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Self-Adjusting Network Slicing for Dynamic Heterogeneous Task Offloading in UAV-Enabled Mobile Edge Computing

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Li, Xulong Mao, Wencan Xu, Xinyi Liu, Yaxi Zhang, Huifeng Huangfu, Wei Long, Keping University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan Zhejiang Lab Hangzhou311500 China

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has received wide attention for its ability to significantly reduce latency with flexible mobile aerial nodes. However, the existing works merely consider a fixed periodic network slicing and usually neglect the characterization of computing tasks in practical applications. Motivated by this, we envision a novel scenario where the dynamics and heterogeneity of users and tasks are considered in UAV-enabled MEC networks. We propose a novel self-adjusting two-timescale network slicing scheme based on the internal mechanism in deep reinforcement learning (DRL) to automatically determine whether and when network slicing needs to be reconstructed. Such a novel scheme balances reconstruction cost and system performance compared with traditional fixed schemes. We build an optimization to minimize total system cost for joint UAV trajectory planning and resource allocation at both network and user slice levels. To tackle this problem, we design a novel self-adjusting two-timescale proximal policy optimization (PPO) algorithm that utilizes the property of the critic network, referred to as SaTPPO. Experiment results manifest the effectiveness of the proposed SaTPPO, and it has a lower system cost than the traditional fixed schemes. Moreover, the proposed SaTPPO outperforms the state-of-the-art baselines concerning effectiveness and efficiency. © 2015 IEEE.

关键词： Micro air vehicle (MAV)

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A SystemC Based Framework for Cycle Accurate Processor Simulation and Parameter Analysis

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IFAC-PapersOnLine 2016年第25期49卷 92-97页

作者： Kohl, Johannes Reichenbach, Marc Demel, Carsten Fey, Dietmar Department of Computer Science Chair of Computer Architecture Friedrich-Alexander-University Erlangen-Nuremberg Germany

The development of new domain specific microprocessors architectures is a very complex task. Hardware designers are confronted with several hundred design parameters (e.g. cache size, ALU operation, etc.) which can be combined in a nearly arbitrary way to develop new architectures. Often these parameters are guessed by the designer or best practice is used to find an apparently good parameter set. The utilization of virtual hardware can be one solution to solve this problem. Different parameter sets can be evaluated inside a simulation environment, without the need for designing any real hardware. Unfortunately, most virtual hardware simulators are either to inflexible or not accurate enough to get valuable results. Therefore, this paper presents a SystemC based framework, which enables the construction of highly parameterizable virtual hardware processor models, directed at parameter analysis. The structure and working principle of two exemplary processors are showcasing the use of this framework. A presentation of how different parameter sets impact the programs execution time and power consumption of the processor will be given, as a result of the virtual hardwares execution. © 2016

关键词： Integrated circuit design Application programs Hardware Simulators Cycle accurate Design space exploration Design tool Emulators Microprocessor based control

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Plausibility in Early Stages of Architectural Design: A New Tool for High-Rise Residential Buildings

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Tsinghua science and Technology 2009年第3期14卷 327-332页

作者： Dirk Donath Danny Lobos Chair of Computer Science in Architecture Faculty of Architecture Bauhaus-UniversityWeimar 99423 Germany

This paper analyzes the problem of the design of envelopes for high-rise isolated residential buildings. The phenomenon of envelope creation appears in the early stages of the architectural design. Variables that influence the final shape and size of such envelopes are then identified. This paper presents the state-of-the-art tools for the current solutions at the commercial and academic/scientific level. The variables identified in this research are the client＇s needs, the urban code and architectural practice, and their specific components for the final creation of a new decision support system tool based on the building information modeling （BIM） software platform, to facilitate the work in the project development and drawing production stages. This tool generates several options for building envelopes according to the parameters required by the city Zoning Planning Commission. These options then lead to deliver reliable data and a geometry that can be analyzed in a timely fashion by the engineers, builders, architects, government, and clients in the early stages of the building＇s design. The results show that use of specific information and communication technologies （ICT） tools in the early stages of a building design helps reduce the working time, increases confidence in the generated solution, and contributes to the exploration of alternatives in a short period of time.

关键词： constraint based design parametric programming urban modeling optimization architectural design

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An interpretable spatially weighted machine learning approach for revealing spatial nonstationarity impacts of the built environment on air pollution

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Building and Environment 2025年 280卷

作者： Wang, Shumin Hu, Mingxing Li, Jianyu Liu, Guoao Hu, Wanqi Qi, Junheng Huang, Jingyue Liu, Ziye Wang, Hui Han, Bing School of Architecture Southeast University Si Pailou Campus Nanjing210096 China School of Computer and Information Science University of Pennsylvania PhiladelphiaPA 19104 United States College of Landscape Architecture Nanjing Forestry University 159 Longpan Road Nanjing210037 China College of Horticulture Jinling Institute of Technology Nanjing210038 China

