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End-to-End Delay Performance Analysis of Industrial Internet of Things: A Stochastic Network Calculus Perspective

IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 5374-5387页

作者： Cui, Peng Han, Shujun Xu, Xiaodong Zhang, Jingxuan Zhang, Ping Ren, Shoushou Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Guangdong Peoples R China Huawei Technol Co Ltd Network Lab Beijing 100085 Peoples R China

In a hybrid scenario of 5G and Industrial Internet of Things (IIoT), there is a lack of a theoretical tool to analyze probabilistic end-to-end (E2E) delay. In this article, we provide a comprehensive procedure, which is based on stochastic network calculus (SNC) with moment-generating functions (MGFs), for calculating the E2E delay violation probability for the target traffic in IIoT. The particularity of the scenario is that the E2E network is composed of two segments: 1) the industrial wireless link challenged by the complex fading channel and 2) the multinode wired network that supports common schedulers. An improved concatenation theorem is proposed to calculate the service capability of the E2E network, and a method based on Meijer G-functions is proposed to calculate the MGF for the service processes of various wireless fading channels. We investigate the impacts of various resource allocation strategies (on both wireless and wired networks) and parameters (e.g., bandwidth, weight, and cycle time) on probabilistic E2E delay and provide numerical performance bounds. We show that the capability joint adaptation of the wireless and wired networks is the key to E2E service guarantee. Moreover, related parameters such as the weights and message sizes should be carefully considered to improve E2E delay.

关键词： Delay violation probability end-to-end delay Industrial Internet of Things (IIoT) moment-generating function (MGF) scheduling stochastic network calculus (SNC)

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Advances and challenges in semantic communications:A systematic review

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National Science Open 2024年第4期3卷 172-207页

作者： Ping Zhang Yiming Liu Yile Song Jiaxiang Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Broadband Communication Peng Cheng LaboratoryShenzhen 518055China

Inspired by the recent success of machine learning(ML),the concept of semantic communication introduced by Weaver in 1949 has gained significant attention and has become a promising research *** conventional communication systems,semantic communication emphasizes the precise retrieval of conveyed meaning from the source to the receiver,rather than focusing on the accurate transmission of ***,semantic communication can achieve a significant gain in source data compression,alleviate communication bandwidth pressure,and support new intelligent services,which is envisioned as a crucial enabler of future sixth-generation(6G)*** this review,we critically summarize the advances made in semantic information and semantic communications,including theory,architecture,and potential ***,we deeply explore the major challenges in developing semantic communications and present the development prospects,aiming to prompt further scientific and industrial advances in semantic communications.

关键词： semantic communications semantic information 6G artificial intelligence deep learning

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Resource Allocation for Metaverse Experience Optimization: A Multi-Objective Multi-Agent Evolutionary Reinforcement Learning Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 3473-3488页

作者： Feng, Lei Jiang, Xiaoyi Sun, Yao Niyato, Dusit Zhou, Yu Gu, Shiyi Yang, Zhixiang Yang, Yang Zhou, Fanqin Beijing Univ Posts & Telecommun BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China Univ Glasgow James Watt Sch Engn Glasgow City Scotland Nanyang Technol Univ Coll Comp & Data Sci Singapore City Singapore

In the Metaverse, real-time, concurrent services such as virtual classrooms and immersive gaming require local graphic rendering to maintain low latency. However, the limited processing power and battery capacity of user devices make it challenging to balance Quality of Experience (QoE) and terminal energy consumption. In this paper, we investigate a multi-objective optimization problem (MOP) regarding power control and rendering capacity allocation by formulating it as a multi-objective optimization problem. This problem aims to minimize energy consumption while maximizing Meta-Immersion (MI), a metric that integrates objective network performance with subjective user perception. To solve this problem, we propose a Multi-Objective Multi-Agent Evolutionary Reinforcement Learning with User-Object-Attention (M2ERL-UOA) algorithm. The algorithm employs a prediction-driven evolutionary learning mechanism for multi-agents, coupled with optimized rendering capacity decisions for virtual objects. The algorithm can yield a superior Pareto front that attains the Nash equilibrium. Simulation results demonstrate that the proposed algorithm can generate Pareto fronts, effectively adapts to dynamic user preferences, and significantly reduces decision-making time compared to several benchmarks.

