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Content Collaborative Caching Strategy in the Edge Maintenance of Communication Network: A Joint Download Delay and Energy Consumption Method

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第12期33卷 4148-4163页

作者： Rui, Lanlan Song, Dai Chen, Shiyou Yang, Yingtai Yang, Yang Gao, Zhipeng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the development of Big Data technology and Internet, the surge of data in the network will cause network congestion and untimely task processing. Additionally, caching content in the core network may cause redundant access of content and backhaul bottlenecks. Due to the increasing requirements of users for task processing efficiency, the centralized maintenance system based on traditional cloud computing cannot meet the current computing requirements. In view of these problems, we propose a content collaborative caching mechanism based on joint decision of download delay and energy consumption. By integrating network coding and content caching technology, the work content maintained in the communication network is deployed near the edge of the network in the form of coding to reduce the redundant transmission of content and acquisition time of content. This article establishes a user QoE satisfaction model, which consists of two indexes that measure time delay and energy consumption. This article proposes a e-hybrid Q-learning algorithm to optimize the placement of cache files, and made the cache action selection based on the combination of improved heuristic greedy algorithm and simulated annealing algorithm. The experimental results show that the proposed cache strategy can reduce the delay of users downloading content and the energy consumption of content cache, so as to improve the quality of field maintenance work in communication network.

关键词： Multi-access edge computing communication network content caching quality of experience

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Joint Communication and Computation Resource Allocation in Fog-Based Vehicular Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13195-13208页

作者： Zhang, Xinran Peng, Mugen Yan, Shi Sun, Yaohua Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

To satisfy the low-latency requirements of emerging computation-intensive vehicular services, offloading these services to edge or cloud servers has been recognized as an effective solution. Due to the limited resources of edge servers and the faraway distance of cloud servers, it is challenging to provide an efficient resource allocation strategy to balance the latency, throughput and the resource utilization. In this paper, an end-edge-cloud collaboration paradigm is presented for computation offloading in fog-based vehicular networks (FVNETs) by incorporating vehicles with idle resources as fog user equipments (F-UEs). To adaptively orchestrate end-edge-cloud resources in different load cases, a two-timescale resource reservation and allocation framework is proposed. Wherein, a Stackelberg-game-based dynamic F-UE incentive problem is first formulated with the cloud server as the leader and multiple F-UEs as the followers, and then an iterative algorithm is proposed to achieve the Stackelberg equilibrium of the computation resource pricing and reservation. On a small timescale, the joint communication and computation resource allocation problem is transferred into a multiagent stochastic game and a lenient multiagent deep-reinforcement-learning-based distributed algorithm is developed to minimize the sum latency. When latency performance deteriorates, F-UE incentive optimization will be triggered to reserve more resources of F-UEs. Simulation results show that the proposed end-edge-cloud orchestrated computation offloading scheme in FVNETs outperforms baselines in terms of average latency.

关键词： Resource management Servers Cloud computing Task analysis Vehicle dynamics Collaboration Games Computation offloading fog-computing-based vehicular networks (VNs) multiagent deep reinforcement learning (DRL) resource allocation

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DBT-PDP:Provable data possession with outsourced data batch transfer based on blockchain

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High-Confidence Computing 2024年第2期4卷 62-72页

作者： Chengming Yi Hua Zhang Weiming Sun Jun Ding State key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Zhe Jiang Computer Network Emergency Response Technical Team Hangzhou 310052China

In the scenario of large-scale data ownership transactions,existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communication,which greatly affects their *** paper proposes a data integrity audit scheme based on blockchain where data ownership can be traded in batches.A data tag structure which supports data ownership batch transaction is adopted in our *** update process of data tag does not involve the unique information of each data,so that any user can complete ownership transactions of multiple data in a single transaction through a single transaction auxiliary *** the same time,smart contract is introduced into our scheme to perform data integrity audit belongs to third-party auditors,therefore our scheme can free from potential security risks of malicious third-party *** analysis shows that our scheme is proved to be safe under the stochastic prediction model and k-CEIDH *** with similar schemes,the experiment shows that communication overhead and computing time of data ownership transaction in our scheme is ***,the communication overhead and computing time of our scheme is similar to that of similar schemes in data integrity audit.

关键词： Data integrity Cloud outsourcing audit Data ownership batch transaction Blockchain Smart contract

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Quantum secure multi-party computational geometry based on multi-party summation and multiplication

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QUANTUM SCIENCE AND technology 2024年第2期9卷 025023-025023页

作者： Dou, Zhao Wang, Yifei Liu, Zhaoqian Bi, Jingguo Chen, Xiubo Li, Lixiang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr Beijing 100876 Peoples R China

Secure multi-party computational geometry is a branch of secure multi-party computation, which is applied in many important fields. But up to now, the research on how to solve this problem with quantum methods has just started. Therefore, we study the design of quantum secure multi-party computational geometry (QSMCG) protocols in this paper. As the foundation, we extend the two-party summation and multiplication protocols proposed by Sutradhar and Om to multi-party protocols. Then we propose three new QSMCG protocols. Our proposed quantum secure two-party distance computation protocol has improvement in efficiency and accuracy. And the proposed quantum secure multi-party polytope volume computation protocol as well as the quantum secure circle-circle intersection area computation protocol are designed for the first time. Moreover, these protocols are designed to use multi-third parties, which solves the problem of intermediate value leakage in computation. The correctness, security and performance of all these protocols are analyzed.

