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2nd International Conference on Green Communication, Network, and Internet of Things, CNIoT 2022

作者： Chen, Chen Li, Zhong The College of Information Science and Technology Donghua University Shanghai201620 China The Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (纸本)9781510662940

Time-Sensitive networking (TSN) meets the needs of industrial internet of things (IIoT). It solves the challenges of deterministic transmission and reliable communication of time sensitive data streams. Traffic scheduling is the core mechanism of time-sensitive networks. Many excellent researches have explored and optimized the method of scheduling time-triggered streams in TSN. However, the existing time-triggered streams scheduling in TSN mostly separates routing and scheduling, which limits the scalability of scheduling. Many researches are based on fixed routing to schedule streams. Due to the interaction between scheduling and routing, the quality of fixed route solution is inferior to that of joint routing and scheduling. In this paper, we propose a meta-heuristic based multipath joint routing and scheduling method for time-triggered traffic in TSN, named MMRS. We set path selection as a variable and consider multipath routing for fault tolerance. At the same time, we establish the integer linear programming formulation and use meta-heuristic to obtain high-quality solutions. The evaluations show that compared with other excellent routing and scheduling methods, the runtime of 60 streams of MMRS method is reduced by 65.6%, and the schedulability is improved by 21% on average. The experiments verify that our proposed scheduling method can obtain high-quality solutions in an acceptable solution time. © 2023 SPIE.

关键词： Internet of things

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Artificial Impostors: An Efficient and Scalable Scheme for Location Privacy Preservation

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IEEE Transactions on Services Computing 2025年第3期18卷 1262-1277页

作者： Hao Tang Kunfeng Chen Zhiyang Xie Cheng Wang College of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for efficient and scalable location privacy preservation (LPP). They are often vulnerable to inference attacks with side information, or hard to be implemented due to the high computational complexity. In this article, we pursue the high protection quality with low computational complexity. We propose a scalable LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them by synthesizing artificial impostors. The so-called artificial impostors refer to the synthesized traces that have similar semantic features to the actual traces, e.g., similar transition patterns, and do not contain any protected location, i.e., the exact location that needs to be protected. We devise two dedicated techniques: the station-based synthesis method and the population-level semantic model. We conduct the experiments on real datasets of two cities (Shanghai of China and Asturias of Spain) to validate the quality of privacy preservation, utility loss, and scalability of the proposed method. Based on these two datasets, the experimental results show that our method achieves the privacy preservation quality of 97.68% and 96.24%, respectively, and the time spent on building generators is only 144.96 seconds and 136.08 seconds, respectively. The experimental results also show that our method achieves a good trade-off between privacy and utility. Our study would give the research community new insights into improving the practicality of LPP paradigm via counterfeiting locations.

关键词： Privacy Semantics Feature extraction Urban areas Generators Protection Counterfeiting Computational efficiency Artificial intelligence Transforms

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BMSAD: Behavior Feature and Multi-Scenario -Based Sybil Attack Detection Method in Vehicular Networks 3

BMSAD: Behavior Feature and Multi-Scenario -Based Sybil Atta...

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3rd International Seminar on Artificial Intelligence, Networking, and Information Technology, AINIT 2022

作者： Luo, Jie Li, Zhong College of Information Science and Technology Donghua University Shanghai201620 China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai201804 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

Vehicular networks improve traffic safety and efficiency by wireless communications among vehicles and infrastructures. However, security has always been a challenge to vehicular networks, which may cause severe harm to the intelligent transportation systems. Sybil attack is considered as a serious security threat to vehicular networks since the attacker can disseminate false messages with multiple forged identities. In this paper, we designed a behavior feature and multi-scenario-based Sybil attack detection method, named BMSAD in vehicular networks. In BMSAD, we propose solutions for two scenarios: normal traffic and traffic congestion. A node is allowed to verify the authenticity of another node by estimating their geographic distance based on received signal strength, and compare them to its claimed localizations. Then we design long- and short-term pattern to analyze the similarity of vehicles' behavior features and trajectory for Sybil nodes detection, which act as the second line of defense in normal traffic scenario. At last, the Sybil nodes of the traffic congestion scenario are detected by logical inference based on kinematics theory. And experiment results demonstrate that the proposed scheme achieves high detection rate and low false positive rate. © 2023 SPIE.

