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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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Perceptions of Beauty in Security Ceremonies

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Philosophy and Technology 2022年第3期35卷 1-34页

作者： Bella, Giampaolo Ophoff, Jacques Renaud, Karen Sempreboni, Diego Viganò, Luca Dipartimento di Informatica Università di Catania Catania Italy Division of Cybersecurity Abertay University Dundee United Kingdom University of Cape Town Cape Town South Africa Department of Computer and Information Sciences University of Strathclyde Glasgow United Kingdom Department of Information Systems Rhodes University Grahamstown South Africa School of Computing Science University of South Africa Pretoria South Africa Department of Informatics King’s College London London United Kingdom

When we use secure computer systems, we engage with carefully orchestrated and ordered interactions called “security ceremonies”, all of which exist to assure security. A great deal of attention has been paid to improving the usability of these ceremonies over the last two decades, to make them easier for end-users to engage with. Yet, usability improvements do not seem to have endeared end users to ceremonies. As a consequence, human actors might subvert the ceremony’s processes or avoid engaging with it. Here, we consider whether beautification could be one way of making ceremonies more appealing. To explore beautification in this context, we carried out three studies. Study 1 surveyed 250 participants to derive a wide range of potential dimensions of “beautiful ceremonies”. These statements were sorted into dominant themes and converted into statements, which fed into the second study, with 309 respondents, to reveal the dominant dimensions constituting beauty. Study 3 asked 41 participants to carry out a Q-sort, which revealed the ways that people combine the identified dimensions when characterising security ceremonies as “beautiful”. These studies have allowed us to pin down the perceived dimensions of beauty in the context of security ceremonies, and also to understand how people combine these dimensions in different ways in judging security ceremonies to be beautiful, confirming the old adage of beauty being “in the eye of the beholder”. We conclude by highlighting the constraints imposed by the overarching requirement for security to be maintained in the face of any usability improvements and beautification endeavours. © 2022, The Author(s).

关键词： Beauty Human-centred security Perception Security ceremonies

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Assessment of Inertial Measurement Units for Estimating Kinematic Data in Gait Analysis

Assessment of Inertial Measurement Units for Estimating Kine...

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International Conference on Automation and computing (ICAC)

作者： Mohamed Hassan Basel Barakat Mona Mohamed Ibrahim Ahmed M. Kamel Mays Abukeshek Computing and Engineering Department University of Huddersfield UK School of Computer Science Faculty of Technology University of Sunderland UK Department of Physical Therapy for Musculoskeletal Disorder and Its Surgery Faculty of Physical Therapy Cairo University Egypt Guidance Department Military Technical College Egypt School of Computer Science Faculty of Technology University of Sunderland University of Huddersfield UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Accurate measurement of human joint angles during gait is crucial for understanding movement biomechanics and assessing musculoskeletal conditions. Inertial measurement units (IMUs) have become popular for capturing joint kinematics due to their portability, affordability, and ease of use. In this paper, a system with four IMU sensors was designed and tested to compare joint angles obtained from IMUs with those from a 3D motion capture system. The experiments showed that an excellent correlation (r = 0.9) in hip and knee joint angles, outperforming the state-of-the-art systems.

关键词： Knee Wireless communication Measurement units Correlation Three-dimensional displays Inertial navigation Optical variables measurement Gain measurement Sensor systems Hip

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Enhanced Expectation-Maximization Algorithm for Smart Traffic IoT Systems using Deep Generative Adversarial Networks to Reduce waiting time

Enhanced Expectation-Maximization Algorithm for Smart Traffi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： B Yamini M Jayaprakash S Logesswari V Ulagamuthalvi. R Porselvi G S Uthayakumar Department of Networking and Communication SRM Institute of Science and Technology Kattangulathur Chennai Tamil Nadu India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMD Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering School of Computing Satyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Tagore Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering St.Joseph’s Institute of Technology OMR Chennai Tamilnadu India

The main focus of the work is to improve the smart traffic systems for the Internet of Things (IoT) such as traditional signals and safe driving via tracking the congestion and monitoring traffic slowly. Depending on the circumstances, intersections between roads can sometimes lead to accidents, so this problem is considered part of the research. These limitations can be overcome by using modern sensors that can reduce the traffic signal waiting time and rash driving on the National Highway (NH) road. Urban countries make use of AI-based decisions performing smart control over managing vehicles. In the proposed model the novel combination of collecting road maps coordinating points that can connect the access movements. Also IoT devices such as mobile phones, unusual network failure, and peak time traffic control by preprocessing the collected traffic dataset. Based on the dataset the detection of coverage area can be extracted using Enhanced Expectation Maximization Algorithm for ranking the variables. Initially, the classification algorithms were used and grouped as accident months, non-accident months, hill stations, and highway NH Road delay time are some of categories. Using Deep Generative Adversarial Network (DGAN) data can be balanced and used to reduce the waiting time along with predicting the smart cities’ transportation for reliable customers.

