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SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

China Communications 2023年第10期20卷 185-199页

作者： Ningjie Gao Ru Huo Shuo Wang Jiang Liu Tao Huang Yunjie Liu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China

With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT *** order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of *** has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication *** order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the *** multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve ***,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme.

关键词： Industrial Internet of Things Byzantine fault tolerance speculative consensus mechanism Markov decision process deep reinforcement learning

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RoboFormer: A Robust Multi-Modal Transformer for 3D Object Detection in Autonomous Driving 6

RoboFormer: A Robust Multi-Modal Transformer for 3D Object D...

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6th ACM International Conference on Multimedia in Asia, MMAsia 2024

作者： Liu, Yuang Liao, Dacheng Qi, Mengshi Liu, Liang Ma, Huadong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400712739

Multimodal fusion-based perception models are essential for 3D object detection in autonomous driving. However, existing approaches often lack robustness evaluations and corresponding design considerations for real-world challenges, such as adverse weather conditions and sensor noise. Existing robustness solutions are costly for already-trained models because they either involve fine-tuning on corrupted data or adding extra sensor inputs. In this work, we propose RoboFormer, a model with inherent robustness and strong scalability across multiple modalities. Our approach combines Top-Down and Bottom-Up methods for mapping modal space to BEV, enhancing robustness. We also use a Transformer-based network for multimodal and temporal fusion, improving performance and facilitating diverse modality extension. To further improve robustness, we introduce a Batch Normalization (BN) fine-tuning method that enables models to effectively handle abrupt domain shifts during inference in extreme scenarios, eliminating the need for retraining on corrupted datasets. Experimental results demonstrate that RoboFormer achieves state-of-the-art performance, while the BN fine-tuning method enhances robustness in the presence of data corruption. The source code is available at the project page. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Object detection

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IMAP-GCG: Edge Container Resource Scheduling and Configuration Method Based on Improved MAPPO and GCN-GRU 13th

IMAP-GCG: Edge Container Resource Scheduling and Configurati...

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13th International Conference on Computer Engineering and networks, CENet 2023

作者： Gong, Xingle Yang, Yang Sun, Yating Gao, Zhipeng Rui, Lanlan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819992386

Container technology has been widely used in industrial development, including system deployment and program management, and has achieved very good results. However, in the current edge network environment, there are delay problems caused by container cold start and user terminal task changes, which need to be solved by designing a flexible and efficient edge container resource configuration method. In response to this problem, this paper proposes a method for dynamic container resource allocation called Improved Multi-Agent PPO with GCN-GRU (IMAP-GCG). This method mainly implements the prediction of terminal workload, container resource pre-configuration and container resource scheduling methods. First, we propose a timing prediction model with GCN and GRU as the encoder and LSTM as the decoder to predict the temporal periodicity of user requests, so as to deploy containers in advance. Then, a MAPPO algorithm based on trusted region policy optimization is proposed to schedule container resources, which improves the performance of container resource scheduling. Experiments on two workloads show that the IMAP-GCG model proposed in this paper is superior to the existing advanced multi-agent reinforcement learning model in terms of CPU utilization, bandwidth utilization, and task average delay. The task type forecasting accuracy on this indicator is due to the existing advanced time series forecasting models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Containers

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Graph Neural network Enhanced Dynamic Routing for UAV-Assisted Delay Tolerant network 24

Graph Neural Network Enhanced Dynamic Routing for UAV-Assist...

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12th International Conference on Communications and Broadband networking, ICCBN 2024

作者： Gao, Ran Tan, Can Fang, Honglin Yu, Peng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400717109

In certain extreme environments where infrastructure such as base stations is absent, connections between nodes can be intermittent. Traditional networks struggle to function in these conditions. Delay Tolerant network (DTN) are a network architecture designed for such scenarios. Utilizing Unmanned Aerial Vehicle (UAV) as nodes within DTN can enhance the network's transmission capabilities and compensate for the deficiencies of ground nodes. However, previous routing methods have shortcomings. Given the high speed and greater freedom of movement of UAVs compared to ground nodes, the network topology is subject to constant and significant change. Traditional routing methods, which typically rely on predetermined rules for selecting forwarding nodes, struggle to adapt to such variations. This paper introduces Graph Neural network (GNN) into routing decisions for DTN to learn network structural information and assist with routing decisions. In this paper, we first model the routing problem within DTN, accounting for the dynamic changes in network topology and limited resources. Subsequently, we propose a GNN-based dynamic routing method. This method utilizes a graph neural network model that takes as input the feature s of nodes at the current moment and the network's connectivity information to generate optimal forwarding nodes for each data packet. The proposed method has been implemented in the Opportunistic networking Environment (ONE) simulator. Experimental results indicate that our method achieves higher delivery ratio, lower latency, and reduced overhead compared to traditional methods. © 2024 ACM.

