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Resource Allocation for Heterogeneous Statistical QoS Provisioning Over Internet-of-Vehicles via Hierarchical Deep Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhou, Yuchen Yu, Fei Richard Ren, Mengmeng Yang, Long Chen, Jian Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The high-dynamic characteristics of vehicular networks make it difficult to satisfy strict deterministic quality-of-service (QoS) conditions. Statistical QoS provisioning instead of deterministic QoS provisioning can provide an efficient way to satisfy delay-bounded QoS requirements of vehicular services. Different from the existing work, this paper considers the heterogeneity of statistical QoS provisioning for delay-sensitive services in Internet-of-Vehicles (IoV), where the overall delay violation probability is limited in each time slot for mission-critical applications, and the queue length bound along with its higher-order statistics is limited from a long-term perspective for infotainment services. A long-term resource optimization problem is formulated to minimize the average energy consumption with heterogeneous statistical latency guarantees. To provide a stable and fast solution, Lyapunov optimization method is first leveraged to transform the formulated stochastic problem to a series of short-term deterministic optimization sub-problems. Afterwards, a novel two-level hierarchical deep reinforcement learning (H-DRL) framework is presented, where the knowledge sharing is further introduced to realize the fast model training and resource decision-making with QoS performance guarantees. Simulation results verify the convergence speed and the training accuracy of the presented H-DRL framework, and it also shows the superiority of the proposal in the perspective of the end-to-end delay and the queue stability for heterogeneous delay-bounded services under high-dynamic environments of IoV. © 2025 IEEE.

关键词： Deep reinforcement learning

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Small-UAV GPS Spoofing Attack Detection and Multilabel Classification

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Security and Privacy 2025年第3期8卷

作者： Alsubai, Shtwai Sampedro, Gabriel Avelino Almadhor, Ahmad Karovic, Vincent Hejaili, Abdullah Al Abbas, Sidra College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City Philippines Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka Saudi Arabia Department of Information Management and Business Systems Faculty of Management Comenius University in Bratislava Bratislava Slovakia Faculty of Computers & Information Technology Computer Science Department University of Tabuk Tabuk Saudi Arabia Department of Computer Science COMSATS University Islamabad Pakistan

Small unmanned aerial vehicles (UAVs) rely heavily on global positioning systems (GPS) for navigation. Nevertheless, it is possible to launch GPS spoofing attacks, which makes the UAVs' ability to track themselves inexplicable. To solve this issue, this study proposed an ensemble stacking method to identify GPS signal spoofing in small UAVs. It contributes to the dataset extraction that contains information from real GPS signals collected from various locations to simulate an automated and stationary autonomous vehicle using an accessible software radio peripheral unit established as a GPS receiver and 13 features extracted from eight parallel mediums. We use a grid search approach to identify and fine-tune hyperparameters to improve the detection rate. The results show that the proposed approach performed well in traditional machine learning approaches and accurately predicts spoofing attacks with an accuracy of 98%. The results suggested that the proposed ensemble stacking performs better than alternative methods. The findings show that the proposed approach outperforms spoofing small-UAV GPS signal diagnosis techniques. © 2025 John Wiley & Sons Ltd.

关键词： Global positioning system

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Public space security management using digital twin technologies

arXiv

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arXiv 2025年

作者： Zindros, Stylianos Chronis, Christos Radoglou-Grammatikis, Panagiotis Argyriou, Vasileios Sarigiannidis, Panagiotis Varlamis, Iraklis Papadopoulos, Georgios Th. Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom

As the security of public spaces remains a critical issue in today’s world, Digital Twin technologies have emerged in recent years as a promising solution for detecting and predicting potential future threats. The applied methodology leverages a Digital Twin of a metro station in Athens, Greece, using the FlexSim simulation software. The model encompasses points of interest and passenger flows, and sets their corresponding parameters. These elements influence and allow the model to provide reasonable predictions on the security management of the station under various scenarios. Experimental tests are conducted with different configurations of surveillance cameras and optimizations of camera angles to evaluate the effectiveness of the space surveillance setup. The results show that the strategic positioning of surveillance cameras and the adjustment of their angles significantly improves the detection of suspicious behaviors and with the use of the DT it is possible to evaluate different scenarios and find the optimal camera setup for each case. In summary, this study highlights the value of Digital Twins in real-time simulation and data-driven security management. The proposed approach contributes to the ongoing development of smart security solutions for public spaces and provides an innovative framework for threat detection and prevention. © 2025, CC BY.

关键词： Subway stations

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Integrated Robotic Aerial Base Stations Deployment and Backhaul Design in 6G Multihop networks

Integrated Robotic Aerial Base Stations Deployment and Backh...

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IEEE Conference on Wireless Communications and Networking

作者： Wen Shang Yuan Liao Vasilis Friderikos Halim Yanikomeroglu Center of Telecommunication Research King's College London London U.K. Department of Systems and Computer Engineering Non-Terrestrial Networks Lab Carleton University Ottawa Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

关键词： 6G mobile communication Base stations Wireless networks Spread spectrum communication Pressing Reliability engineering Resource management Vehicle dynamics Robots Backhaul networks

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Spatiotemporal dynamics in a network of modified Morris–Lecar neurons with nonlinear magnetic flux diffusion

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The European Physical Journal Special Topics 2025年 1-19页

作者： Seralan, Vinoth Leela Lakshmi, S. Suresh Babu, Kunchala Rajagopal, Karthikeyan Center for Nonlinear and Complex Networks SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vemu Institute of Technology Chittoor India Department of Computer Science Engineering PSCMR College of Engineering and Technology Vijayawada India Center for Cognitive Science Trichy SRM Medical College Hospital and Research Center Trichy India Center for Research Easwari Engineering College Chennai India

