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14th International Conference on Computer Engineering and Networks, CENet 2024

作者： Zhao, Lihua Wang, Zhili State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789819649624

Time-Sensitive networking (TSN) is a set of protocol standards being developed by the TSN Task Group within the IEEE802.1 Working Group. These standards define time-sensitive mechanisms for Ethernet data transmission to ensure that Ethernet can provide a stable and consistent level of service for the transmission of critical data. In existing TSN static scheduling algorithms, either the reliability of traffic flows is not considered, or scheduling is performed using only backup path transmission schemes. This paper addresses the no-wait TSN scheduling problem by proposing an enhanced tabu search (ETS) algorithm based on multiple sets of redundant paths. The primary objective is to maximize the scheduling success rate, with minimizing the standard deviation of link load as a secondary objective. A mathematical model is established, and a method for calculating the reliability of traffic flows is proposed. Using a K-shortest Paths (KSP) algorithm, multiple sets of redundant paths are generated, and the global load is updated based on adaptive threshold values. Experimental results show that the proposed ETS effectively improves the reliability and success rate of traffic flows while reducing the variance of network load. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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Trusted Transaction Mechanism of Virtual Power Plant Based on PoC Consensus 14th

Trusted Transaction Mechanism of Virtual Power Plant Based o...

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14th International Conference on Computer Engineering and Networks, CENet 2024

作者： Fu, Hao Sun, Yan Qiu, Xuesong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789819649624

The concept of virtual power plant (VPP) has evolved to enhance the flexibility and reliability of power systems by integrating distributed energy re-sources (DERs). However, traditional VPP transaction models suffer from trust issues, such as default deliveries and payment arrears, which under-mine the welfare of trading parties and threaten systemic stability. To address these challenges, this paper proposes a blockchain-based trusted trading mechanism tailored for VPPs. The proposed mechanism is structured around a consortium blockchain with a hierarchical architecture, featuring one primary chain and multiple secondary chains. A reputation score-based Proof of Contribution (PoC) consensus algorithm is introduced to improve transaction efficiency and system security. The algorithm leverages historical trading behavior to dynamically adjust mining difficulty, incentivizing honest behavior among participants. Simulation results demonstrate that the PoC consensus algorithm significantly enhances transaction processing efficiency while providing robust defenses against malicious activities, paving the way for a secure and efficient VPP trading environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Consensus Algorithm Trusted Transaction Virtual Power Plant

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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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PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem

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BMC BIOINFORMATICS 2023年第1期24卷 1-21页

作者： Suo, Jingwen Gu, Lize Yan, Xingyu Yang, Sijia Hu, Xiaoya Wang, Licheng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China

BackgroundAs one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over *** main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server;Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012;And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP *** pro

关键词： Double digest problem Privacy-preserving Outsourcing computation Order-preserving homomorphic index scheme Quantum inspired genetic algorithm

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Maximizing Optical Inter-DC Emergency Backup Reliability in Unpredictable Disasters 97

Maximizing Optical Inter-DC Emergency Backup Reliability in ...

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97th IEEE Vehicular technology Conference (VTC-Spring)

作者： Wang, Ying Liu, Jiang Cui, Mingwei Wu, Weihong Huang, Tao Liu, Yunjie BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China Purple Mt Lab Nanjing Peoples R China

ISBN: (纸本)9798350311143

The emergency backup problem of optical inter-datacenter (inter-DC) networks is widely studied to avoid data loss caused by disasters. However, when unpredictable disasters such as earthquakes and large-scale power outages attack the optical inter-DC network, it is impossible to predict the network damage under a discrete time domain through the early warning system (EWS). Designing a reliable emergency backup scheme is becoming a key challenge in the optical inter-DC network in unpredictable disasters. Previous work only optimized emergency backup in the optical inter-DC networks in predictable disasters, which would lead to the unreliability of emergency backup of the optical inter-DC networks in unpredictable disasters. Therefore, this work proposes a reliable emergency backup method for the optical inter-DC networks in unpredictable disasters. Firstly, the disaster probability propagation model of the optical inter-DC network in unpredictable disasters is established based on the Markov process to quantify the network damage under a discrete time domain and install the emergency backup reliability problem. Next, constructing a time-sensitive variable time extension network (TS-VTEN) transforms the dynamic emergency backup reliability problem into a static flow problem. Finally, the problem is expressed as integer linear programming (ILP). The proposed ILP method outperforms state-of-the-art emergency backup methods by simultaneously achieving high reliability and time efficiency.