关键词： Metaverse Quality of experience Delays Resource management Optimization Heuristic algorithms Rendering (computer graphics) Wireless communication Energy consumption Costs Metaverse experience multi-objective optimization resource allocation Meta-Immersion energy consumption

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Strongly Synchronized Redactable Blockchain Based on Verifiable Delay Functions

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IEEE INTERNET OF THINGS JOURNAL 2023年第19期10卷 16778-16792页

作者： Wang, Wei Duan, Junke Wang, Licheng Hu, Xiaoya Peng, Haipeng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

As one of the crucial features of the blockchain technique, immutability plays the most important role in winning the so-called praise of the "trust machine" for blockchain. However, there are two sides to everything. The property of immutability of blockchain is applied maliciously sometimes, say publishing harmful or even dangerous data and hindering authorities' law enforcement. To address this issue, authorized redactability of blockchain was introduced to support block modification without lowering the fundamental basis of security and trust that is cherished on the blockchain. During the past years, several techniques of redactable blockchain were proposed, mainly based on the well-known chameleon hashing. Different from existing methodologies, we propose a new redactable blockchain scheme for permissioned settings in this article. We first employ the trapdoor verifiable delay function to attach a time-lapse proof to each block. Moreover, the trapdoor is used to quickly construct a chain fork to redact blocks that are authorized to alter. Our proposal does not need to rollback irrelevant blocks. As a remarkable and unique feature, our proposal realizes the property of strong synchronization of redaction, which means that all nodes in the blockchain will have identical views on the chain even after some blocks are altered. Security analysis shows that the consistency of the chain is guaranteed, and the long-range attack can be resisted effectively. The performance comparison shows that our method is feasible and practical.

关键词： Consistency long-range attack resistance redactable blockchain verifiable delay functions (VDFs)

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Collaborative Task Offloading Based on Deep Reinforcement Learning in Heterogeneous Edge Networks 20

Collaborative Task Offloading Based on Deep Reinforcement Le...

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20th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Du, Yupeng Huang, Zhenglei Yang, Shujie Xiao, Han Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Res Inst China Mobile Beijing Peoples R China

ISBN: (纸本)9798350361261;9798350361278

The field of multimedia video has shown a vigorous development trend in recent years. The massive heterogeneous data computing tasks generated by new multimedia applications cause user experience to face two major challenges: latency and energy consumption. The emergence of multimedia edge networks allows users' computing tasks to be offloaded to edge servers for execution, which will greatly reduce communication delays. But on the contrary, multimedia edge networks also face problems such as limited communication and computing resources, unknown global system information and difficulty in multi-agent collaboration. In this paper, we first describe a multimedia edge network architecture. In this architecture, multiple edge servers use communication base stations to provide multimedia computing services to a large number of users. Then a multi-agent collaborative decision-making model is proposed, which ensures that agents with different strategies can ultimately achieve consistent decision-making. Finally, we provide a distributed computing task scheduling algorithm based on deep reinforcement learning. A large number of simulation experiments have proven the superiority of this method in minimizing delay, energy consumption and convergence.

关键词： multimedia task offloading network multi agent deep reinforcement learning

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Semantic information processing for interoperability in the Industrial Internet of Things

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Fundamental Research 2024年第1期4卷 8-12页

作者： Shengshi Yao Yanpeng Lu Kai Niu Jincheng Dai Chao Dong Ping Zhang Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

With the advent of the Internet of Everything(IoE),the concept of fully interconnected systems has become a reality,and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever *** address the challenges posed by massive data traffic,we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial *** particular,the scheme is featured with task-orientation and collaborative *** illustrate its applicability,we provide examples of time series and images,as typical industrial data sources,for practical tasks,such as lifecycle estimation and surface defect *** results show that semantic information processing achieves a more efficient way of information processing and exchanging,compared to conventional methods,which is crucial for handling the demands of future interconnected industrial networks.

关键词： Semantic communication Intelligent manufacturing Signal processing Industrial Internet Task-oriented communication

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CSMA/PJ: A Protective Jamming Based MAC Protocol to Harmonize the Long and Short Links

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IEEE-ACM TRANSACTIONS ON networking 2023年第1期31卷 118-132页

作者： Zhu, Shaopeng Zheng, Xiaolong Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

WiFi-based Long Distance (WILD) networks are promising to cover the rural and remote regions. But the explosive short-range Wail deployments result in the long-short coexistence. Due to CSMA is ignorance of propagation delay, its carrier sensing is too short to detect long links, leading to the temporal hidden terminal problem that causes serious performance degradation and even starvation of long links. Existing methods for traditional hidden terminal problem are inefficient to cope with this problem because of the different causes. In this paper, we propose CSMA with Protective Jamming (CSMA/PJ), a new WiLD MAC protocol that solves the temporal hidden terminal problem with the minimized influence on uncontrollable short links. The key is generating protective jamming at the WILD receiver that is sensible to the short links. By leveraging the asymmetric propagation delay of the WiLD transmitter and receiver, we make the jamming protective rather than destructive. We precisely control the jamming right before the arrivals of WiLD packets to set aside channel time for short links. We implement and evaluate CSMA/PJ on commercial devices. The experimental results show that CSMA/PJ can improve the throughput of the WiLD link by 6x and 5x compared with the CSMA/CA and RTS/CTS methods.