关键词： Traffic congestion

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Discretized Gaussian Mixture Hyperprior for Learned Image Compression with Mask Module

Discretized Gaussian Mixture Hyperprior for Learned Image Co...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wang, Shengkai Wang, Hanli Tongji University Department of Computer Science and Technology Shanghai China Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Learned image compression approaches have shown great potential with promising results. However, according to the commonly used measurement methods, there still lies a performance gap between learned compression methods and the latest compression standard versatile video coding (VVC), because of the remaining redundancy existing in contemporary algorithms. To obtain a more accurate entropy model for rate estimation, discretized Gaussian mixture hyperprior is proposed in this work to parameterize the distribution of latent codes. In addition, a proposed mask module is exploited to enhance the feature extraction ability of the encoder and adaptively allocate bit rates. In this case, the proposed learned image compression model achieves the state-of-the-art performance among the existing learned compression methods and most compression standards on both Kodak24 and CLIC Mobile Validation datasets. The proposed model also outperforms VVC under several bitrate scenarios. © 2022 IEEE.

关键词： Image compression

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Attack by Yourself: Effective and Unnoticeable Multi-Category Graph Backdoor Attacks with Subgraph Triggers Pool

arXiv

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arXiv 2024年

作者： Li, Jiangtong Liu, Dongyi Cheng, Dawei Jiang, Changjun School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China

Graph Neural Networks (GNNs) have achieved significant success in various real-world applications, including social networks, finance systems, and traffic management. Recent researches highlight their vulnerability to backdoor attacks in node classification, where GNNs trained on a poisoned graph misclassify a test node only when specific triggers are attached. These studies typically focus on single attack categories and use adaptive trigger generators to create node-specific triggers. However, adaptive trigger generators typically have a simple structure, limited parameters, and lack category-aware graph knowledge, which makes them struggle to handle backdoor attacks across multiple categories as the number of target categories increases. We address this gap by proposing a novel approach for Effective and Unnoticeable Multi-Category (EUMC) graph backdoor attacks, leveraging subgraph from the attacked graph as category-aware triggers to precisely control the target category. To ensure the effectiveness of our method, we construct a Multi-Category Subgraph Triggers Pool (MC-STP) using the subgraphs of the attacked graph as triggers. We then exploit the attachment probability shifts of each subgraph trigger as category-aware priors for target category determination. Moreover, we develop a "select then attach" strategy that connects suitable category-aware trigger to attacked nodes for unnoticeability. Extensive experiments across different real-world datasets confirm the efficacy of our method in conducting multi-category graph backdoor attacks on various GNN models and defense strategies. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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An intelligent guided-airborne virus detection 21

An intelligent guided-airborne virus detection

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer Engineering, STA 2022

作者： Hadded, Ahmed Ayed, Mossaad Ben Alshaya, Shaya A. National Engineering School of Sfax Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Electronic Industrial ENISo Sousse University Tunisia ENIS Sfax University Computer and Embedded System Laboratory Sfax3038 Tunisia Majmaah University Faculty of Sciences and Humanities Sciences Department of Computer Science Saudi Arabia Prince Sultan University Dean of Digital Transformation and E-transactions Majmaah11952 Saudi Arabia

ISBN: (纸本)9781665482615

Health has recently faced many challenges, including improving a healthy environment and reducing human life's dangers and economic crises. The last pandemic COVID-19 had badly affected survivor sectors with infection and lockdown exigence. Scientists proposed several solutions to reduce the negative impact of a such pandemic by proposing systems for earlier detection of viruses. The use of metamaterials as an emerging technology in the biosensors field allows a high accuracy. This paper presents a method for detecting and capturing airborne viruses using metasurface technology. The goal is to develop a system that can identify and capture these viruses using FET sensors. The accuracy of the detection is tied to the concentration of aerosols. The model proposes a guided flow of aerosols that positively impacts the detection of viruses through the FET biosensor. The simulation results based on Concentration and airflow velocity delays prove the proposed model's performance. © 2022 IEEE.