关键词： Delay tolerant networks

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The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites

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China Communications 2024年第4期21卷 208-217页

作者： Wang Shangguang Zhang Qiyang Xing Ruolin Qi Fei Xu Mengwei State Key Laboratory of Networking and Switching Beijing University of Posts and TelecommunicationsBeijing 100876China Star Network and Intelligent Computing Laboratory Beiyou Shenzhen InstituteShenzhen 518000China Huawei Technologies Co. LtdShenzhen 518129China

Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit(LEO).However,these constellations often rely on bent-pipe architecture,resulting in high communication *** onboard inference architectures suffer from limitations in terms of low accuracy and inflexibility in the deployment and management of in-orbit *** address these challenges,we propose a cloud-native-based satellite design specifically tailored for Earth Observation tasks,enabling diverse computing *** this work,we present a case study of a satellite-ground collaborative inference system deployed in the Tiansuan constellation,demonstrating a remarkable 50%accuracy improvement and a substantial 90%data *** work sheds light on in-orbit energy,where in-orbit computing accounts for 17%of the total onboard energy *** approach represents a significant advancement of cloud-native satellite,aiming to enhance the accuracy of in-orbit computing while simultaneously reducing communication cost.

关键词： cloud-native satellite orbital edge computing satellite inference verification test

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Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

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Computers, Materials & Continua 2024年第5期79卷 2697-2721页

作者： Kamlesh Kumar Soothar Yuanxiang Chen Arif Hussain Magsi Cong Hu Hussain Shah School of Electronics Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an710071China State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)*** emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of *** existingliterature witnessed various partial or inadequate *** the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and *** traditional fault managementsystems,this research has three-fold ***,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC ***,this paper assesses the classificationdelay of each classification ***,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults *** work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various *** addition,this work evaluated the performance of DL-based Convolutional Neural network and Long-Short Term Memory(CNN-LSTM)hybrid *** findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 *** the other hand,EL techniques improved the accuracy in detecting fiberoptic ***,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.

关键词： Fiber optics fault detection multiclassification machine learning ensemble learning

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Robust Transmission Design for Covert Satellite Communication Systems With Dual-CSI Uncertainty

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IEEE Internet of Things Journal 2025年第12期12卷 21892-21903页

作者： Jia, Huaiqi Wang, Ying Wu, Wen Yuan, Jun Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Frontier Research Center Shenzhen518000 China Joint Laboratory of Technology and Applications on Frequency Orbital Space Information Networks China Satellite Network System Research Institute Co Ltd Beijing100070 China

In this paper, we investigate a novel covert transmission scheme for satellite communication systems. Specifically, the satellite employs a rate-splitting multiple access technique to covertly transmit messages to multiple users and simultaneously transmit jamming signals, thereby enhancing the covert rate and robustness while avoiding detection by a warden. Due to the long propagation delay and lack of the warden's precise location information, acquiring perfect channel state information (CSI) between the satellite, covert users, and the warden is difficult. To this end, we establish a dual-CSI uncertainty model, which incorporates phase and norm-bounded uncertainty for the covert and wiretap channels to characterize the actual CSI. In addition, we formulate a stochastic optimization problem with the objective of maximizing the minimum covert rate while adhering to covert communication constraints. The optimization problem is non-convex and difficult to solve directly due to the dual-CSI uncertainty and the coupled nature of the variables. To solve the problem, we reformulate the original problem into a series of convex optimization problems by utilizing semidefinite relaxation, fractional programming, and the S-procedure methods. Then, we propose a robust Common Rate Allocation and Beamforming (CRAB) algorithm to obtain near-optimal solutions for the beamforming vectors and common rate allocation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms baseline schemes in terms of both covert rate and robustness. © 2014 IEEE.

