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20th European Conference on Computer Systems, EuroSys 2025, co-located 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Ma, Mingxiao Zhang, Yuehan Wang, Jingyu He, Bo Zhao, Chenyang Qi, Qi Zhuang, Zirui Sun, Haifeng Guo, Lingqi Guo, Yuebin Zhang, Gong Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Pengcheng Laboratory China *** China Huawei Technologies China

ISBN: (纸本)9798400711961

Data plane verification (DPV) can be critical in ensuring the network operates correctly. To be useful in practice, they need to be: (1) fast so as to prevent significant packet loss or security violations;(2) scalable so as to accommodate today’s large-scale network architecture. Current DPV tools struggle to meet these requirements due to their centralized architecture. To be concrete, there is a bottleneck for a single-point server to perform real-time DPV tasks. Furthermore, a single-point server makes it hard to collect real-time data plane updates from every device in large-scale networks. This paper proposes Atlas, a native distributed data plane verification (DPV) solution, which systematically solves its scalability problem and improves its real-time performance. To achieve this, (1) Atlas designs a hierarchical distributed architecture for DPV in large-scale networks. (2) Atlas proposes a native approach to maximize the parallelism of the architecture. Both dataset-driven simulation experiments and deployments in the wild demonstrate that Atlas is capable of fast and scalable distributed verification. The advantages offered by Atlas continue to expand as network sizes increase. Compared to state-of-the-art solutions, Atlas is 2-7× faster while keeping each component lightweight. © 2025 Copyright held by the owner/author(s).

关键词： Packet loss

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Quantum Computing Catalyzes Future Quantum Networks: Efficiency and Inherent Privacy

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IEEE NETWORK 2025年第1期39卷 124-131页

作者： Ju, keyi Zhong, Hui Zhang, Xinyue Qin, Xiaoqi Pan, Miao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Kennesaw State Univ Dept Comp Sci Marietta GA 30060 USA

Quantum networks show promise in enhancing the overall functional advantages of the Internet and enabling applications that are incomparable in the classical world. Supported by quantum communication, quantum networks are expected to offer almost unconditional security. However, securing personal privacy within quantum networks is of paramount importance. The aim of this paper is to extend the differential privacy (DP) framework to quantum networks, addressing the issue of personal privacy security in the future quantum networks. We begin by discussing the fundamental principles of quantum computing and DP. Additionally, in the current noisy intermediate-scale quantum (NISQ) era, unavoidable noise is inherent in quantum computing. We explore in detail how this inherent noise can be utilized to protect personal privacy while also enhancing the utility of data. Finally, we analyze several pressing challenges and open research directions in personal privacy protection within future quantum networks.

关键词： Quantum computing Noise Quantum networks Privacy Protection Quantum state Qubit quantum computing quantum differential privacy quantum noise

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GlobalLight: Exploring global influence in multi-agent deep reinforcement learning for large-scale traffic signal control

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NEUROCOMPUTING 2025年 637卷

作者： Liu, Yilin Liang, Jintao Zhang, Yifeng Gong, Ping Luo, Guiyang Yuan, Quan Li, Jinglin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol 10 Xitucheng Rd Beijing 100876 Peoples R China Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117575 Singapore

By treating each intersection as an intelligent agent, multi-agent deep reinforcement learning (MADRL) offers a promising solution to adaptive traffic signal control (ATSC) in complex urban environments. However, existing approaches often emphasize the interactions between adjacent intersections while overlooking the global influence of distant relationships. This oversight limits their scalability to small-scale traffic networks, reducing their effectiveness in real-world urban transportation systems. In this paper, we propose GlobalLight, a novel MADRL-based traffic signal control method that addresses these challenges by exploring and exploiting global influence in traffic networks. We first propose a multidimensional feature extraction module via a multi-head graph attention network, which captures the mutual influence among locally adjacent intersections. Then we design a similarity mining module with two loss functions to analyze node embeddings in the representation space, uncovering latent relationships across distant intersections in the global traffic network. Finally, GlobalLight enables similar intersections to share policy parameters for decision-making within an effective MADRL framework. Our method simultaneously considers local dependencies between adjacent intersections and global traffic flow influence, enhancing scalability and decision efficiency for ATSC in city-level larger-scale traffic systems. Experimental evaluations on both synthetic and real-world traffic networks, encompassing up to 1000 of intersections, demonstrate that our method significantly outperforms SOTA approaches across multiple performance metrics, particularly in large-scale traffic scenarios.