关键词： Biosensors

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A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

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Chinese Journal of Electronics 2025年第4期23卷 706-711页

作者： Jiarui Zhou Zexuan Zhu Zhen Ji College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Shenzhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen China

A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.

关键词： Memetics Metabolomics Metaheuristics Urban areas Liver Prediction algorithms Search problems Physiology Vectors Classification algorithms

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Adaptive Granulation for Efficient Classification on Noise Labeled Images

Adaptive Granulation for Efficient Classification on Noise L...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Kecan Cai Hongyun Zhang Duoqian Miao Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positively to model training. This paper introduces a novel semi-supervised image data granulation method that employs adaptively generated granular noisy sample subsets in place of the original noisy samples to enhance classification efficiency. The granular data generated retain the essential features of the original data, thereby improving efficiency without compromising classification accuracy. The quality of the granular data is assessed using coverage and specificity criteria, standard metrics for evaluating information granules. The proposed method consists of three main components: (i)selecting clean and noisy samples through network co-training, (ii)calculating granular noisy sample subsets by adaptive granulation, and (iii)optimizing the network model using a semi-supervised strategy. Experimental results on benchmark datasets with varying noise rates demonstrate that our method significantly improves the efficiency of noisy label image classification while maintaining accuracy.

关键词： Training Adaptation models Accuracy Computational modeling Noise Benchmark testing Noise measurement Standards Image classification Noise level

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Consensus in Second-Order Discrete-Time MAS With Distinct and Multiple Coupling Matrices

Consensus in Second-Order Discrete-Time MAS With Distinct an...

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International Conference on systems and Informatics (ICSAI)

作者： Chenggong Sun Xiwei Liu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In this paper, we explore and establish several necessary and sufficient conditions that are instrumental in achieving consensus within second-order discrete-time multi-agent systems (SDMAS). Different with previous research, this study addresses the more intricate scenario where the velocity coupling matrix is distinct from the position coupling matrix. We introduce a novel eigenvector condition tailored to this challenge. Furthermore, in response to the burgeoning interest in multiweighted networks, this study is extended to SDMAS with multiple weights. We employ a technique, that rearranges the order of variables, to effectively simplify the analysis of multiweighted networks by transforming them into single-weighted equivalents. The efficacy of our method is substantiated through three numerical examples, which illustrate the practical value in elucidating consensus dynamics within SDMAS featuring diverse coupling matrices.

关键词： Couplings Sufficient conditions Instruments Numerical simulation Trajectory Numerical models Multiaccess communication Informatics Pragmatics Multi-agent systems

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Deep Learning-based Mortality Prediction of COVID-19 Patients with Cardiovascular Disease

Deep Learning-based Mortality Prediction of COVID-19 Patient...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Huilin Zheng Yingyi Liang Daming Zhang Kai Shi Luxi Dong Xiaolan Xie College of Computer Science and Engineering Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiovascular disease (CVD) have a higher risk of getting seriously ill. Therefore, this study proposes a deep learning-based prediction model for the mortality of COVID-19 patients with CVD. The research substances are introduced as follows. First, we obtain the original dataset from the Kaggle website provided by the Mexican government, and extract our experimental data. After that, we develop a deep learning-based prediction model for the mortality of COVID-19 patients with CVD while applying a 5-fold cross-validation approach for the model training and validation. Finally, we evaluate the performance of the proposed model and also compare it with many common machine learning models. In our experimental results, the proposed model achieved higher performance than all of the other models. It facilitates decision-making and provides an accurate mortality prediction for COVID-19 patients with CVD.

关键词： COVID-19 Training Support vector machines Radio frequency Government Mortality Machine learning Predictive models Nearest neighbor methods Cardiovascular diseases

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