关键词： Optical inter-datacenter networks Emergency backup Time-expanded networks (TEN) Disaster probabilistic propagation Unpredictable disaster Reliability

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Online Service Request Duplicating for Vehicular Applications

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第7期22卷 4168-4180页

作者： Li, Qing Ma, Xiao Zhou, Ao Joo, Changhee Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Korea Univ Dept Comp Sci & Engn Seoul 02841 South Korea

Vehicles on roads have increasingly powerful computing capabilities and edge nodes are being widely deployed. They can work together to provide computing services for onboard driving systems, passengers, and pedestrians. Typical applications in vehicular systems have service requirements such as low latency and high reliability. Most studies in vehicular networks concerning latency and reliability focus on vehicular communication at the network level. Based on these fundamental works, an increasing proportion of vehicles boast complex applications that require service-level end-to-end performance guarantees. Several works guarantee service-level latency or reliability while new and innovative applications are demanding a joint optimization of the above two metrics. To address the critical challenges induced by the joint modeling of latency and reliability, system uncertainty, and performance and cost trade-off, we employ service request duplication to ensure both latency and reliability performance at the service level. We propose an online learning-based service request duplication algorithm based on a multi-armed bandit framework and Lyapunov optimization theory. The proposed algorithm achieves an upper-bounded regret compared to the oracle algorithm. Simulations are based on real-world datasets and the results demonstrate that the proposed algorithm outperforms the benchmarks.

关键词： Reliability Task analysis Computer network reliability Telecommunication network reliability Reliability theory Costs Uncertainty Vehicular edge computing service-level latency service-level reliability service request duplication

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Semi-Asynchronous Federated Split Learning for Computing-Limited Devices in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5196-5212页

作者： Ao, Huiqing Tian, Hui Ni, Wanli Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science Singapore

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness. © 2002-2012 IEEE.

关键词： Federated learning

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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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Enforcing group fairness in privacy-preserving Federated Learning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 160卷 890-900页

作者： Chen, Chaomeng Zhou, Zhenhong Tang, Peng He, Longzhu Su, Sen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Shandong Univ Key Lab Cryptol Technol & Informat Secur Minist Educ Qingdao Peoples R China Shandong Univ Sch Cyber Sci & Technol Qingdao Peoples R China

With the expansive application of Federated Learning (FL) in critical domains such as healthcare and financial services, ensuring group fairness in FL has become imperative. The intrinsic distributed architecture of FL, while enhancing training efficiency, introduces significant challenges in maintaining group fairness due to client data privacy concerns and heterogeneous data distribution. For this reason, we present a suite of innovative strategies to navigate these challenges. First, we construct an approximate global dataset to achieve global group fairness. It was accomplished by collecting statistical information from clients and generating an approximate global dataset that circumvents the direct sharing of raw data. Moreover, we present the Local Sensitive Hashing-Categorical Attribute Data Completion (LSH-CADC) method, which improves the precision and efficiency of the generation of the approximate global dataset. In addition, an adaptive fairness-weighted aggregation (AFA) algorithm was developed to enhance the model's group fairness. By integrating these methodologies within the classical privacy-preserving FL method HybridAlpha, we get a new framework, enforcing group fairness in privacy-preserving Federated Learning (GFL). GFL not only ensures privacy but also solidifies group fairness. Through extensive experimentation in various cross-dataset/domain non-IID scenarios, GFL demonstrated exceptional performance and group fairness metrics, surpassing existing benchmarks and highlighting its significant potential to promote group fairness.

关键词： Federated learning Privacy-preserving Group fairness Differential privacy Machine learning

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Max-Min Fairness Robust Beamforming for LEO Satellite Multibeam Communication Systems With Two CSI Uncertainty Model

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 9368-9381页

作者： Meng, Meng Hu, Bo Chen, Shanzhi Kang, Shaoli Zhang, Hao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China China Acad Telecommun Technol State Key Lab Wireless Mobile Commun Beijing 100191 Peoples R China China Mobile Commun Corp Res Inst China Mobile Beijing 100033 Peoples R China

The widespread employ of Internet of Things (IoT) devices relies on the massive deployment of sensor nodes and data collection timely. Benefit from development of low-Earth orbit (LEO) satellite technology, the LEO Satellite is considered an effective way for achieving wider coverage to terrestrial IoT devices in remote area. However, it is challenging to obtain perfect channel state information (CSI) in LEO satellite system because estimation error and longer round trip time in practice. To overcome this problem, we propose max-min fairness (MMF) robust beamforming deterministic uncertainty model of imperfect CSI convex-concave optimization algorithm (D-ICCA) and stochastic uncertainty model of imperfect CSI convex-concave optimization algorithm (S-ICCA) in LEO satellite communication system, respectively. MMF optimization problems are formulated under the constraints of the maximum per-antennas power constraint in the LEO satellite communication system. In deterministic uncertainty model of imperfect CSI, we obtain the lower bound of CSI by the Cauchy-Schwarz inequality first. Then, we transform the formulated MMF optimization problem to a series of standard convex problem and solve convex optimization subproblems by adopting the alternating direction method of multipliers (ADMM) to obtain suboptimal robust beamforming vectors. In stochastic uncertainty model of imperfect CSI, we model the MMF optimization problem with stochastic phase error and solve the MMF optimization problem via ADMM with closed-from solution to obtain suboptimal robust beamforming vectors. Finally, simulation results demonstrate that the proposed D-ICCA and S-ICCA beamforming algorithms can achieve better performance than ConADMM and FFA-SCA without robust design.

关键词： Optimization Alternating direction method of multipliers (ADMM) low-Earth orbit (LEO) satellite max-min fairness (MMF) MIMO robust beamforming

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