关键词： Deep reinforcement learning Adaptive traffic signal control Intelligent transportation system Global influence exploration

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Space-Terrestrial Integrated Time-Sensitive Networks: Architecture, Challenges, and Open Issues

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IEEE Network 2025年第3期39卷 140-147页

作者： Peng, Guoyu Wang, Shuo Li, Guizhen Huang, Tao Huang, Yudong Liu, Yunjie The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Purple Mountain Laboratories Nanjing211111 China The China Mobile Chengdu Institute of Research and Development Chengdu610000 China

Space-terrestrial integrated network (STIN) has been recognized as a novel network paradigm to offer global seamless coverage and enhanced service capacity in emerging 6G networks. Being a promising technology, time-sensitive networking (TSN) has been deemed essential for supporting delay sensitivity and driving service perception in STIN. However, most of the existing works in TSN-enabled STIN focus on migrating terrestrial TSN mechanisms to satellite networks, with little attention given to the feasibility of delay-oriented task processing across earth-space domains. Furthermore, the problem of hierarchical TSN platform integration architecture design and heterogeneous time-sensitive clusters cooperative communication, have not been fully considered. Thus, different from previous studies, this article presents a novel network architecture named space-terrestrial time-sensitive network (STTSN), which integrates space, aerial, and terrestrial TSN clusters to provide ubiquitous coverage, predictable low-latency connectivity, and quality of experience (QoE)-driven service continuity. The key functional modules of the STTSN are detailed, and we propose the promising technical challenges, including Internet of Things (IoT) service slicing, multi-domain cooperative communication, mobility management, and dynamic configuration. Finally, some potential research issues for future research are discussed. © 1980-2012 IEEE All rights reserved.

关键词： Internet of things

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MDVSCEfficient Wireless Model Division Video Semantic Communication

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IEEE INTERNET OF THINGS JOURNAL 2025年第2期12卷 1109-1124页

作者： Bao, Zhicheng Liang, Haotai Dong, Chen Li, Cong Xu, Xiaodong Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518000 Peoples R China Acad Space Elect Informat Technol Natl Key Lab Sci & Technol Space Microwave Xian 710100 Peoples R China

This article introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based semantic importance coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and semantic importance coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.

关键词： Semantics Encoding Feature extraction Wireless communication Channel coding Streaming media Bandwidth Joint source-channel coding (JSCC) latent transform model division multiple access (MDMA) semantic coding semantic communications video transmission

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Rethinking Congestion Management in Intra-Host Networks

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IEEE COMMUNICATIONS MAGAZINE 2025年第4期63卷 184-191页

作者： Wan, Zirui Zhang, Jiao Wang, Yuxiang Liu, Kefei Pan, Yongchen Huang, Tao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China ByteDance Inc Beijing Peoples R China

Conventional wisdom believes that the intra-host network provides ample and stable band-width. However, to meet the growing demand of datacenter applications, modern commodity servers are equipped with numerous heterogeneous hardware accelerators. These devices consume substantial intra-host network resources (e.g., memory bus bandwidth, cache sizes, etc.), while technology trends in these areas have stagnated. As a result, congestion emerges in the intra-host network and leads to performance jitters. In this article, we revisit existing works in intra-host congestion management and find that these works typically focus on only partial root causes of the congestion and lack a holistic view of the intra-host network. Then, we propose host congestion management (HCM), a holistic congestion management architecture for intra-host networks. HCM consists of three components: a holistic intra-host status collector, a congestion risk level monitor, and a resource allocation manager. HCM directly utilizes hardware/software counters or implements state-of-the-art architectural innovations to realize a fine-grained system while ensuring high performance. We implement HCM on commodity servers and conduct multiple testbed experiments. The results show that HCM can consistently maintain high performance even with a high degree of intra-host congestion. Finally, we analyze and outline the future of congestion management in intra-host networks.

关键词： Smart homes Wireless fidelity Protocols Message systems IEEE 802.15 Standard Internet of Things Interoperability Instruction sets Zigbee Network topology Telecommunication network management

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Radio Frequency-Enhanced Multi-Factor IoT Device Authentication Via Swarm Learning

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IEEE Transactions on Network Science and Engineering 2025年

作者： Zhou, Fanqin Zhang, Lei Yang, Zhixiang Feng, Lei State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the increased popularity of Internet of things (IoT) devices, security issues have notably risen in recent times. Typically, wireless IoT applications are vulnerable to impersonation attacks by malicious entities. This paper proposes a lightweight multi-factor authentication mechanism boosted by radio frequency fingerprinting (RFF) to physically identify IoT devices. A novel application of swarm learning (SL) is utilized to develop the authentication model and enable distributed authentication. This approach maintains privacy and is resilient against faults when processing RFF data from various sources. The device-side multi-factor authentication is lightweight and has been validated through a formal security model. Experimental results indicate that the proposed scheme achieves the highest authentication success rate and the lowest computational cost on the device side compared to other authentication methods, which also validated its effectiveness in defending against impersonation and poisoning attacks. © 2013 IEEE.

