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SSRN 2024年

作者： Lim, Sunho Lee, Ingyu Choi, Gyu Sang Chae, Jinseok Ashish, Ellora Ward, Eric Pu, Cong T2WISTOR: TTU Wireless Mobile Networking Laboratory Dept. of Computer Science Texas Tech University LubbockTX79409 United States Dept. of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Dept. of Computer Science and Engineering Incheon National University Incheon22012 Korea Republic of Dept. of Computer Science Oklahoma State University StillwaterOK74078 United States

Commercially well-known drones are increasingly popular and have been deployed in post-disaster relief to support traditional search and rescue operations. However, there are still barriers to conducting a drone-centric search operation, including but not limited to operational difficulty for non-professional drone pilots, inefficient pre-planned path, and more importantly initial setup delay. In this paper, we propose a small-scale prototype of a mobile framework, called FastPlan, to facilitate the development for expediting drone-centric search decisions and operations. The basic idea is to automate the required operations with no or minimized user intervention. The framework consists of three major operations: points-of-interest (POIs) extraction, clustering, and path planning. We extract a set of target POIs from a public map (i.e., Google Maps) to decide what and where to search. Then we deploy simple density-based clustering and search priority-based path planning strategies to efficiently group and cover the POIs. We implement the framework as an Android-based mobile software (App) for a proof-of-the-concept and conduct extensive simulation experiments for performance evaluation. We analyze the different performance behavior and their implication and applicability. The results indicate that the proposed framework can be extended flexibly depending on the search priority and be a viable approach in post-disaster relief. © 2024, The Authors. All rights reserved.

关键词： Motion planning

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A Privacy-Preserving Incentive Mechanism in Satellite-Terrestrial Crowdsensing 7

A Privacy-Preserving Incentive Mechanism in Satellite-Terres...

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7th International Conference on computer and communications, ICCC 2021

作者： Zhu, Boxiang Li, Jiarui Liu, Zhongkai Liu, Yang Beijing University of Posts and Telecommunications School of Information and Communication Engineering China Beijing University of Posts and Telecommunications Department of Computer Science China Beijing University of Posts and Telecommunications International School China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China

ISBN: (纸本)9781665409506

In crowdsensing, users are concerned about their location privacy leakage, while users in remote areas are unable to connect to base stations, which prevents them from completing crowdsensing tasks. Current privacy-preserving algorithms and assignment mechanisms cannot provide personalized privacy protection for users. Therefore, we propose a differential private long-term privacy preserving auction with Lyapunov (DP-LAL) algorithm based on a satellite-terrestrial network (STN) that not only gives an approximate optimal total payment in polynomial time, but also compensates for the poor signal in remote areas. We employ reverse combinatorial auction and Lyapunov optimization theorem to jointly optimize queue stability and total payment. Compared with the widely used algorithms, the simulation results show that our algorithm is more efficient and has better economic benefits. © 2021 IEEE.

关键词： Polynomial approximation

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Satisfaction-Aware Incentive Scheme for Federated Learning in Industrial Metaverse: DRL-Based Stackbelberg Game Approach

arXiv

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arXiv 2025年

作者： Li, Xiaohuan Qin, Shaowen Tang, Xin Kang, Jiawen Ye, Jin Zhao, Zhonghua Niyato, Dusit Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Nanning530001 China School of Automation Guangdong University of Technology Guangzhou510006 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530000 China School of Computer and Electronic Information Guangxi University Nanning530000 China College of Computing and Data Science Nanyang Technological University Singapore

Industrial Metaverse leverages the Industrial Internet of Things (IIoT) to integrate data from diverse devices, employing federated learning and meta-computing to train models in a distributed manner while ensuring data privacy. Achieving an immersive experience for industrial Metaverse necessitates maintaining a balance between model quality and training latency. Consequently, a primary challenge in federated learning tasks is optimizing overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency. Additionally, the satisfaction function is incorporated into the utility functions to incentivize node participation in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for industrial Metaverse. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves at least 23.7% utility compared to existing schemes without compromising model accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Secure Beamforming Design Based on Multistatic Cooperative Sensing via IRS

Secure Beamforming Design Based on Multistatic Cooperative S...

