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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Wireless modulation classification based on Radon transform and convolutional neural networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

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Enhancing Diabetic Retinopathy Detection using Hybrid Deep Learning Approach

Enhancing Diabetic Retinopathy Detection using Hybrid Deep L...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： S. Prasanth M. Roshni Thanka E. Bijolin Edwin V. Ebenezer Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

One of the main causes of impaired vision is diabetic retinopathy or DR, for which rapid and precise diagnosis is essential to successful medical care. This inquiry presents a hybridized deep learning AI model that combines a Support Vector Machine, that's the SVM classifier for improved outlined elegance with MobileNet for extracting the features. With low computational cost, MobileNet, a light weighted convolutional neural network, accurately extracts significant spatial information from retinal fundus imagery. SVM is also used to classify these detached characteristics, which improves the model's capacity to distinguish across different DR phases. In comparison to existing deep learning approaches, the suggested method delivers increased precision, excellence, and learned by fusing SVM's powerful framed features with Mobile Net’s crucial feature description.

关键词： Deep learning Support vector machines Diabetic retinopathy Computational modeling Transfer learning Medical services Feature extraction Retina Hybrid learning Data mining

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Blockchain-enabled Privacy Preserving of IoT Data for Sustainable Smart Cities using Machine Learning 14

Blockchain-enabled Privacy Preserving of IoT Data for Sustai...

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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Kumar, Priyan Malarvizhi Rawal, Bharat Gao, Jiechao Kyung Hee University Seoul Seoul Korea Republic of Gannon University Cyber Security Cyber Engineering EriePA United States University of Virginia Department of Computer Science CharlottesvilleVA United States

ISBN: (纸本)9781665421041

The development of sensor technologies and an explosion of the inexpensive electronic circuit, the Internet of Things (IoT) is emergent as an encouraging innovation to comprehend sustainable smart city. Smart cities can bid various intelligent applications like smart transportation, smart banking, and industry 4.0, among others, to boost citizens' life quality. However, security is one of the critical problems of a smart city. These emerging smart infrastructure and applications based on IoT can benefit users only if vital private and secure features are guaranteed. Hence, in this paper, Blockchain-enabled Privacy-Preserving Access Control System (BPACS) has been suggested for IoT data in a smart city environment. This study utilizes blockchain methods to construct a reliable and secure data-sharing policy between numerous data providers, where IoT information is encoded and then verified on disseminated ledgers. Furthermore, this study design protected construction blocks, like secure comparison and secure polynomial multiplications, by retaining cryptosystems and build a secure Support Vector Machine (SVM) and Principle Component Analysis (PCA) training algorithms. Hard security analysis proves that the suggested model guarantees the privacy of the sensitive information for every data provider and the SVM and PCA model variables for data analysts. © 2022 IEEE.

关键词： Blockchain

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Survival Analysis with Cox Proportional Hazards Model in Predicting Patient Outcomes 5

Survival Analysis with Cox Proportional Hazards Model in Pre...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Monikapreethi, S.K. Preetha, J. Reddy, K. Narsimha Ramya, S. Yuvaraj, S. Murugan, S. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Vardhaman College of Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India Paavai Engineering College Department of Cyber Security Tamil Nadu Namakkal India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

Survival analysis is crucial for understanding the factors that influence patient outcomes across time. The objective is to predict the outcomes of patient survival under various circumstances using the Cox Proportional Hazards Model. The main objectives are to assess the model's predictive ability and to determine the impact of different parameters on survival lengths. As a result of these efforts to enhance clinical prediction models, patient treatment strategies that are personalized to each person's individual risk profile will be improved. Integrating diverse patient data and evaluating the importance of various elements might improve insights into therapy success and prognosis accuracy. Improved healthcare delivery and individualized medical treatments are possible outcomes. This approach brings attention to the use of advanced statistical techniques in medical diagnosis and decision-making, which may improve patients' quality of life and raise their chances of survival. Age range: 58-72, stages: 1-4, comorbidities: yes (1, no), Karnofsky score: 60-90, follow-up: 12-30 months, confidence interval: 0.2% to 10.3%, hazard ratio: 0.8-3.5, p-value: 0.08-0.67, according to the Patient Survival Analysis Dataset, Stage-Specific Cancer Survival Analysis Dataset. © 2024 IEEE.