关键词： Internet of things Multi-factor authentication Radio frequency fingerprint Swarm learning

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COMPRESSO: Latency-Aware Transmission of Compressed IoT Measurement Data Over SDN

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IEEE Internet of Things Journal 2025年

作者： Feng, Wendi Dou, Xintan Taherkordi, Amirhosein Cheng, Bo Zhang, Wei State Key Laboratory of Networking and Switching Technology China University of Oslo College of Computer Science Dept. of Informatics Norway State Key Laboratory of Networking and Switching Technology China

Measurement data obtained from "things"in the Internet of Things (IoT) faces challenges in efficient transmission due to the low-bandwidth data transmission *** observe that measurement data are fixed in size and format, and low-entropy in the time domain, indicating that compression can be benefited. Rather than employing a single compression algorithm as advocated in existing literature, we argue that optimal transmission can be achieved by jointly considering compression overheads and network status, where software-defined networking (SDN) is employed to enforce network statistics and packet forwarding. This paper presents a new paradigm that achieves optimal transmission of SDN-empowered compressed measurement data. We formulate the problem as an optimization problem and prove its non-polynomial hardness time complexity. Due to this complexity, we introduce COMPRESSO, a heuristic algorithm that efficiently solves the problem. We conduct rigorous simulations, and the results demonstrate the efficiency of the new paradigm and COMPRESSO, i.e., attaining comparable performance to the optimal solution with 50% time usage reduction. © 2014 IEEE.

关键词： Data transfer

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A Type Fusion and Span Relation Enhanced Event Extraction Framework for Confused Event 29th

A Type Fusion and Span Relation Enhanced Event Extraction Fr...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Meng, Fanshen Lin, Rongheng Beijing Univ Posts & Telecommun Sch Comp Sci State Key Lab Networking & Switching Technol Natl Pilot Software Engn Sch Beijing 100876 Peoples R China

ISBN: (纸本)9789819757787;9789819757794

Event extraction (EE) is a crucial aspect of information extraction facing challenges with overlapped, nested, and confused events. Most prior efforts have focused on overlapped or nested events, overlooking confused events. To address these issues, we introduce a novel event extraction model which employs type fusion to guide argument extraction tasks and utilizes span relation for event splitting to address the issue of confused event. This model simultaneously addresses the challenges of Overlapped, Nested and Confused events for Event Extraction named as ONCEE. Evaluation results on public event extraction datasets FewFC and FNDEE demonstrate a noteworthy improvement in our approach to event extraction. The average F1 score on the datasets increased by 1.8% compared to CasEE and 1.2% compared to OneEE. Besides, our model demonstrates superior performance in extracting confused events.

关键词： Information Extraction Event Extraction Confused Event

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Adaptive Hardcore Bit and Quantum key Leasing over Classical Channel from LWE with Polynomial Modulus 30th

Adaptive Hardcore Bit and Quantum Key Leasing over Classical...

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30th International Conference on the Theory and Application of Cryptology and Information Security

作者： Duong Hieu Phan Wen, Weiqiang Yan, Xingyu Zheng, Jinwei Telecom Paris LTCI Inst Polytech Paris Paris France Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9789819609468;9789819609475

Quantum key leasing, also known as public key encryption with secure key leasing (PKE-SKL), allows a user to lease a (quantum) secret key to a server for decryption purpose, with the capability of revoking the key afterwards. In the pioneering work by Chardouvelis et al. (arXiv:2310.14328), a PKE-SKL scheme utilizing classical channels was successfully built upon the noisy trapdoor claw-free (NTCF) family. This approach, however, relies on the superpolynomial hardness of learning with errors (LWE) problem, which could affect both efficiency and security of the scheme. In our work, we demonstrate that the reliance on superpolynomial hardness is unnecessary, and that LWE with polynomial-size modulus is sufficient to achieve the same goal. Our approach enhances both efficiency and security, thereby improving the practical feasibility of the scheme on near-term quantum devices. To accomplish this, we first construct a noticeable NTCF (NNTCF) family with the adaptive hardcore bit property, based on LWE with polynomial-size modulus. To the best of our knowledge, this is the first demonstration of the adaptive hardcore bit property based on LWE with polynomial-size modulus, which may be of independent interest. Building on this foundation, we address additional challenges in prior work to construct the first PKE-SKL scheme satisfying the following properties: ( i) the entire protocol utilizes only classical communication, and can also be lifted to support homomorphism. (ii) the security is solely based on LWE assumption with polynomial-size modulus. As a demonstration of the versatility of our noticeable NTCF, we show that an efficient proof of quantumness protocol can be built upon it. Specifically, our protocol enables a classical verifier to test the quantumness while relying exclusively on the LWE assumption with polynomialsize modulus.

关键词： Trapdoor claw-free functions Adaptive hardcore bit Secure key leasing Proofs of quantumness Learning with errors

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