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International Conference on Wireless communications and Signal Processing (WCSP)

作者： Xianglin Yu Jinlei Xu Xiaoqi Qint Jie Tang Xu Jiang Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China School of Electronic and Information Engineering South China University of Technology Guangzhou China School of Information Science and Engineering Harbin Institute of Technology (Weihai) Weihai China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). To be specific, a multistatic cooperative sensing scheme is proposed to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. Our goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and nonconvex objective function, we decompose it into three subproblems and develop an alternative algorithm to solve them iteratively. The association variables are first obtained by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, an alternating direction method of multiplier is adopted for the IRS phase-shift design. Simulation results indicate that the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the security.

关键词： Wireless communication Location awareness Array signal processing Simulation Signal processing algorithms Linear programming Sensors Security Surface treatment Signal to noise ratio

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A Practical Quantum Network Coding Protocol Based on Non-Maximally Entangled State

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computers, Materials & Continua 2021年第8期68卷 2651-2663页

作者： Zhen-Zhen Li Zi-Chen Li Xiu-Bo Chen Zhiguo Qu Xiaojun Wang Haizhu Pan School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China School of Electronic Engineering Dublin City UniversityDublin9Ireland College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China

In many earlier works,perfect quantum state transmission over the butterfly network can be achieved via quantum network coding protocols with the assist of maximally entangled ***,in actual quantum networks,a maximally entangled state as auxiliary resource is hard to be obtained or easily turned into a non-maximally entangled state subject to all kinds of environmental ***,we propose a more practical quantum network coding scheme with the assist of non-maximally entangled *** this paper,a practical quantum network coding protocol over grail network is proposed,in which the non-maximally entangled resource is assisted and even the desired quantum state can be perfectly *** achievable rate region,security and practicability of the proposed protocol are discussed and *** practical quantum network coding protocol proposed over the grail network can be regarded as a useful attempt to help move the theory of quantum network coding towards practicability.

关键词： Quantum network coding non-maximally entangled state quantum grail network practical protocol

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A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

arXiv

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arXiv 2023年

作者： Hu, Jiesi Yang, Yanwu Guo, Xutao Wang, Jinghua Ma, Ting School of Electronics and Information Engineering Harbin Institute of Technology at Shenzhen The Peng Cheng Laboratory China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology at Shenzhen The Peng Cheng Laboratory Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Shenzhen China International Research Institute for Artifcial Intelligence Harbin Institute of Technology Shenzhen China

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to privacy concerns. Recent State-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, PLs suffer from accuracy deterioration caused by domain shift, and thus limit the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating probability lower bound of the PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to improve PLs iteratively. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of pseudo-labels and a framework for obtaining high-quality PLs, resulting in improved adaptation performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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UAV-Aided Covert Federated Learning Networks

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IEEE Network 2025年

作者： Wang, Chao Guo, Shaoyong Xiong, Zehui Xing, Chengwen Zhao, Nan Niyato, Dusit Karagiannidis, George Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Beijing Institute of Technology School of Information and Electronics Beijing100081 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

With the increasing emphasis on data privacy, federated learning (FL) networks show great potential through distributed training without directly sharing the raw data. However, the coverage of FL terrestrial servers is usually limited. Therefore, we propose leveraging the unmanned aerial vehicle (UAV) as a mobile flying server to further improve the wireless coverage and training efficiency of FL. Nevertheless, frequent exchanges of model parameters in UAV-assisted FL can result in serious security risks. In order to achieve this, we propose a covert FL scheme assisted by the UAV, which can protect the transmission key features of local models from being detected by wardens. Furthermore, we discuss the FL network and covert communications, as well as the exceptional features of UAV-assisted covert FL. Finally, we present two case studies, one of which focuses on minimising the network latency, while the other concentrates on reducing the energy consumption of covert FL. The simulation results demonstrate the efficacy of the proposed schemes in addressing future challenges. © 1986-2012 IEEE.

