $79.0\%$, $63.9\%$, and $29.2\%$ lower than that obtained by the implemented HF-MARL, H-MAAC, and non-federated baselines, respectively. These results demonstrate the effectiveness of CHFAC for freshness-aware vehicular sensing in dynamic UAV-assisted edge environments. Full article

As an emerging communication paradigm, semantic communications (SC) focuses on the semantic content of information transmission, aiming to achieve more efficient and accurate information interaction. However, SC requires an in-depth analysis of information semantics and accurate adaptation to application scenarios. With the substantial growth in information volume, richness, and application diversity, the resources, constraints, and requirements associated with SC systems have also increased. Optimal resource allocation (ORA) of SC can effectively deal with these practical problems, a key technology for improving communication efficiency. In complex network environments and special scenarios, communication resources are limited. Increasing transmission efficiency, ensuring accuracy and quality of information, and reducing energy consumption can be achieved by rationally allocating bandwidth, power, and related resources. Since SC technology is still in its early stages of development, there is a lack of a comprehensive review of ORA for SC in the existing literature. This paper provides a comprehensive review of ORA for SC. First, the basic concepts, characteristics and development motivations for SC and ORA are reviewed. Then, a comprehensive analysis of the key technologies for ORA in SC is presented, covering end-to-end and semantic network multi-link ORA. These technologies include technology for predicting resource demand based on semantic understanding, resource optimization technology for semantic information (SI) processing and transmission, and technology for dynamic resource adjustment. Then, a conceptual ORA framework is synthesized from the reviewed technologies to unify key design principles and provide a foundation for future research. In addition, this paper provides research prospects in future trends of ORA for SC, comprising emerging artificial intelligence (AI) and machine learning, laying the foundation for next-generation intelligent communication networks. Finally, this paper points out the main application direction of ORA in SC, which reflects the practical significance of this study. Full article

The widespread use of location-based services (LBSs) has led to the continuous collection of user location and trajectory data, increasing the risk of privacy leakage and creating a persistent tradeoff between privacy protection and data utility. To address this problem in discrete location query scenarios, this paper proposes a single-point location privacy protection method based on Q-R tree retrieval and differential privacy, termed QRDPP. QRDPP combines the adaptive spatial partitioning capability of a Q-tree with the minimum bounding rectangle (MBR)-based indexing capability of an R-tree. It applies an improved geometric privacy budget allocation strategy to leaf nodes and an arithmetic allocation strategy to non-leaf nodes, followed by Laplace perturbation of the corresponding location data and node information. For continuous trajectory query scenarios, this paper proposes a spatiotemporal generalization and differential privacy method, termed STG-DPTP, to address inadequate temporal protection, inappropriate generalization, and trajectory distortion. STG-DPTP performs hierarchical spatiotemporal clustering, separately models temporal and spatial distributions using Gaussian kernel density estimation, dynamically optimizes bandwidth parameters through Bayesian optimization, selects representative candidate subsets using the exponential mechanism, and generates protected trajectories through constrained sampling. Experiments on the GeoLife dataset evaluate the proposed methods in terms of query accuracy, computational efficiency, spatial trajectory similarity, reconstruction error, adversarial uncertainty, and temporal preservation. The results show that QRDPP improves the utility and efficiency of privacy-preserving spatial queries, while STG-DPTP better preserves the spatial distribution, trajectory structure, and temporal characteristics of the original data under the adopted differential privacy framework. Full article

The growth of cloud services and immersive applications based on extended reality (XR), including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), imposes increasingly demanding requirements on access networks. The large-scale integration of IoT devices in smart buildings further increases the need for high throughput, low latency and jitter, and reliable connectivity. Traditional Fiber-to-the-Home (FTTH) networks with a single access point (AP) become quite limiting when there are high performance requirements, with many users with indoor mobility and high device density. Fiber-to-the-Room (FTTR) is an extension of FTTH, which brings fiber optics to each room of the house through a Main FTTR Unit (MFU) and several Sub FTTR Units (SFU) along with the APs, with centralized device management. Green FTTR networks are characterized by their energy efficiency through centralized control of signal power and Dynamic Bandwidth Allocation (DBA) management. The fgONT architecture allows for deterministic network slicing, enabling the allocation of specific resources isolated from the rest of the network traffic, allowing for predictable bandwidth and QoS. This work presents a framework that allows for a comparative analysis of FTTR and FTTH networks in different scenarios in order to ensure a compromise between transmission quality, network energy efficiency, and the user’s perceived experience. The results obtained show that, in high device density scenarios, FTTR reduces the average packet loss from 52.69% to less than 0.08%, decreases the average latency from 151 ms to less than 2 ms, and maintains the overall QoE above 0.974, compared to 0.27 in FTTH with a single AP. Full article

