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Efficient security analysis for quantum key distribution against side-channel leakage

Optics Express 2025年第10期33卷 20588-20597页

作者： Luo-Jia Ma Yue-ying Wang Ming-Shuo Sun Chun-Hui Zhang Hua-Jian Ding Xing-Yu Zhou Qin Wang Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education NUPT Nanjing 210003 China chz@*** qinw@***

Quantum key distribution (QKD) allows the transmission of keys with unconditional security. Nevertheless, a discrepancy remains between the theoretical framework and the practical implementation, as device imperfections are frequently neglected in most QKD protocols. Some previous works proposed to use Hong-Ou-Mandel interference to quantify information leakage from side-channels. However, the security bound was relatively loose, resulting in poor performance. In this paper, we present an improved method for the security analysis of practical QKD protocols and provide a more precise parameter estimation. Furthermore, we apply it to several important QKD protocols, including BB84, measurement-device-independent, and twin-field, and carry out corresponding numerical simulations. Our work demonstrates significant improvements in both the key rate and the transmission distance compared to previous methods, representing a crucial step towards the practical implementation of QKD.

关键词： Bit error rate Diode lasers Interference Numerical simulation Quantum key distribution Quantum key distribution networks

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Multi-Modal Sensing and Lighting Estimation for PoE-Enabled Intelligent and Energy-Efficient Lighting networks

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IEEE Internet of Things Journal 2025年

作者： Tang, Liwei Xu, Yuanyuan Liu, Xiaodong Wang, Yuhao Wang, Jinyuan Shao, Huilong Nanchang University School of Information Engineering Nanchang330031 China Nanchang Laboratory Nanchang330224 China Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China

The application of power over Ethernet (PoE)-enabled lighting has emerged as a groundbreaking technology with the potential to achieve cooperative control and create an energy-saving lighting network. However, fine-grained user-side lighting environment perception is crucial to improve energy efficiency and user experience. To tackle this issue, a PoE-enabled intelligent lighting network with multi-modal sensing is proposed. Specifically, a multi-modal sensor is designed and installed on the side of the luminaire to provide both fine-grained lighting environment monitor and dimming. Then, a multilayer perceptron (MLP)-based estimation scheme is proposed to estimate the illumination and color temperature information around the user activity area. Moreover, an experimental lighting network is constructed and deployed in a classroom to measure practical lighting dataset. The proposed scheme effectively estimates the lighting quality on the user side based on the measurement data. Furthermore, based on the estimated information and the collaborative control capabilities of PoE, the operating current of the luminaire is updated in real time, and thus the ambient light resource is fully utilized. Finally, the proposed lighting network achieves a 39.99% energy saving. © 2014 IEEE.

关键词： Lighting fixtures

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communication-Efficient Distributed Learning in Massive IoT: A Graph-Based Perspective

Communication-Efficient Distributed Learning in Massive IoT:...

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IEEE Conference on wireless communications and networking

作者： Lindong Zhao Jingyue Tang Mingzhe Chen Liang Zhou Weihua Zhuang Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.

关键词： Performance evaluation Knowledge engineering Computer aided instruction Costs Distance learning Scalability Graph neural networks Data models Internet of Things Interchannel interference

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Progressive Masking Oriented Self-Taught Learning for Occluded Facial Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Kang, Bin Wang, Shuangshuang Wang, Zongyu Li, Xin Dou, Haie Wang, Lei Xia, Zhijie College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China School of Mechanical Engineering Southeast University Nanjing China

Self-taught learning (STL) is a promising solution that reduces the performance gap between weakly supervised and fully supervised learning for easily accessible, label-free images. The success of traditional STL solutions relies on the assumption that the target appearance is completely visible and well-defined. In real-world facial expression recognition scenarios, however, saliency regions are often partially occluded, which significantly hampers the generalization capability of STL methods. Nevertheless, few studies have investigated the impact of occlusion on STL. In this paper, we propose an interweaved autoencoder network for weakly supervised facial expression recognition in occlusion scenarios. The key innovation of our network lies in the Residual Connection Union (RCU) blocks that can integrate the Convolutional Neural network (CNN) and Transformer layers into a multi-scale structure. The RCU enables a progressive masking strategy to accurately identify and focus on contributive yet often overlooked image patches by analyzing the relationships among region-level target representations. In addition, we introduce a self-knowledge distillation module for the effective training of the proposed autoencoder network. Extensive experiments are conducted on four public datasets to demonstrate the superiority of our method over related works. © 2010-2012 IEEE All rights reserved.

关键词： Self-supervised learning

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Cross-Modal Haptic Compression Inspired by Embodied AI for Haptic communications

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IEEE Transactions on Multimedia 2025年

作者： Lu, Hang Tan, Xinmeng Chen, Mingkai Zhang, Zhe Zhang, Xuguang Chen, Jianxin Wei, Xin Zhao, Tiesong Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Science & Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China

Haptic data compression has gradually become a key issue for emerging real-time haptic communications in Tactile Internet (TI). However, it is challenging to achieve a tradeoff between high perceptual quality and compression ratio in haptic data compression scheme. Inspired by the perspective of embodied AI, we propose a cross-modal haptic compression scheme for haptic communications to improve the perception quality on TI devices in this paper. Since multimodal fusion is routinely employed to improve the ability of system in cognition, we assume that haptic codec is guided by visual semantics to optimize parameter settings in the coding process. We first design a multi-dimensional tactile feature fusion network (MTFFN) relying on multi-head attention mechanism. The MTFFN extracts the multi-dimensional features from the material surface and maps them to infer the coding parameters. Secondly, we provide second-order difference and linear interpolation to establish an criterion for the determination of optimal codec parameters, which are customized by the material categories so as to give high robustness. Finally, the simulation results reveal that our compression scheme can efficiently make a personalized codec procedure for different materials, obtaining more than 17% improvement in terms of compression ratio with high perceptual quality at the same time. © 2025 IEEE.