In this work, a network of Morris–Lecar neurons with electromagnetic induction is imposed with nonlinear magnetic flux diffusion. We study wave propagation in a network of Morris–Lecar (ML) neurons with magnetic flux diffusion, connected to the local nodes of the nearest neighbors in a $$110 \times 110$$ lattice of neurons with periodic boundary conditions. First, we explore the effect of various initial conditions on the modified ML neuron network without imposing external stimuli. Subsequently, we apply external stimuli at different positions and study wave propagation by changing the amplitude and frequency of the stimuli. The effects of varying Nernst potential of potassium ions, coupling strengths, and flux constants are also analyzed. The resulting collective dynamics of the considered neuronal network are provided in snapshots with different model parameters. This study offers a novel perspective on wave propagation in networks of biological neurons.

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Fatemeh Ahmadvand Mahmood Hosseini Imani Hamid Taheri Andani Mansour Taheri Andani Department of Control and Computer Engineering Politecnico di Torino Torino Italy Department of Energy (DENERG) Politecnico di Torino Torino Italy Department of Electronic Engineering Isfahan University of Technology Isfahan Iran School of Manufacturing Systems and Networks Arizona State University Tempe AZ

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price.

关键词： Machine learning algorithms Accuracy Electricity Neural networks Predictive models Prediction algorithms Robustness Forecasting Long short term memory Optimization

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Movable Frequency Diverse Array for Wireless Communication Security

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IEEE Transactions on Communications 2025年

作者： Cheng, Zihao Si, Jiangbo Li, Zan Liu, Pengpeng Huang, Yangchao Al-Dhahir, Naofal Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China AFEU College of Information and Navigation China University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

Frequency diverse array (FDA) is a promising antenna technology to achieve physical layer security by varying the frequency of each antenna at the transmitter. However, when the channels of the legitimate user and eavesdropper are highly correlated, FDA is limited by the frequency constraint and cannot provide satisfactory security performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where the positions of antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the secrecy capacity by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on block successive upper-bound minimization (BSUM) method. Moreover, to evaluate the security performance provided by MFDA, we introduce two benchmark schemes, i.e., phased array (PA) and FDA. Simulation results demonstrate that MFDA can significantly enhance security performance compared to PA and FDA. In particular, when the frequency constraint is strict, MFDA can further increase the secrecy capacity by adjusting the positions of antennas instead of the frequencies. © 1972-2012 IEEE.

关键词： Convex optimization

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Cooperative Digital Healthcare Task Scheduling and Resource Management in Edge Intelligence Systems

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Tsinghua Science and Technology 2025年第2期30卷 926-945页

作者： Xing Liu Jianhui Lv Byung-Gyu Kim Keqin Li Hongkai Jin Wei Gao Jiayuan Bai Department of Oncology The First Affiliated Hospital of Jinzhou Medical UniversityJinzhou 121012China Department of Imaging The First Affiliated Hospital of Jinzhou Medical UniversityJinzhou 121012China Department of Networks Peng Cheng LaboratoryShenzhen 518057China Tsinghua Shenzhen International Graduate School Shenzhen 518055China Department of Artificial Intelligence Engineering Sookmyung Women’s UniversitySeoul 04310Republic of Korea Department of Computer Science State University of New YorkNew PaltzNY 12561USA School of Life Sciences Jinzhou Medical UniversityJinzhou 121001China School of Basic Medicine Jinzhou Medical UniversityJinzhou 121001China School of First Clinical Medicine Jinzhou Medical UniversityJinzhou 121001China

The rapid growth of digital healthcare applications has led to an increasing demand for efficient and reliable task scheduling and resource management in edge computing ***,the limited resources of edge servers and the need to process delay-sensitive healthcare tasks pose significant *** solutions often need help to balance the trade-off between system cost and quality of service,particularly in resource-constrained *** address these challenges,we propose a novel cooperative task scheduling and resource management framework for digital healthcare applications in edge intelligence *** approach leverages a two-step optimization strategy that combines the Multi-armed Combinatorial Selection Problem(MCSP)for task scheduling and the Sequential Markov Decision Process(SMDP)with alternative reward estimation for computation *** MCSP-based scheduling algorithm efficiently explores the combinatorial task scheduling space to minimize healthcare task completion time and *** SMDP-based offloading strategy incorporates alternative reward estimation to improve robustness against dynamic variations in the system *** simulations using real-world healthcare data demonstrate the superior performance of our proposed framework compared to state-of-the-art baselines,achieving significant improvements in cost,task success rate,and *** proposed approach enables reliable and efficient digital healthcare services in resource-constrained edge computing environments.

关键词： digital healthcare task scheduling resource management edge intelligence

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Guo, Jingjing Li, Jiaxing Liu, Zhiquan Xiong, Yupeng Ma, Yong Vasilakos, Athanasios V. Li, Xinghua Ma, Jianfeng Xidian University School of Cyber Engineering Xi'an710071 China Jinan University College of Cyber Security Guangzhou510632 China Jiangxi Normal University Department of Computer Science and Technology Nanchang330022 China IAU Department of Networks and Communications College of Computer Science and Information Technology Dammam31441 Saudi Arabia Grimstad Norway

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%. © 2002-2012 IEEE.

关键词： Contribution evaluation federated learning model projection theory secure aggregation trust evaluation

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