关键词： WiFi-based long distance coexistence MAC protocol

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Harmony: An Eco-Friendly Adaptive Rate Control Scheme for Video-on-Demand in Low Earth Orbit Satellite Internet

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO technology 2025年第4期35卷 3867-3881页

作者： Li, Zitong Xu, Changqiao Xiao, Han Fang, Chuxing Zhong, Lujie Yang, Shujie Muntean, Gabriel-Miro Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin 9 Ireland

This paper addresses the rate control issue for Video-on-Demand (VoD) services in Low Earth Orbit (LEO) satellite Internet. LEO systems employ long-distance Non-Orthogonal Multiple Access (NOMA), where the transmission rate of the last hop directly determines the Quality of Experience (QoE) levels for the VoD users and the satellite's energy consumption. Our research identifies two primary issues: (i) determining the transmission rate to ensure high user QoE while minimizing energy consumption, and (ii) ensuring fairness among users within the satellite coverage area. To address these issues, we model the multi-user VoD viewing process as a Partially Observable Markov Process (POMDP) and describe the interactions among users using a cooperative coalition game framework. We propose Harmony, a distributed and dynamic improvement solution based on the Deep Deterministic Policy Gradient (DDPG) approach. Harmony intelligently determines each user's transmission rate by combining feedback from user applications and MEC server metrics, ensuring superior QoE levels, energy efficiency, and fairness. The trained Harmony can be adapted to various Adaptive BitRate (ABR) algorithms, providing scalability and immediate applicability in existing LEO networks. It can also achieve improved performance in dynamic user environments. Simulation results demonstrate that Harmony improves energy efficiency and fairness, while maintaining high QoE levels and reducing MEC traffic overhead by 28.1% to 62.6%.

关键词： LEO satellite internet video-on-demand non-orthogonal multiple access transmission rate decision dynamic cooperative reinforcement learning LEO satellite internet video-on-demand non-orthogonal multiple access transmission rate decision dynamic cooperative reinforcement learning

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Strengthening attention: knowledge distillation via cross-layer feature fusion for image classification

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INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2024年第2期13卷 23-23页

作者： Zhai, Zhongyi Liang, Jie Cheng, Bo Zhao, Lingzhong Qian, Junyan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Guilin Peoples R China

Deep learning has achieved great success in computer vision, especially in image classification tasks. How to improve the generalization ability and compactness of deep neural networks has gradually attracted widespread attention from researchers. Knowledge distillation is an effective technique for model compression. It transfers general knowledge from a sophisticated teacher model to a smaller student model. Recently, some studies refine knowledge from feature maps or adopt complex attention mechanisms to better supervise students imitating teachers. However, their methods focus too much on how to improve students' accuracy and largely overlook the associated training costs, which violates the original intention of knowledge distillation to compress the model. To achieve a balance between performance and efficiency, in this paper, we introduce a straightforward and effective distillation method to utilize the deepest feature maps to enhance shallow features. Specifically, our method performs processing only on the original feature maps without an extra assisting network. Moreover, we use cross-layer feature fusion to enhance the attention on shallow feature maps. By visualizing the features of different layers, we demonstrate the importance of the fusion operation in our method. Our experimental results on the CIFAR-100, tinyImageNet and miniImageNet datasets show that our approach outperforms previous methods, especially in the balance between performance and training cost. Further ablative studies verify the effectiveness of the design.

关键词： Deep learning Knowledge distillation Image classification Attention

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Improving unified named entity recognition by incorporating mention relevance

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NEURAL COMPUTING & APPLICATIONS 2023年第30期35卷 22223-22234页

作者： Ji, Lijun Yan, Danfeng Cheng, Zhuoran Song, Yan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Shanghai Int Studies Univ Sch Business & Management Shanghai 200092 Peoples R China

Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped and discontinuous named entity recognition has received increasing attention. However, we note that few studies have considered both overlapped and discontinuous entities. In this paper, we proposed a novel sequence-to-sequence model that is capable of recognizing both overlapped and discontinuous entities based on machine reading comprehension. The model utilizes machine reading comprehension formulation to encode significant inferior information about the entity category. Then input sequence passes through a question-answering model to predict the mention relevance of the given source sentences to the query. Finally, we incorporate the mention relevance into the BART-based generation model. We conducted experiments on three type of NER datasets to show the generality of our model. The experimental results demonstrate that our model beats almost all the current top-performing baselines achieves a vast amount of performance boost over current SOTA models on overlapped and discontinuous NER datasets.

关键词： Named entity recognition Sequence-to-sequence model Machine reading comprehension Mention relevance attention

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