关键词： Satellite communication systems

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Efficient Collaborative Perception with Adaptive Communication in Bandwidth-Constrained Scenarios 6

Efficient Collaborative Perception with Adaptive Communicati...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Guo, Junbo Luo, Guiyang Yuan, Quan Li, Jinglin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798331541699

In autonomous driving, collaborative perception significantly improves safety by addressing occlusions and expanding the perceptual range of individual vehicles. Nevertheless, effectively exchanging data among vehicles under bandwidth constraints presents a major challenge, especially regarding adaptive communication strategies. To tackle this, we introduce Adacomm (Adaptive Communication for Collaborative Multi-agent Perception), a network-adaptive framework that enhances multi-agent collaborative perception by optimizing data exchange to improve perception accuracy and reduce communication costs. The Adacomm framework consists of modular operations such as blind spot identification, multi-scale feature generation, data scheduling optimization, shared message fusion, and detection decoding. Experimental results demonstrate that Adacomm enhances multi-vehicle perception by 16.8% in AP@IoU=0.5, compared to traditional single-vehicle perception, and lowers average communication costs by 29.5% relative to the existing Where2comm method, with a minimal performance decline of 5.6%. Our analysis across different feature layers indicates a trade-off between communication volume and perception accuracy, with medium-resolution features providing a balanced solution. Overall, Adacomm effectively addresses critical challenges in collaborative perception, showing substantial benefits and advancements in scenarios with limited communication resources. © 2024 IEEE.

关键词： Bandwidth

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Energy Efficient Multi-aerial Base Station Deployment Via DDPG-Mix Algorithm 13th

Energy Efficient Multi-aerial Base Station Deployment Via D...

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13th International Conference on Computer Engineering and networks, CENet 2023

作者： He, Haoran Zhao, Yikun Zhang, Jinli Zhou, Fanqin Li, Wenjing Feng, Lei State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819992386

Compared to ground base stations, aerial base stations (AeBSs) offer advantages in terms of overcoming limitations posed by complex terrain and transportation constraints, providing broader coverage and faster response speeds. In this paper, we investigate the problem of energy efficiency maximization in a multiple AeBSs network and adopt multi-agent deep reinforcement learning (MADRL) for the deployment of AeBSs. Specifically, considering the partial observation range of AeBSs, the multi-AeBS deployment problem is modeled as a decentralized partially observable markov decision process (Dec-POMDP) and a deep deterministic policy gradient mix (DDPG-MIX) algorithm is designed to solve the problem. The proposed algorithm uses the value decomposition framework to solve the lazy agent problem. Simulation results show that our proposed DDPG-MIX algorithm performs better than other baseline algorithms in the multi-AeBS deployment scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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Locating the Root Cause of Large-scale BGP Anomaly with Routing Dependence

Locating the Root Cause of Large-scale BGP Anomaly with Rout...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zhao, Shiqi Huang, Xiaohong Zhang, Pei Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350367942

BGP anomaly has a major impact on the stability, availability, and efficiency of the Internet, thus locating the network element which initiates the anomaly is crucial for network recovery. However, BGP is an information-hiding protocol and BGP anomalies are heterogeneous, which limit the ability to perform root cause localization. This paper proposes an approach to automatically detect large-scale BGP anomaly and locate the root cause in an efficient and generalized fashion. Firstly, time-series analysis and clustering strategy are performed on BGP traffic to ascertain "when"and "where"anomaly happened to start the investigation. Next, based on interdomain routing's propagation property, a subtopology is constructed which not only reveals the dynamic evolution during anomaly, but also helps narrow down the scale of the Internet for further root cause analysis. Last, by taking deep analysis in the routing characteristics of the root cause from different anomalous scenarios, a routing dependence-based algorithm is developed to realize the localization goal. The proposed method is validated on three different real-world, large-scale BGP anomalies. Results demonstrate its effectiveness and robustness, and in most cases the root causes identified with maximum likelihood are consistent with ground-truths. Further investigations reveal some insights which are helpful for network administrations. © 2024 IEEE.

关键词： anomaly detection BGP anomaly root cause localization routing dependence

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