关键词： Data compression ratio

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Cross-Modal Haptic Generation for Emergency Rescue in Internet of Robotic Things

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IEEE Internet of Things Journal 2025年

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE68588 United States

Robots equipped with multi-modal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual-haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multi-modal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multi-modal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue. © 2025 IEEE.

关键词： Robots

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Improved Dynamic Model Identification of Cobots Based on a Nonlinear Friction Model

Improved Dynamic Model Identification of Cobots Based on a N...

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IEEE International Conference on Industrial technology (ICIT)

作者： Hang Ruan Bitao Yao Wenjun Xu School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In the field of advanced manufacturing with human robot collaboration (HRC), accurate dynamic model of cobots plays a crucial role in model-based control, simulation, and safe physical human-robot interaction (pHRI). However, excessive noise and inaccurate friction modeling often lead to unsatisfactory identification results. To address these challenges, this paper improves dynamic model identification by adopting a nonlinear friction model that captures both the Stribeck effect and the nonlinear characteristics of viscous friction, offering a more precise representation of frictional behavior across various velocity ranges. Furthermore, an iterative identification scheme is employed to to obtain the optimal physically feasible parameters of the dynamic model, thereby enhancing the model's robustness and performance. Building on the identified physics-based model, we introduce a clear learning-based method that integrates physical knowledge into the black-box model to enhance friction modeling. Through experimental analysis of six different methods, it is demonstrated that the proposed method outperforms the others.

关键词： Temperature dependence Torque Friction Noise Closed box Collaborative robots Robustness Manufacturing Object recognition Iterative methods

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Improved Dynamic Model Identification of Cobots Based on a Nonlinear Friction Model

Improved Dynamic Model Identification of Cobots Based on a N...

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26th International Conference on Industrial technology, ICIT 2025

作者： Ruan, Hang Yao, Bitao Xu, Wenjun School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China School of Information Engineering Wuhan University of Technology Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan China

ISBN: (纸本)9798331521950

关键词： Collaborative robots

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Improved finite-size analysis for measurement-device-independent quantum digital signature

arXiv

引用

arXiv 2025年

作者： Zhu, Jia-Li Zhang, Chun-Hui Wang, Yue-Ying Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China "Broadband Wireless Communication and Sensor Network Technology" Key Lab of Ministry of Education NUPT Nanjing210003 China "Telecommunication and Networks" National Engineering Research Center NUPT Nanjing210003 China

Quantum digital signatures (QDS), based on the principles of quantum mechanics, provide information-theoretic security, ensuring the integrity, authenticity, and non-repudiation of data transmission. With present QDS protocols, measurement-device-independent QDS (MDI-QDS) can resist all attacks on detections, yet it suffers from finite-size effect. In this work, we present and compare three parameter estimation models for finite-size analysis of two-decoy MDI-QDS. The first model is a commonly used model in previous schemes, and we propose two new models to improve the performance. Subsequently, we perform numerical simulations to evaluate the performance of the three models. The results demonstrate that the proposed methods are less affected by finite-size effect, thereby effectively enhancing the signature rate. This work contributes to the practical development of QDS. Copyright © 2025, The Authors. All rights reserved.

关键词： Authentication

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Hybrid Implementation for Untrusted-node-based Quantum Key Distribution network

arXiv

引用

arXiv 2025年

作者： Liu, Jingyang Zhou, Xingyu Ding, Huajian Xu, Jiaxin Zhang, Chunhui Li, Jian Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China "Broadband Wireless Communication and Sensor Network Technology"Key Lab of Ministry of Education Ministry of Education Nanjing210003 China "Telecommunication and Networks"National Engineering Research Center NUPT Nanjing210003 China

Quantum key distribution (QKD) serves as a cornerstone of secure quantum communication, providing unconditional security grounded in quantum mechanics. While trusted-node networks have facilitated early QKD deployment, their vulnerability to node compromise underscores the need for untrusted-node architectures. Measurement-device-independent QKD (MDI-QKD) and twin-field QKD (TF-QKD) have emerged as leading candidates, addressing security vulnerabilities and extending transmission distances. Despite the wide adoptions in various fiber scaling, no integrated implementation of these two protocols has been demonstrated to date. Here, we present a hybrid system that seamlessly integrates TF-QKD and MDI-QKD into one untrusted-node-based architecture. Utilizing an efficient phase estimation method based on asymmetric interferometers, we convert twin-field global phase tracking to relative phase calibration, allowing near continuous running of both protocols. Experiments demonstrate secure finite-size key rates for sending-or-not-sending QKD and MDI-QKD over fiber distances of 150 to 431 km. The results align with theoretical simulations and show the ability to surpass the absolute repeaterless key capacity. Our work offers an unified framework for deploying multi-protocol QKD networks, laying the foundation for adaptable and scalable quantum infrastructures that can meet a wide range of security and performance needs. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum communication

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