The risk of cross-domain cascading failures in cyber–physical power systems (CPPSs) has become increasingly significant. The existing studies generally employ communication networks with static routing and fixed bandwidth allocation, which are insufficient to cope with dynamic load fluctuations and unexpected faults. To address these limitations, this paper incorporates the flexible optical-layer resource scheduling capability of software-defined optical networks (SDONs) and proposes an SDON-enabled CPPS model along with a control network optimization method. First, a three-layer CPPS architecture based on the SDON framework is constructed to characterize the interaction mechanism between the control layer and the data forwarding layer, as well as the fault propagation paths. Second, a control network optimization configuration model with the objective of minimizing energy consumption is established in which primary–backup routing schemes and wavelength resources are jointly designed using a mixed-integer linear programming approach. Finally, with load shedding rate adopted as the evaluation metric, a multidimensional vulnerability assessment method for CPPSs is proposed. The simulation results demonstrate that, compared with random control networks, the optimized CPPS reduces the average energy consumption by 53.6% and 34.2% under single-fault and multiple-fault scenarios, respectively, while the load shedding rate is reduced by 23% and 37.2%, thereby verifying the effectiveness of the proposed method. Full article

In cloud computing, multi-tenant shared storage is widely used for efficiency. ZNS SSDs have become a popular choice due to their high throughput and low latency. However, they still face challenges: the zone structure is not exposed to tenants, making performance control difficult, and the multi-namespace design can lead to wasted performance when tenant workloads are highly imbalanced. This paper proposes a tenant pressure-aware performance isolation scheme, called IP-Zone, that partitions users into independent regions and maximizes zone parallelism through cross-resource allocation and flexible address mapping. For severely unbalanced loads, a complementary load-aware strategy dynamically reallocates bandwidth among tenants to improve SSD utilization. Experiments show that this scheme improves IOPS and throughput by 34.6% and 25.0% over traditional channel isolation, while average and tail latencies are reduced by 76.3% and 36.0% compared to black-box sharing. Full article

Rapid preliminary reconnaissance of Critical Transportation Hubs (CTHs) by UAV swarms is vital during post-disaster rescue operations. However, limited camera fields of view, large heading variances, and GNSS multipath errors near massive steel-concrete structures complicate multi-view cooperative perception. This paper introduces a discrete, vision-based cooperative perception framework utilizing a decentralized anchor-wingman architecture. The pipeline integrates a Prob-IoU-optimized YOLO26m-OBB detector to extract oriented infrastructure footprints. To handle severe rotational discrepancies without IMU priors, a global scene registration cascade—combining SuperPoint and an Optimal Transport-driven LightGlue—is employed to establish robust geometric correspondences. Furthermore, a Projected Polygon Intersection over Union (Proj-IoU) mechanism, coupled with an RMSE-weighted spatial fusion strategy, dynamically associates and deduplicates overlapping targets across distributed views. Experimental results indicate that the framework achieves a low pixel-level RMSE of 2.12 pixels on the source domain and maintains a highly stable 2.36 pixels during zero-shot cross-domain testing (SUES-200 dataset), successfully resolving extreme heading variances up to 270°. The Proj-IoU mechanism resolves multi-source redundancies—collapsing overlapping projections by over 50%—bounding the localization error to approximately 1.06 m. Operating at 6.7 FPS on edge hardware via low-bandwidth tensor transmission, this system provides a rigorous geometric foundation for autonomous swarms, enabling downstream collision-free trajectory planning and Multi-Target Task Allocation (MTTA) in GNSS-denied environments. Full article