关键词： quantum secure multi-party computational geometry accuracy multi-TP quantum multi-party summation and multiplication

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Fast and Scalable ACL Policy Solving Under Complex Constraints With Graph Neural Networks

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IEEE-ACM TRANSACTIONS ON networking 2024年第5期32卷 4175-4190页

作者： Sun, Haifeng Liao, Xingjian Wang, Jingyu Qi, Qi Zhuang, Zirui Liao, Jianxin Wu, Dapeng Oliver Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Network operators often need to modify Access Control List (ACL) policies to align with to network upgrades. An essential part of the ACL update task is reachability satisfaction. Previous studies formalize reachability requirements as a set of constraints and then use Boolean Satisfiability (SAT) or Satisfiability Modulo Theories (SMT) solvers to search for solutions. However, as today's networks grow in size and complexity, the constraints derived from the requirements become increasingly complex, leading to an unacceptable time cost to obtain a correct policy. The sluggish updating of ACL policies can affect the properties of a network, such as connectivity and security. This paper presents a novel approach for fast and scalable ACL policy synthesis under complex constraints. We utilize Graph Neural Networks (GNNs) to learn the relations between nodes and reason the solution that satisfies the update requirements. We further integrate global position encoding into the GNN architecture, which allows for better differentiation of nodes in ACL update tasks. Additionally, an enhanced stochastic local search solver is introduced to address incorrect predictions made by the GNN. Experiments on real-world topologies show that GNN saves up 278 x time costs compared to advanced SAT/SMT solvers on a 125-node network, and this advantage expands with the network size. Furthermore, our model extrapolates well when faced with different requirements and topologies, demonstrating its ability to handle frequent network upgrades.

关键词： Graph neural networks Topology Costs Task analysis Encoding Sun Performance evaluation Access control list graph neural network network management

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AlsoDTN: An Air Logistics Service-Oriented Digital Twin Network Based on Collaborative Decision Model 1

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20th International Conference on Service-Oriented Computing (ICSOC)

作者： Fu, Qianlong Chen, Dezhi Sun, Haifeng Qi, Qi Wang, Jingyu Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (数字)9783031265075

ISBN: (纸本)9783031265068;9783031265075

As cities expand and the pace of life accelerates, modern logistics services need to explore new air routes. While air logistics is fast and convenient, it also faces many problems, such as time-sensitive dynamic order requirements, the limited battery power of unmanned aerial vehicle (UAV) and so on. Besides, UAVs need to cooperate and make decisions in real time to meet city-wide logistics needs. However, limited by the computing power, it is difficult to process massive logistics demands in real time. In this paper, we propose an Air Logistics Service-Oriented Digital Twin Network based on collaborative decision model, called AlsoDTN. Firstly, in order allocation task, we establish an information fusion mechanism based on Transformer architecture to obtain the optimal order. Secondly, to adapt to the long-term route planning task, we use multi-agent deep reinforcement learning technology to make UAVs cooperate with each other. Experimental results show that our algorithm has an improvement of 18% relative to baseline algorithm.

关键词： Service-oriented architecture Digital twin network Logistics transport Multi-agent deep reinforcement learning

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DLI-Net: Dual Local Interaction Network for Fine-Grained Sketch-Based Image Retrieval

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO technology 2022年第10期32卷 7177-7189页

作者： Sun, Haifeng Xu, Jiaqing Wang, Jingyu Qi, Qi Ge, Ce Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Fine-grained sketch-based image retrieval (FG-SBIR) is considered an ideal method of image retrieval due to the rich and easily accessible characteristics of sketches. It aims to find the most similar photo from the photo gallery based on the input sketch. Most previous works follow the paradigm that extracting global feature first and then projecting the features of sketch and photo to unified embedding feature space using triplet loss. However, the global feature is not appropriate for extracting the crucial fine-grained information. Based on this principle, we propose a Dual Local Interaction Network (DLI-Net). DLI-Net explores an effective and efficient way to utilize local features for FG-SBIR. Specifically, we first propose a Local Feature Extractor to extract mid-level local features. Then, in response to the problems brought by local features, we propose a Dual Interaction Module, which contains Self Interaction Module and Cross Interaction Module. Self Interaction Module speeds up retrieval by eliminating the redundant local features of background. Cross Interaction Module solves the spatial misalignment by making the sketches interact with photos. Extensive experiments on six commonly used datasets show that our DLI-Net outperforms state-of-the-art competitors by a significant margin with a reasonable retrieval speed. Moreover, to the best of our knowledge, DLI-Net is the first model that beats humans on all six datasets. Besides, DLI-Net also performs best on cross-category fine-grained sketch-based image retrieval task, which further demonstrates local features are more appropriate for FG-SBIR.