With urbanization acceleration, air pollution has become increasingly severe, and the built environment plays a crucial role in pollutant dispersion and accumulation. In this study, the central urban area of Nanjing is analysed in a case study, focusing on the plot scale to systematically explore spatial heterogeneity and nonlinear interactive mechanisms of built environment impacts. To accurately capture the complex spatial nonstationarity effects of the built environment on air pollution, a geographically weighted machine learning (GeoML) approach driven by an adjacency-based spatial mechanism integrated with the SHapley Additive exPlanations (SHAP) framework for interpretability is established. The findings reveal that the GeoML model outperforms other models (the ordinary least squares (OLS), random forest (RF), geographically weighted regression (GWR), and geographically weighted random forest (GWRF) models), with an R² value of 0.74, effectively capturing spatial heterogeneity and nonlinearity. Further analysis reveals significant variations in the impact of spatial weighting on the contributions of different feature variables: the explanatory power of most variables increases with the inclusion of spatial effects, whereas the contributions of variables such as population density, road network density, and parcel compactness are reduced. The SHAP framework is employed to provide interpretable analysis of the patterns in the impacts of various feature variables on air pollution. This study emphasizes the importance of spatial effects at the microscale plot level in understanding the mechanisms of air pollution and highlights their importance for developing urban environmental policies, thus promoting the transformation of air pollution control towards spatially refined management. © 2025

关键词： Air quality

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VHF Band III Spectrum Availability and Utilization for Cognitive Radio Implementation in Malaysia

VHF Band III Spectrum Availability and Utilization for Cogni...

引用

Learning and Technology Conference (L&T)

作者： Hashim Elshafie Hafizal Mohamad Rashid A. Saeed Mosab Hamdan Mahmoud M. Khattab Abdelmoty M. Ahmed College of Computer Science King Khalid University Saudi Arabia Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Nilai Negeri Sembilan Malaysia College of Computers and Information Technology (CIT) Taif University Taif Saudi Arabia Walton Institute for Information and Communication Systems Science South East Technological University Waterford Ireland College of Engineering and Architecture Al Yamamah University Riyadh Saudi Arabia Faculty of Computer Science Nahda University Beni Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The VHF Band III spectrum (174-230 MHz), recognized for its low utilization, was opened by the FCC in 2002 for opportunistic use by communication technologies. This paper presents a quantitative analysis of the availability of VHF Band III for cognitive radio CR access in Malaysia. Measurements were conducted over a week in 18 Malaysian cities to analyze spectrum utilization and received signal strength, alongside the impact of broadcasts from neighboring countries. The findings highlight actual utilization patterns and reveal the spectrum’s potential for opportunistic access, especially post-digital switchover (DSO).

关键词： Regulators Statistical analysis Urban areas Switches FCC Time measurement Communications technology Cognitive radio

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Energy duality methods for Piecewise Bezier Curve construction

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computer GRAPHICS FORUM 1996年第2期15卷 143-154页

作者： Bercovier, M Volpin, O Institute of Computer Science The Hebrew University of Jerusalem Jerusalem Israel †Bertold Badler Chair of Computer Science.

Piecewise Bezier Curves are constructed using a minimization principle. C-k and GC(k) continuity is imposed by linear constraints. The corresponding quadratic programming with linear constraints problem is introduced and solved by duality methods. Bordering matrices methods are implemented to deal with local refinement (subdivision). The result is a versatile tool for defining/editing contours made of piecewise Bezier curves.

关键词： finite-element method(FEM) stiffness matrix Piecewise Bezier Curve local refinement computer-aided geometric design Lagrange multiplier duality minimization under constraints

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Multimodal Large Models Are Effective Action Anticipators

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IEEE Transactions on Multimedia 2025年 27卷 2949-2960页

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an710055 China Northwestern Polytechnical University School of Astronautics Xi'an710072 China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen518055 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence China University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. © 1999-2012 IEEE.

关键词： Semantics

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From Inconsistency to Unity: Benchmarking Deep Learning-Based Unsupervised Domain Adaptation for RUL

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IEEE Transactions on Automation science and Engineering 2025年

作者： Krokotsch, Tilman Ragab, Mohamed Wu, Min Li, Xiaoli Chen, Zhenghua Guhmann, Clemens Technische Universität Berlin Chair of Electronic Measurement and Diagnostic Technology Straße des 17. Juni 135 10623 Germany A∗STAR Institute for Infocomm Research and Centre for Frontier AI Research 1 Fusionopolis Way 138632 Singapore Technology Innovation Institute Propulsion and Space Research Center United Arab Emirates Nanyang Technological University School of Computer Science and Engineering Singapore

Data-driven Remaining Useful Life (RUL) estimation is critical for various industries, yet scarce labeled data poses a significant challenge. Unsupervised Domain Adaptation (UDA) combined with deep learning has emerged as a promising solution by leveraging unlabeled data. However, recent work on deep UDA exhibits notable inconsistencies, including variations in backbone networks and data handling. Such inconsistencies hinder fair comparisons, obscuring if actual advances were made. To address this, we propose CRULE, a comprehensive benchmarking framework that standardizes deep UDA evaluation. CRULE incorporates a unified 1-D CNN backbone architecture, a standardized training scheme with comprehensive hyperparameter tuning, consistent evaluation protocols (both inductive and transductive), and unified performance metrics (RMSE and Score). This ensures fair and reliable comparisons across different UDA methods. Through experiments on three popular RUL datasets, we found, that only one of the evaluated approaches achieves statistically significant improvements over no adaptation. This suggests that deep UDA approaches proposed for RUL estimation may be less reliable under fair evaluation schemes. To catalyze genuine advancements in the field, we open-source CRULE, empowering the research community to develop and consistently benchmark UDA approaches. CRULE is accessible at https://***/r/crule-55D1. © 2004-2012 IEEE.

关键词： Labeled data

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Communication-Efficient Stochastic Distributed Learning

arXiv

引用

arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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