Very-high-resolution (VHR) multispectral satellite imagery contains rich semantic information, yet its real-time transmission is constrained by limited satellite-to-ground bandwidth and dynamic channel impairments. Conventional communication schemes prioritize pixel-level reconstruction, resulting in large transmission overhead and poor robustness under unfavorable channel conditions. To address these challenges, an end-to-end task-oriented semantic communication framework for remote sensing downstream recognition tasks, termed Semantic Transmission Architecture for Remote Sensing (STARS), is proposed. To improve transmission efficiency for very-high-resolution remote sensing images with highly redundant background regions, a Semantic Feature Reweighting Module (SFRM) is introduced to dynamically evaluate token-level semantic importance and adaptively allocate transmission resources to task-critical features. Furthermore, vector quantization and a practical digital transmission chain are jointly integrated to achieve efficient semantic compression, while dynamic channel variations are incorporated during training to improve robustness under fading channel conditions. Experimental results on the DOTA dataset demonstrate that STARS consistently outperforms conventional schemes and existing semantic baselines under Rician fading channels, validating the effectiveness of semantic-aware feature allocation for bandwidth-efficient VHR imagery transmission. Full article

Dynamic task allocation for unmanned aerial vehicles (UAVs) is often constrained by stale and asymmetric information. Variants of the Consensus-Based Bundle Algorithm (CBBA) can waste bandwidth by transmitting every state or bid update without distinguishing assignment-altering information from irrelevant perturbations. This paper proposes Assignment-Consistent Dynamic Multi-UAV Task Allocation (AC-DMTA), a decision-centric framework in which agents communicate only when local uncertainty may invalidate the current assignment. AC-DMTA represents stale records with uncertainty sets and converts point bids into interval bids, enabling task-winner and bundle-stability certificates to suppress assignment-irrelevant messages. These certificates provide local sufficient conditions for certifying the stability of task-winner and bundle decisions within the modeled uncertainty envelopes and candidate sets, allowing assignment-irrelevant communication to be skipped. Timestamp and recency records further resolve asynchronous conflicts without broad resynchronization. Across four benchmark environments and 20 paired seeds, AC-DMTA sends fewer bytes and directed messages than periodic CBBA, Event-Driven Consensus-Based Bundle Algorithm (ED-CBBA), Clustered CBBA, and Two-Level Clustered Consensus-Based Bundle Algorithm (TLC-CBBA), achieving byte reductions of 10.4–91.1% across baseline–scenario pairs while maintaining competitive reward and task-completion rates. Under the tested delay and packet-loss stress conditions, AC-DMTA also achieves the lowest number of bytes per completed task among the compared methods. Full article

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving edge intelligence because it enables geographically distributed devices to collaboratively train a shared model without transferring raw data to a central cloud. This capability is particularly valuable for 5G and emerging 6G networks, where edge-native services are required to satisfy stringent latency, bandwidth, and privacy constraints while operating on highly heterogeneous devices and time-varying wireless channels. In practice, however, synchronous FL is often constrained by straggling clients with limited computation capability or unfavorable communication conditions, which increases round latency and reduces overall resource efficiency. To address this challenge, this study develops a rigorously structured framework for dynamic client selection and radio resource allocation in heterogeneous wireless edge environments. Each FL round is formulated as a latency-aware scheduling problem that jointly captures local computation time, uplink transmission time, minimum participation constraints, and resource block assignment. On this basis, we propose a Dynamic Client Selection and Resource Allocation (DCS-RA) method that integrates computation-aware, channel-aware, and fairness-aware scoring with greedy resource block allocation guided by marginal completion time reduction. The study further provides a clear methodological structure, workflow visualization, literature-grounded justification, dataset documentation, and uncertainty-aware result reporting. Under the reported simulation setting with 100 clients and 20 resource blocks, DCS-RA reduces the average round completion time from 1.92 s to 1.55 s on MNIST and from 2.02 s to 1.57 s on CIFAR-10, corresponding to improvements of 19.39% and 22.47%, respectively. Standard deviation reductions of 70.59% and 80.77% further indicate improved round-to-round stability and more reliable training behavior. These results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks. Full article

Existing solutions for resource allocation in Zero Trust (ZT) SDN networks treat security, resilience, and efficiency separately; centralised approaches violate data privacy; deep reinforcement learning (DRL) lacks trust dynamics; and federated learning (FL) has not incorporated graph neural networks (GNNs) or failure resilience. These limitations motivate a federated GNN-DRL framework that preserves data locality while jointly optimising trust, resilience, and performance. Each domain trains a local GNN-DRL agent with a GNN encoder for topology awareness and a hybrid DRL module (Deep Q-Network for discrete failover actions and Soft Actor-Critic for continuous bandwidth tuning) under a stochastic trust evolution modelled by stochastic differential equations (SDEs) with a reflection mechanism to ensure well-posedness. Three pseudo-code algorithms detail client-side training (Algorithm 1), server-side q-fair aggregation (Algorithm 2), and local gradient updates (Algorithm 3). Extensive simulations on a 100-node topology divided into five domains demonstrate that: (i) under low-to-moderate failures (≤20%), trust violations remain below 1.8%, and even under severe failures (40%), violations stay at 4.2% (within the 5% ZT boundary); (ii) recovery time is reduced by 53%; (iii) throughput under failures improves by 32%; (iv) compromise resistance reaches seven nodes (vs. three centralised); (v) attack surface shrinks to four nodes (vs. 98 baseline); and (vi) lateral movement containment attains 98%. The federated framework approaches centralised performance while preserving data locality, offering a practical and secure solution for multi-domain ZT, SDN networks. Full article