关键词： Prediction models

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Generative Adversarial Networks for Synthetic Jamming Attacks on UAVs

Generative Adversarial Networks for Synthetic Jamming Attack...

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International Conference on computer Science and engineering (UBMK)

作者： Burcu Sönmez Sarikaya Şerif Bahtiyar Department of Computer Engineering Cyber Security and Privacy Research Lab. SPFLab Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

Intrusion Detection Systems (IDS) have been significant for Unmanned Aerial Vehicles (UAVs) since high connectivity is essential for such vehicles. Recently, machine learning-based defense mechanisms have contributed to detecting intrusions. Data are crucial to better results with machine learning algorithms. However, security data is not freely distributed so there is a lack of security-related data about UAVs that can be processed with machine learning. In this research, we create synthetic security data for UAVs using Generative Adversarial Networks (GANs) that can be utilized to develop machine learning-based IDS systems. We propose employing Conditional Tabular Generative Adversarial Networks (CTGANs), Gaussian copulas, and Variational AutoEncoders (VAEs) to generate syn-thetic jamming attacks from the widely-used UAV attack dataset. We experimentally evaluated the performance of each model by using many metrics, such as Kullback-Leibler decomposition. Experimental results show that the distribution of CTGAN synthetic data is similar to the distribution of real data. Finally, we created a machine learning-based IDS and applied it to each dataset to validate the success rate of intrusion detection with newly generated synthetic data. It was observed that CTGAN-generated data decreased the IDS accuracy by approximately 25%, while Gaussian copula-generated data resulted in a 20% decrease, and VAE-generated data led to a 1% reduction, all relative to the accuracy achieved with real data. These findings suggest that synthetic data generation holds significant potential for enhancing the security frameworks of UAVs. The experimental results demonstrate that the CTGAN model is particularly effective in producing synthetic data that challenges traditional intrusion detection mechanisms, thereby contributing to the development of more robust IDS capabilities.

关键词： Machine learning algorithms Intrusion detection Distributed databases Machine learning Autonomous aerial vehicles Generative adversarial networks Vectors security Jamming Synthetic data

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Towards Task-Free Privacy-Preserving Data Collection

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China Communications 2022年第7期19卷 310-323页

作者： Zhibo Wang Wei Yuan Xiaoyi Pang Jingxin Li Huajie Shao School of Cyber Science and Engineering Wuhan University430072 China School of Cyber Science and Technology Zhejiang University310027 China State Key Laboratory of Integrated Services Networks Xidian University710071 China Department of Computer Science College of William and MaryWilliamsburgVA23186 USA

With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users do not want to *** privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local ***,they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined *** this paper,we propose a novel task-free privacy-preserving data collection method via adversarial representation learning,called TF-ARL,to protect private attributes specified by users while maintaining data utility for unknown downstream *** this end,we first propose a privacy adversarial learning mechanism(PAL)to protect private attributes by optimizing the feature extractor to maximize the adversary’s prediction uncertainty on private attributes,and then design a conditional decoding mechanism(ConDec)to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized *** the joint learning of PAL and ConDec,we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except *** experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.

关键词： IoT privacy protection data collection adversarial learning deep learning

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Secure Communication for MUM-T: A Blockchain and Lightweight Cryptography Framework

Secure Communication for MUM-T: A Blockchain and Lightweight...