关键词：

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Advanced AI Techniques for Grid-Connected Renewable Energy Systems

Advanced AI Techniques for Grid-Connected Renewable Energy S...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Laith H. Fezaa Ahmed Alkhayyat J.S. Chohan Raj Kumar Jatinder Kumar Nilesh Bhosle Department of Optical Techniques Al-Zahrawi University College Karbala Iraq College of Technical Engineering The Islamic University Najaf Iraq School of Mechanical Engineering Rayat Bahra University Mohali India Dept. of CSE Chandigarh University Mohali Punjab India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India NIMS School of Computing Science and Artificial Intelligence NIMS University Jaipur Rajasthan India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

This research paper explores the ability to integrate innovative AI methods to optimise grid-integrated renewable energy systems. Consequently, this study assesses the benefits of energy demand forecasting, fault detection, and real-time energy management using Long Short Term Memory LSTM networks, SVM and RL models focusing on three broad areas. The LSTM model performed well in the energy demand forecast with low MAE and RMSE than other models while the SVM model performed well in fault detection with a 92% accuracy. LBC revealed that the RL-based system contributed to the reduction of energy cost while regulating the grid stability through optimizing energy in different demand situations. This paper also presents several key issues associated with data quality, model generalization, computational overhead, and interfaces to extant systems. This emphasizes the gaps on the effectiveness of data acquisition techniques, flexibility of AI mod- els and robustness of the algorithms to perform within resource constraints. There are some issues arise while integrating new AI technologies with existing traditional systems which must be considered and standardized. Possible future works are provided to overcome these issues, which include the refinement of pre processing techniques, flexi- bility of models, and solution extensibility. The studies highlight multiple possibilities of using AI in the enhancement of energy systems and their managing factor, decrease of operation costs, as well as increase of the robustness of grids, opening the way to further development of the more effective and stable energy managing solutions.

关键词： Support vector machines Renewable energy sources Energy consumption Accuracy Costs Computational modeling Predictive models Data models Artificial intelligence Long short term memory

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Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework

arXiv

引用

arXiv 2025年

作者： Lin, Yushen Zhang, Ruichen Huang, Wenqi Wang, Kaidi Ding, Zhiguo So, Daniel K.C. Niyato, Dusit School of Electrical and Electronic Engineering The University of Manchester M13 9PL United Kingdom Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore

In this work, we develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) specifically for wireless communication applications. The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard. By utilizing advanced language models for entity extraction and question generation, rigorous data curation processes are employed to maintain high quality and relevance. Additionally, we introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data with 2.24% and 1.31% performance boost for different models compared to baselines, respectively. To demonstrate the effectiveness of the fine-tuned models with the proposed methodologies on practical tasks, we also consider different tasks, including summarizing optimization problems from technical papers and solving the mathematical problems related to non-orthogonal multiple access (NOMA), which are generated by using the proposed multi-agent framework. Simulation results show significant performance gain in summarization tasks with 20.9% in the ROUGE-L metrics. We also study the scaling laws of fine-tuning LLMs and the challenges LLMs face in the field of wireless communications, offering insights into their adaptation to wireless communication tasks. This dataset and fine-tuning methodology aim to enhance the training and evaluation of LLMs, contributing to advancements in LLMs for wireless communication research and applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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DISYRE: DIFFUSION-INSPIRED SYNTHETIC RESTORATION FOR UNSUPERVISED ANOMALY DETECTION

arXiv

引用

arXiv 2023年

作者： Marimont, Sergio Naval Baugh, Matthew Siomos, Vasilis Tzelepis, Christos Kainz, Bernhard Tarroni, Giacomo CitAI Research Centre Department of Computer Science City University of London United Kingdom BioMedIA Department of Computing Imperial College London United Kingdom Department of Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Germany

Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free of anomalies. Diffusion models learn to modify inputs x to increase the probability of it belonging to a desired distribution, i.e., they model the score function ∇x log p(x). Such a score function is potentially relevant for UAD, since ∇x log p(x) is itself a pixel-wise anomaly score. However, diffusion models are trained to invert a corruption process based on Gaussian noise and the learned score function is unlikely to generalize to medical anomalies. This work addresses the problem of how to learn a score function relevant for UAD and proposes DISYRE: Diffusion-Inspired SYnthetic REstoration. We retain the diffusion-like pipeline but replace the Gaussian noise corruption with a gradual, synthetic anomaly corruption so the learned score function generalizes to medical, naturally occurring anomalies. We evaluate DISYRE on three common Brain MRI UAD benchmarks and substantially outperform other methods in two out of the three tasks. © 2023, CC BY.

关键词： Restoration

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Designing Playful and Ethical Child-AI Systems 25

Designing Playful and Ethical Child-AI Systems

引用

Proceedings of the 24th Interaction Design and Children

作者： Leigh M Levinson Elmira Yadollahi Bengisu Cagiltay Shyamli Suneesh Vicky Charisi Angela Colvert Kruakae Pothong Selma Sabanovic Informatics and Cognitive Science Indiana University Bloomington Indiana USA School of Computing and Communications Lancaster University Lancaster United Kingdom Department of Computer Sciences University of Wisconsin - Madison Madison Wisconsin USA School of Computing and Communications Lancaster University Lancaster England United Kingdom Berkman Klein Centre Harvard University Cambridge Massachusetts USA School of Education University of Sheffield Sheffield United Kingdom Media and Communications London School of Economics and Political Science London United Kingdom Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana USA

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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