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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin Poor, H. Vincent The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China 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 The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://***/HongyangDu/DistributedDiffusion. © 2023, CC BY.

关键词： Wireless networks

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Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse

Generative AI-empowered Effective Physical-Virtual Synchroni...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Minrui Xu Dusit Niyato Hongliang Zhang Jiawen Kang Zehui Xiong Shiwen Mao Zhu Han School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Electronics Peking University Beijing China School of Automation Guangdong University of Technology Guangzhou China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore Department of Electrical and Computer Engineering Auburn University Auburn AL USA Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Computer Science and Engineering Kyung Hee University Seoul South Korea

Metaverse seamlessly blends the physical world and virtual spaces through ubiquitous communication and computing equipment and infrastructure. In intelligent transportation systems, the vehicular Metaverse can provide a fully immersive and hyperreal travel experience (e.g., via augmented reality head-up displays, AR-HUDs) to drivers and passengers in autonomous vehicles (AVs) through roadside units (RSUs). However, providing real-time and immersive services requires effective physical-virtual synchronization between AVs and virtual simulators. This paper proposes a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In physical-to-virtual synchronization, digital twin (DT) tasks generated by AVs are offloaded for execution in RSUs with future route generation. In virtual-to-physical synchronization, virtual simulators customize diverse and personalized AR content via generative AI models based on user preferences. Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and virtual simulators for RSUs to provide real-time and effective services. Finally, property analysis and experimental results demonstrate that the proposed mechanism is strategy-proof and adverse-selection free while increasing social surplus.

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Natural pigment sensor for solar ultraviolet radiation measurement 14

Natural pigment sensor for solar ultraviolet radiation measu...

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Organic and Hybrid Sensors and Bioelectronics XIV 2021

作者： Castillo, D. Sánchez, A. González, J. Chamba, C. Lakshminarayanan, Vasudevan Chemistry Department Universidad Tecnica Particular de Loja Ap. 11-01-608 Loja Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo ON Canada Departments of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

ISBN: (数字)9781510644595

ISBN: (纸本)9781510644588

The ultraviolet (UV) index is an international standard measure of the strength of solar ultraviolet radiation on the earth's surface at a specific place and time. Solar radiation with a high UV index can produce damage to the skin and eye (photoaging and photokeratitis). The levels of UV radiation are commonly detected using silicon-based optoelectronic sensors, which can be expensive. Here we propose a way to measure the UV index using natural organic pigments which fluoresce when exposed to UV radiation. In combination with an optical fiber, we have built a prototype sensor based on the pigment of turmeric or "Curcuma Longa". Curcuma longa fluoresces in the range of 500 to 680 nm when exposed to UV radiation. The system uses a filter to isolate the sunlight UV component. The sensor measures the variation in fluorescence intensity using a light dependent resistor to determine radiation levels and correlate it with the UV index. The sensor has been tested in Loja, Ecuador which is located at the equator (UV levels can reach up to 20.0 at the equator). When compared to a standard commercially available sensor (ML8511/LAPIS Semiconductor) this prototype has an error of ± 2.8%. We will describe the optical design and present measurements made with this novel inexpensive sensor. © 2021 SPIE.

关键词： Fluorescence

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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SIC-Free NOMA designs Via Symbol-Level Precoding

SIC-Free NOMA Designs Via Symbol-Level Precoding

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yanqing Xu Fang Fang Shuai Wang Donghong Cai School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering Western University London ON Canada Information Systems Technology and Design Singapore University of Technology and Design Singapore College of Cyber Security Jinan University Guangzhou China

The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, traditional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, conventional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the system, which has been little studied in the literature. Furthermore, the proposed SLP scheme makes the “interference signals” to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. To resolve the optimization problem associated with the SLP scheme, we develop a fractional programming and successive upper-bound maximization based algorithm. Our simulation results demonstrate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.

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Learning-Based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing

Learning-Based Sustainable Multi-User Computation Offloading...

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IEEE International Conference on Communications (ICC)

作者： Minrui Xu Dusit Niyato Jiawen Kang Zehui Xiong Mingzhe Chen 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 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.

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Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

arXiv

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arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The 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 University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Emerging with the support of computing and communications technologies, Metaverse is expected to bring users unprecedented service experiences. However, the increase in the number of Metaverse users places a heavy demand on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attention-aware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-object-attention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1, 000 images. A tutorial on how to use UOAL is presented. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interest-aware and time-aware prediction. By using the predicted user-object-attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services. © 2022, CC BY.

关键词： Eye tracking

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A 243 KOPS/W Crypto-Processor Supporting Homomorphic Encryption and Post-Quantum Cryptography for IoT Devices

A 243 KOPS/W Crypto-Processor Supporting Homomorphic Encrypt...

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European Conference on Solid-State Circuits (ESSCIRC)

作者： ByungJun Kim Han-Gyeol Mun Kyeong-Jun Lee Jeong-Min Woo Kiseo Kang Hyunwoo Son Jae-Yoon Sim Postech Integrated Circuits and Systems Laboratory POSTECH Pohang Korea School of Electrical and Computer Engineering Cornell Tech New York NY USA Mixed-Signal IC Design Laboratory Gyeongsang National University Jinju Korea

ISBN: (数字)9798350388138

ISBN: (纸本)9798350388145

This work presents a power-efficient latticebased crypto-processor for IoT edge devices supporting both homomorphic encryption (HE) and post-quantum cryptography (PQC) algorithms. The chip incorporates a reconfigurable architecture on a unified number system to effectively reduce the size of modulo index. It also employs a power-efficient modular arithmetic block and a scheduling for pipelined processing. The implemented chip in 28 nm CMOS achieves the state-of-the-art power efficiencies both in HE and PQC.

关键词： Out of order Protocols Processor scheduling Reconfigurable architectures Cryptography Internet of Things Indexes Homomorphic encryption Arithmetic Pipeline processing

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

arXiv

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arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Xiong, Zehui Du, Hongyang Yang, Zhaohui Niyato, Dusit Shen, Meng Jiao, Yutao Zhang, Yang The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China 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 Electronic Engineering University of Hong Kong Hong Kong The College of Information Science and Electronic Engineering Zhejiang University China The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Cyberspace Science and Technology Beijing Institute of Technology China The College of Communications Engineering Army Engineering University of PLA China

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, Web 3.0 based on blockchains is still in its infancy, such as ensuring block freshness and optimizing block propagation for improving blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block Information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

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