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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Guoquan Wu Jianhang Tang Yang Zhang Bo Wang Wenchao Jiang Zehui Xiong Yixiong Feng State Key Laboratory of Public Big Data Guizhou University China Department of Computer Science and Engineering Southern University of Science and Technology China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Metaverse is envisaged as an evolving Internet paradigm that allows people to play, work, and socialize in a shared and virtual ecosystem with immersive and seamless experiences. However, multiple users simultaneously request massive resources for avatar physics emulation and graphical rendering. How to provide high-quality and low-latency meta-verse services for massive concurrent users is a crucial problem. In this work, an intelligent Grover search-based edge caching (GEC) algorithm is proposed for metaverse applications, where quantum theory is used to generate caching solutions with low time complexity. Metaverse scenes can be divided into massive environment panoramic frames and dynamic objects. An edge server is deployed to render and cache the common environment panoramic frames, while the dynamic objects are rendered on head-mounted displays (HMDs). Based on the quantum theory, the panoramic frames are split into several tiles and represented by qubits. The optimal caching results are obtained by performing unitary transformations for the tiles’ features. Finally, we provide extensive simulation experiments by a real- world metaverse dataset. The numerical results reveal that the GEC algorithm can reduce 11.09% of the service time and increase 13.34% of the cache hit rate on average by comparing it with two benchmark caching algorithms.

关键词： Metaverse Heuristic algorithms Simulation Qubit Quantum mechanics Rendering (computer graphics) Internet of Things

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Channel Estimation Refinement for Tethered Aerial Platform Enabled Multi-User Communication systems

Channel Estimation Refinement for Tethered Aerial Platform E...

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IEEE International Conference on Communications in China (ICCC)

作者： Puguang An Peng Yang Xianbin Cao Chaoqun You Q. S. Tony Quek Dapeng Oliver Wu School of Electronic Information Engineering Beihang University Beijing China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen Guangdong China School of Computer Science Fudan University Shanghai China Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer Science City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper investigates the three-dimensional (3D) downlink sparse channel estimation for tethered aerial platformenabled multi-user communication systems operating in a frequency division duplexing mode with large-scale antenna arrays. To this end, we design a non-identical Bernoulli-Gaussian distribution-based channel model that reflects the potential common sparsity caused by distant scatterers in a low-altitude environment. A low-complexity channel estimation algorithm without the need for prior knowledge of channel sparsity is proposed. It first applies a greedy pursuit algorithm to roughly estimate the common support set (CSS). Given the initial CSS, multi-user channels are then iteratively estimated and refined using a novel neighborhood-based channel estimation refinement scheme, which includes a decentralized sparse channel estimator to recover sparse channels accurately under non-i.i.d channel sparsity priors with low computation burden. Simulation results indicate that the proposed algorithm outperforms its existing counterparts and approaches the performance limit with perfect knowledge of the CSS.

关键词： Three-dimensional displays Communication systems Simulation Channel estimation Clustering algorithms Downlink Frequency conversion

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Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Letaief, Khaled B. School of Computer Science and Engineering Nanyang Technological University Singapore the School of Automation Guangdong University of Technology China the Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore the Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Hong Kong

The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in resource consumption when deployed on user devices. This paper proposes mobile edge networks supported MoE-based GAI. We first review the MoE from traditional AI and GAI perspectives, including structure, principles, and applications. We then propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. We discuss challenges in this process and introduce a deep reinforcement learning based algorithm to select edge devices for subtask execution. Experimental results will show that our framework not only facilitates GAI’s deployment on resource-limited devices but also generates higher-quality content compared to methods without edge network support. © 2024, CC BY.

关键词： Deep learning

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GUEST EDITORIAL

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China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

arXiv

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arXiv 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Information Systems Technology and Design Singapore University of Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering University of Waterloo Canada

—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%. © 2024, CC BY.

关键词： Diffusion

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Development of Software for Modeling the Drying Process of Hygroscopic Materials Using Cellular Automata

Development of Software for Modeling the Drying Process of H...

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International computer sciences and information Technologies (CSIT)

作者： Yaroslav Sokolovskyy Oleksiy Sinkevych Roman Voliansky Oleksandr Storozhuk Department of Computer-Aided Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Computer Science Ukrainian National Forestry University Lviv Ukraine Department of Electromechanical Systems Automation and Electric Drives National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Department of Information Systems and Computer Modeling Ukrainian National Forestry University Lviv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

In this work, software is developed for modeling the drying process of hygroscopic materials using cellular automata. The modeling process using cellular automata is described, and the developed transition rules that consider heat and mass transfer not only within the material but also between stacks are provided. The developed software uses a modern multi-tier client-server architecture, including a database for handling large volumes of computational data. Additionally, the results of the modeling, including their validation based on existing experimental data, are presented and analyzed.

关键词： computer science Accuracy Databases Computational modeling Automata computer architecture Software Data models information technology Heat transfer

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Distributed Sparse Signal Detection With Energy-Efficient Censoring-Quantization in Wireless Sensor Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Chen, Xiangsen Xu, Wenbo Wang, Yue Yau, David K. Y. Li, Yingshu Cai, Zhipeng Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Beijing100876 China Georgia State University Department of Computer Science AtlantaGA30303 United States Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore

In wireless sensor networks (WSNs), the energy consumption of sensors and the system performance are inevitably contradictory. For the distributed detection problem in WSN, the censoring technique has been proposed as an energy-efficient transmission strategy. However, most current detection schemes with censoring do not consider the quantization operations in actual communication systems to further save the transmission energy in actual communication systems. In this paper, we propose two energy-efficient detection schemes including a common censoring-quantization strategy and two different detectors. This censoring-quantization strategy aims to reduce the energy consumption of transmitting observations. It is modeled as a three-state equivalent transmission strategy at sensors, with the states being keeping silent, transmitting "1», and transmitting "-1 ». To fully explore the advantage of this strategy, we design two detectors, namely the Censoring-Quantization Maximum-Likelihood (CQ-ML) detector and the Censoring-Quantization Locally Most Powerful Test (CQ-LMPT) detector, respectively for two scenarios, which correspond to whether the fusion center (FC) knows the parameters of the Phenomenon of Interest (POI). We further analyze the theoretical performance of our schemes to provide the optimal censoring-quantization thresholds. Finally, experimental results demonstrate the effectiveness of our schemes and highlight the significant energy consumption reduction with negligible performance loss. © 2017 IEEE.

关键词： Censoring distributed detection energy-efficient quantization wireless sensor network

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Cross-Modal Generative Semantic Communications for Mobile AIGC: Joint Semantic Encoding and Prompt Engineering

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Zhang, Ping Shen, Xuemin Sherman School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Auburn University Auburn United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering University of Waterloo Canada

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services can become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modal Generative Semantic Communications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize a series of cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perpetual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward. © 2024, CC BY.

关键词： Semantics

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