The Internet of Vehicles (IoV) is a decentralized network architecture that enables autonomous driving, real-time applications, infotainment services, and seamless vehicle-to-everything communication. While infotainment systems enhance the user experience by providing entertainment and navigation features, their high data demands can cause network congestion, potentially delaying mission-critical messages and compromising safety and reliability. Further, the increasing volume of connected devices and data traffic exacerbates these challenges, resulting in high latency, low throughput, and reduced network efficiency. To address this, we propose a novel lightweight adaptive network slicing strategy with four dedicated slices and priority-weighted dynamic bandwidth allocation for IoV networks to mitigate congestion and ensure the availability of the required bandwidth for critical communications. The proposed mechanism defines four distinct network slices, allocating resources to balance infotainment and mission-critical needs. Simulation results demonstrate that our approach achieves ultra-low latency (<5 ms), near-zero packet loss (<0.5%), and high throughput (53 Mbps for infotainment), significantly outperforming existing methods, and ensuring reliable communication for safety-critical tasks while improving spectrum utilization. Findings validate implementing network slicing in IoV environments, paving the way for efficient, congestion-free, and high-performance vehicular networks. Full article

AVs are also bound to capitalize on 5G networks, which creates crucial challenges in the adaptable management of resources because they need very low latency, a high-speed connection, and energy-efficient functionality. Older approaches to optimizing resource allocation in the high-frequency changing vehicle environment fail to deliver as mobility trends and network status constantly adapt and change. To overcome these problems, we suggest a new Deep Reinforcement Learning (DRL)-based algorithm, which is aimed at optimizing the allocation of resources to AVs. This model combines a Spatiotemporal Graph Convolution Network (ST-GCN), Gated Recurrent Units (GRU), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to create a unified model. The ST-GCN is successful at both capturing the dynamic space relationship between vehicles and between vehicles and roadside infrastructure, and also gives a complete picture of network topology. GRU uses traffic and communication information to forecast future mobility patterns and bandwidth demand of each agent and therefore allocate resources proactively. The MADDPG algorithm is used to enable decentralized but coordinated decision-making among AVs, which enables the realization of dynamic policies of bandwidth allocation in real-time. Simulations using such aspects as a realistic Rayleigh fading channel model, a node density of 100 vehicles/km², and 100 MHz of bandwidth prove the effectiveness of the framework extensively. We find that the end-to-end latency increase is reduced by up to 30%, and the system throughput is increased by up to 28, and the energy efficiency is increased by an average of 40 percent in comparison with the baseline techniques. Such results confirm our framework to be a plausible solution to building effective and sustainable communication systems to enable AVs to cooperate in the information exchange of important data. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

Efficient task–channel allocation in satellite communication networks becomes particularly challenging when tasks are subject to both time and frequency constraints, and when resource failures or environmental changes invalidate an initially feasible allocation. Existing studies often treat static allocation and dynamic adaptation separately, lacking a unified framework that ensures both a low resource fragmentation rate and low reconfiguration cost. This paper proposes a two-stage approach that integrates static task–channel allocation with dynamic reconfiguration. In the static stage, a greedy algorithm is developed to assign tasks to channels under time-window, bandwidth, and conflict-free constraints, aiming to achieve as low a resource fragmentation rate as possible within the heuristic search. When channel failures occur, a heuristic search-based reconfiguration algorithm is proposed to generate a sequence of reconfiguration events that transitions the initial static allocation strategy step by step to a feasible target static allocation strategy, while maintaining constraint satisfaction and an acceptable resource fragmentation rate throughout the process. Comparative experiments on both small-scale and large-scale datasets demonstrate that the unified framework effectively balances allocation quality, low-cost and compact dynamic reconfiguration, and adaptability in dynamic network environments. Full article