关键词： Feature extraction Task analysis Image retrieval Shape Detectors Proposals Computer architecture FG-SBIR sketch local features dual interaction module self interaction module cross interaction module

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Beam prediction and tracking mechanism with enhanced LSTM for mmWave aerial base station

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WIRELESS NETWORKS 2024年第9期30卷 7513-7525页

作者： Zhang, Jinli Zhou, Fanqin Li, Wenjing Qi, Fei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Telecom Corp Ltd Beijing Res Inst Beijing 102209 Peoples R China

By combining millimeter wave (mmWave) with abundant spectral resources and advanced directional beamforming technology, mmWave aerial base stations (mAeBSs) can provide high-speed services to users on the ground while reducing interference to existing terrestrial networks. Due to the mobility of users and the corresponding changes of other environmental factors, the beam misalignment between mAeBSs and users will pose a serious challenge to the stability and quality of communication link. Consequently, we propose schemes based on deep learning for beam prediction tracking in this paper. On the basis of received signals from both the present and previous beam training, we apply long short term memory (LSTM) module to realize the beam prediction. In order to enhance performance of LSTM scheme, cascaded LSTMs are designed in the model. Furthermore, we attain the derivative of implicit state function of the beam changes to formulate an ordinary differential equation (ODE) problem and construct the LSTM_ODE model for beam prediction. Simulations demonstrate that, in comparison to existing other schemes, our proposed approaches attain greater beamforming gain while incurring less overhead during beam training.

关键词： Beam prediction and tracking MmWave aerial base stations Deep learning LSTM

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Chainknot: Redactable Blockchain Protocol With Forced Synchronization

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第3期11卷 3091-3104页

作者： Duan, Junke Gu, Lize Wang, Wei Wang, Licheng 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

Immutability has always been a core attribute of blockchain to ensure a trusted public ledger. However, in recent years, many malicious actions on blockchain have raised controversy over strict immutability. As a practical matter, it is necessary to design redactable blockchain protocols in some cases. Previous studies have proposed methods to implement redaction features in permissionless blockchains. However, in these protocols, parties can refuse to accept redacted blocks without affecting their mining. It makes these protocols lack feasibility in practice. In this paper, we present Chainknot, a redactable blockchain protocol with forced synchronization. We use the incentives of permissionless blockchain to effectively facilitate the review of a redaction proposal, and design a novel blockchain structure that ensures the acceptance of modified blocks by parties. In addition, we present instantiations of Chainknot on the PoW-based and PoS-based blockchain systems, with further improvements for efficiency and security, respectively. The experimental results show that the additional latency our protocol imposes on the original immutable blockchain system is negligible (less than 4%).

关键词： Blockchain redactable blockchain forced synchronization GDPR

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Cross-Domain Integration for General Sensor Data Synthesis: Leveraging LLMs and Domain-Specific Generative Models in Collaborative Environments

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IEEE SENSORS JOURNAL 2024年第24期24卷 42311-42326页

作者： Zhou, Xiaomao Hu, Yujiao Jia, Qingmin Xie, Renchao Future Network Res Ctr Purple Mt Labs Nanjing 211111 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Synthetic data has emerged as a critical component in the fields of machine learning and data science, providing a solution to overcome limitations associated with real-world data, including scarcity, privacy concerns, and the high cost of acquisition. While the existing generative models demonstrate promising results in synthesizing various sensor data, they are struggling to enhance performance and generalization. In this article, we introduce a large language models (LLMs)-driven framework that leverages the power of LLMs in concert with various domain-specific generative models (DGMs) for general sensor data synthesis. Specifically, our method employs LLMs as the core to interpret task requests, decompose a complex task into a manageable set of subtasks, and delegate each subtask to the most suitable DGM, thereby automatically constructing customized data generation pipelines. Meanwhile, DGMs contribute their expertise to generate high-fidelity, domain-relevant data, whose specialized knowledge can be further enhanced by the LLM's broad linguistic knowledge via knowledge transfer. In addition, the integration of diffusion model (DM)-based reinforcement learning (RL) is promising to enhance the framework's ability to optimally utilize DGMs, resulting in data generation with superior quality and control flexibility. Experimental results demonstrate the effectiveness of LLMs in augmenting DGMs via knowledge transfer and in facilitating multimodal data synthesis through collaborative interactions with diverse DGMs, which is further improved by the DM-based refinement process.

关键词： Diffusion model (DM) generative models knowledge transfer large language models (LLMs) reinforcement learning (RL) sensor data synthesis Diffusion model (DM) generative models knowledge transfer large language models (LLMs) reinforcement learning (RL) sensor data synthesis

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