关键词： Data privacy

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Time-delay Robust Safety Control of Connected Vehicles under Stochastic DoS Attack

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IEEE Internet of Things Journal 2025年

作者： Song, Xiulan Liu, Xueyang Shan, Hangguan Lu, Rongxing Zhejiang University of Technology College of Information Engineering Hangzhou310023 China University of Birmingham School of Engineering BirminghamB15 2TT United Kingdom Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonE3B 5A3 Canada

Connected vehicles utilize vehicle-to-vehicle (V2V) wireless communication networks to gather vital information for platoon tracking control, not only enhancing road traffic efficiency but also preventing collisions. This paper introduces a robust safety platooning control method for connected vehicles, which addresses safety challenges in heterogeneous vehicle platoon systems, including time delays, dynamic uncertainties, and stochastic denial-of-service (DoS) attacks. Specifically, we present an uncertain longitudinal dynamic model for heterogeneous vehicles to account for variable driving conditions. Then, a robust safety platooning controller, based on linear matrix inequality (LMI) and Markov jump system theory, is designed. This controller considers communication delays to ensure vehicle-following stability under dynamic uncertainties and stochastic DoS attacks. Finally, the effectiveness of the proposed algorithm is validated through co-simulations using MatLab and CarSim, as well as actual vehicle experiments, demonstrating its capability to mitigate the impact of communication delays, dynamic uncertainties, and stochastic DoS attacks. © 2014 IEEE.

关键词： Markov processes

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Exploring Channel Estimation and Signal Detection for ODDM-based ISAC Systems

arXiv

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arXiv 2024年

作者： Wang, Dezhi Huang, Chongwen Liu, Lei Chen, Xiaoming Wang, Wei Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Electrical Engineering and Computer Science the KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication (ISAC) systems based on the orthogonal delay-Doppler multiplexing (ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler (DD) plane. To enhance the communication performance in the ODDM-based ISAC systems, we first propose a low-complexity approximation algorithm for channel estimation, which addresses the challenge of the high complexity from high resolution in the ODDM modulation, and achieves performance close to that of the maximum likelihood estimator scheme. Then, we employ the orthogonal approximate message-passing scheme to detect the symbols in the communication process based on the estimated channel information. Finally, simulation results show that the detection performance of ODDM is better than other multi-carrier modulation schemes. Specifically, the ODDM outperforms the orthogonal time frequency space scheme by 2.3 dB when the bit error ratio is 10−6 Copyright © 2024, The Authors. All rights reserved.

关键词： Signal detection

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Ultra-Low Concentration Biosensing Harnessing Aulter-Townes Doublets in Meta Waveguide Ring Resonators

SSRN

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SSRN 2025年

作者： Li, Wanxin Li, Jiewen Li, Rui Chen, Gina Jinna Shum, Perry Ping Zou, Yi Chen, Ray T. Dong, Yongkang Xu, Xiaochuan National Key Laboratory of Laser Spatial Information Guangdong Provincial Key Laboratory of Integrated Photonic-Electronic Chip Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Harbin Institute of Technology Xili University Town Harbin Institute of Technology campus Guangdong Shenzhen518055 China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Department of EEE Southern University of Science and Technology Shenzhen518055 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Department of Electrical and Computer Engineering The University of Texas at Austin 10100 Burnet Rd. AustinTX78758 United States

Pushing sensitivity to ultra-low concentrations unlocks new possibilities in sample analysis. However, the limit of detection (LOD) in highly sensitive optical sensors is often constrained by noise. In this study, we address this limitation by leveraging Autler-Townes Splitting (ATS) within meta waveguide microring resonators (MWMRs) for self-referenced biological detection. This approach effectively suppresses noise, achieving a minimal baseline drift of 4.6 fm/min without the need for sophisticated environmental controls. By combining high sensitivity with robust noise suppression, ATS-based MWMRs enable the detection of streptavidin at an ultra low concentration of 0.01 pg/mL, with the blank solution serving as the baseline. These results highlight the significant potential of ATS-based MWMRs as a robust and versatile platform for biomolecule detection at ultra-low concentrations, paving the way for advancements in biological research and medical diagnostics. © 2025, The Authors. All rights reserved.

关键词： Optical resonators

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