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security of Information and Networks (SIN), International Conference on

作者： Halimcan Yaşar Şerif Bahtiyar Department of Computer Engineering Cyber Security and Privacy Research Lab SPFLab Istanbul Technical University Maslak Istanbul Türkiye

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

Manned-Unmanned Teaming (MUM-T) systems integrate manned aircraft and unmanned aerial vehicles (UAVs) to enhance mission effectiveness, allowing a single pilot to coordinate multiple UAVs for tasks like reconnaissance, communication, and targeting. However, the complexity and operational demands of MUM-T systems introduce significant security challenges, particularly for mission-critical data integrity and real-time communication. In this paper, we propose a new framework for adaptive blockchain cryptography that combines Proof of Authority (PoA)-based blockchain with XOR-based lightweight authentication. The blockchain component, with the manned aircraft that serves as the sole validator, ensures tamper-resistant logging of key mission data. Additionally, it supports accountability and traceability through an efficient PoA consensus algorithm. In parallel, XOR-based lightweight authentication secures control and telemetry signals with minimal computational overhead that enables low-latency and a real-time communication. Analyses results show that the proposed framework achieves a better transaction throughput with acceptable latency, which meets the stringent security and performance requirements of MUM-T operations. The proposed framework offers a scalable and resilient solution for secure communications in complex military environments.

关键词： Military computing Mission critical systems Authentication Reconnaissance Throughput Real-time systems Blockchains Telemetry Aircraft Low latency communication

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Multi-view Evidential Learning-based Medical Image Segmentation 39

Multi-view Evidential Learning-based Medical Image Segmentat...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Chao Shi, Yushu Wong, Waikeung Liu, Chengliang Wang, Wei Wang, Zhihua Wen, Jie School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China School of Fashion and Textiles Hong Kong Polytechnic University Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)157735897X

Medical image segmentation provides useful information about the shape and size of organs, which is beneficial for improving diagnosis, analysis, and treatment. Despite traditional deep learning-based models can extract domain-specific knowledge, they face a generalization bottleneck due to the limited embedded knowledge scope. Vision foundation models have been demonstrated to be effective in extracting generalizable knowledge, but they cannot extract domain-specific knowledge without fine-tuning. In this work, we propose a novel multi-view evidential learning-based framework, which can extract both domain-specific and generalizable knowledge from multi-view features by combining the advantages of traditional and vision foundation models. Specifically, a novel multi-view state space model (MV-SSM) is designed to extract task-related knowledge while removing redundant information within multi-view features. The proposed MV-SSM utilizes Mamba, a state space model, to model cross-view contextual dependencies between domain-specific and generalizable features. Additionally, evidential learning is adopted to quantify the segmentation uncertainty of the model for boundary. In special, variational Dirichlet is introduced to characterize the distribution of the result probabilities, parameterized with collected evidence to quantify uncertainty. As a result, the model can reduce the segmentation uncertainties of boundaries by optimizing the parameters of the Dirichlet distribution. Experimental results on three datasets show that our method obtains superior segmentation performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Leveraging AI Diffusion and LoRA for Dynamic 3D Room Design and Visualization

Leveraging AI Diffusion and LoRA for Dynamic 3D Room Design ...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Rajat Tandel Stewart Kirubakaran S Santhiya P Ebenezer V Roshni Thanka M Bijolin Edwin Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Sciences and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

This paper focuses on the challenges faced in the 3D modeling. In this paper, an innovative 3D construction modeling tool using an AI-based diffusion and Low-Rank Adaptation (LoRA) model for producing realistic 3D room models is discussed. The approach hopes to develop contrasts with other 3D modeling tools where a lot of time must be spent by the user/drafter, and intensive specialized knowledge is needed; on the contrary, for our tool textual descriptions, sketches, or mood boards as input generate photorealistic image renderings of the considered room. In this way, through integrating the advantages of diffusion models for the first initial room layout generation and LoRA models for the userled adjusting, the tool can provide lower threshold for non-professionals and meanwhile guarantees the high-quality design. This work shows that there is an opportunity to open up access to 3D modeling for more ordinary people. It proves a considerable result compared with the existing works.

关键词： Solid modeling Visualization Adaptation models Technological innovation Three-dimensional displays Computational modeling User interfaces Diffusion models Artificial intelligence Tuning

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