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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN

An Energy-efficient Traffic Scheduling Method based on Slime...

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International Conference on Information Systems and Computer networks (ISCON)

作者： Zheyuan Wang Junli Wang Chungang Yan Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Software defined network (SDN) enables efficient and green traffic management by separating the control and data planes. However, the existing itemized scheduling approach is prone to waste of network resources and energy consumption due to the different sensitivity of traffic to time delay. To address these problems, we design a Slime Mould Algorithm based Energy-efficient Traffic Scheduling Method (SMA-ETSM) for SDN. First, we model the traffic scheduling problem with delay requirements to evaluate the decision. Second, in order to minimize the generation of invalid solutions in traffic scheduling, the encoding of the slime mould algorithm is improved, and a slime mould adaptation and update mechanism suitable for energy-efficient traffic scheduling is designed. The experiments show that SMA-ETSM can effectively reduce the energy consumption and improve the overall bandwidth utilization of the network compared with ECMP and ACO algorithm, and it also has some improvement in the operation efficiency compared with the original slime mould algorithm.

关键词： Energy consumption Sensitivity Processor scheduling Scheduling Energy efficiency Real-time systems Time factors

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A Money Laundering Structure Detection Method Based on Incremental Transaction Analysis

A Money Laundering Structure Detection Method Based on Incre...

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Data Science and Information Technology (DSIT), International Conference on

作者： Yifei Meng Zhong Li Key Laboratory of Embedded System and Service Computing Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China College of Information Science and Technology Donghua University Shanghai China

Money laundering is the process of legitimizing dirty money through complex transactions, posing a serious threat to a country’s financial stability and national security. Nowadays, with the prevalence of organized money laundering, launderers prefer to use intricate multi-hop laundering chains to transfer dirty money. Moreover, they engage in normal financial activities to disrupt detection by auditors. In response to these trends and challenges, we propose a novel unsupervised money laundering structure detection framework for the anti-money laundering field, called structure incremental expansion (SIE). Our framework consists of two main modules: 1) Initialization of suspicious structures, which adopts a control-limit based method to identify suspicious accounts exhibiting anomalous transaction behavior. These accounts will serve as the starting points for suspicious structure expansion. 2) Dynamic structure expansion, where we design three dynamic membership functions according to the Financial Action Task Force’s definitions of the three stages of money laundering evolution. Newly-added incremental transactions in the network will be assigned to appropriate expanding suspicious structures. We conduct extensive experiments on simulated and public financial networks. SIE exhibits desirable performance and scalability. We also provide a detailed case study, visualizing a complete money laundering structure detection process, demonstrating our method’s strong interpretability.

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Implicit privacy preservation:a framework based on data generation

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Security and Safety 2022年第1期1卷 166-183页

作者： Qing Yang Cheng Wang Teng Hu Xue Chen Changjun Jiang Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai 201804China National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security Tongji UniversityShanghai 201804China

This paper addresses a special and imperceptible class of privacy,called implicit *** contrast to traditional(explicit)privacy,implicit privacy has two essential properties:(1)It is not initially de ned as a privacy attribute;(2)it is strongly associated with privacy *** other words,attackers could utilize it to infer privacy attributes with a certain probability,indirectly resulting in the disclosure of private *** deal with the implicit privacy disclosure problem,we give a measurable de nition of implicit privacy,and propose an ex-ante implicit privacy-preserving framework based on data generation,called *** framework consists of an implicit privacy detection module and an implicit privacy protection *** former uses normalized mutual information to detect implicit privacy attributes that are strongly related to traditional privacy *** on the idea of data generation,the latter equips the Generative Adversarial network(GAN)framework with an additional discriminator,which is used to eliminate the association between traditional privacy attributes and implicit *** elaborate a theoretical analysis for the convergence of the *** demonstrate that with the learned generator,IMPOSTER can alleviate the disclosure of implicit privacy while maintaining good data utility.

关键词： Privacy preservation Implicit privacy Generative adversarial network Data utility Data generation

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Software defined edge computing for distributed management and scalable control in IoT multinetworks

arXiv

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arXiv 2021年

作者： Wu, Di Nie, Xiang Deng, Hanhui Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Recently, edge computing has emerged as a promising paradigm to support mobile access in IoT multinetworks. However, coexistence of heterogeneous wireless communication schemes brings about new challenges to the mobility management and access control in such networks. To maintain its computing continuum, Software-Defined networking (SDN) architecture can be applied to the distributed edge networks, which provides consistent frameworks for mobility management and creates secure mechanisms for access control. In this paper, we first review the state-of-the-art SDN technology and its applications in edge computing. In particular, we highlight the prominent issues related to mobility management and access control for SDN in the edge computing environment. We also introduce a number of effective solutions to these issues by presenting real-world testbed experiments, prototypes and numerical analysis of adopting SDN based edge computing paradigms for IoT multinetworks. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning

Routing Strategy for SDN Large Flow Based on Deep Reinforcem...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yu Ke Junli Wang Chungang Yan Jiamin Yao Key Laboratory of Embedded System and Service Computing Collaborative Innovation Center for Financial Network Security (Tongji University) Ministry of Education National (Province-Ministry Joint) Tongji University Shanghai China

With the expansion of network scale, traditional routing strategies in Software-Defined networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network traffic. An unreasonable schedule of the large flow will lead to the emergence of bottleneck links and reduce the network performance. To solve the above problems, this paper proposes a different routing method, called Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning (RSDRL). Considering the links in the network that can carry large flow are limited, RSDRL can schedule large flow firstly and generate routing strategies adaptively. At the same time, in order to avoid the occurrence of congestion, RSDRL quantifies the degree of congestion in the network to implement routing. Experiments show that the proposed model has better Quality of Service (QoS) performance under the test traffic, realizing a reasonable schedule of traffic.

关键词： Deep learning Training Knowledge engineering Schedules Adaptation models Reinforcement learning Quality of service

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A Dual-stage Attention Based SDN Traffic Prediction Method

A Dual-stage Attention Based SDN Traffic Prediction Method

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Chenjing Gao Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stage attention based Traffic Prediction method for the SDN traffic matrix prediction task. First, our method uses temporal pattern attention as the inter-flow attention mechanism before the encoding stage to realize adaptive feature extraction. Then, temporal attention is introduced as the intra-flow attention mechanism before the decoding stage to capture long-term temporal dependencies. Finally, we use the autoregressive module to handle the highly dynamic SDN traffic volume. Experimental results show that our proposed SDN traffic prediction method can capture more traffic features and improve the SDN traffic matrix prediction performance to some extent.

关键词： network topology Time series analysis Prediction methods Machine learning Feature extraction Graph neural networks Encoding

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Joint Partial Offloading and Resource Allocation for Distributed Multi-access Edge computing Enabled Vehicular network

TechRxiv

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TechRxiv 2022年

作者： Ma, Guifu Hu, Manjiang Wang, Xiaowei Wu, Di Li, Haoran Bian, Yougang Zhu, Konglin The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Hunan University Wuxi214072 China The College of Design Hunan University Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China The College of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In the foreseeable Intelligent Transportation System (ITS), Intelligent Connected Vehicles (ICVs) will play an important role in improving travel efficiency and safety. However, it is challenging for ICVs to support the resource-hungry autonomous driving applications due to the limitation of hardware computing power. Fortunately, the emergence of Multi-access Edge computing (MEC) helps overcome this limitation effectively. In this paper, we investigate the vehicle and edge server collaborative computation offloading problem by jointly optimizing the partial offloading ratio and resource allocation ratio. Different from existing two-level centralized solution methods, we give a new perspective to deal with multi-variable non-convex optimization problem in a distributed manner. That is, by analyzing the feasibility conditions of the problem, we separate the coupling between variables and derive the bounds of partial offloading ratio and resource allocation ratio based on the constraints. We further separate the coupling variables among ICVs and transform the original problem into an equivalent form and decompose it into a two-level convex problem, where each sub-problem satisfies the task offloading requirements. We then develop two low-complexity distributed algorithms to obtain the optimal solution. Finally, extensive simulations demonstrate that our proposed algorithms can converge faster and significantly outperform the baseline schemes in terms of system cost. © 2022, CC BY.

关键词： Resource allocation

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LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-label Text Classification

LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-la...

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IEEE International Conference on Big Data

作者： Yifan Xu Minghao Zhu Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education. National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

ISBN: (纸本)9781665480468

Extreme Multi-label text Classification ( XMC) is a task of recalling the most relevant labels for each given text from an extremely large-scale label set. It is emphasized that XMC is a more complex classification task because there are two main problems: large space of labels and the labels in XMC tasks tend to be correlated. Existing methods attempt to model label correlations by viewing the XMC task as a sequence generation problem however they still suffer from (1) using the slow serial decoding strategy where labels are predicted one-by-one. (2) needing to compare a mass of label ordering strategies in the decoding stage to achieve satisfied accuracy. In this work, we propose LAbel Mask-Predicted Transformer (LAMPT) to address the both issues, which is a novel non-autoregressive generation model that (1) enriches the input raw text representation with the additional label features by fully exploiting the label dependencies, (2) allows for efficient parallel decoding thanks to its non-autoregressive decoding formulation and mask-prediced training strategy. Experimental results demonstrate that single model performance is substantially enhanced by LAMPT. On a Wiki dataset with thirty-one thousand labels, LAMPT-XLNet accuracy has gained 1.6% relative improvement on P@3 over the LightXML-XLNet. Also, the P@1 of ensemble LAMPT is 90.00%, a significant i mprovement over the state-of-the-art ensemble LightXML (transformer-based) and AttentionXML (LSTM-based), which achieve 89.45% and 87.47%, respectively.

关键词： Training Correlation Text categorization Information services Big Data Predictive models Transformers

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Software-Defined Edge computing: A New Architecture Paradigm to Support IoT Data Analysis

arXiv

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arXiv 2021年

作者： Wu, Di Xie, Xiaofeng Nie, Xiang Fu, Bin Deng, Hanhui Zeng, Haibo Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China Department of Electrical and Computer Engineering Virginia Tech United States EECS Queen Mary University of London United Kingdom

—The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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