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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation Systems

IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation Systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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The Influence of Kanban Agile Methodology on Software Project Management: A Survey Method 10

The Influence of Kanban Agile Methodology on Software Projec...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ahmed, Irfan Munir, Fatima Nafees, Fouzia Mahmood, Jabar Chaudhry, Shehzad Ashraf Javaid, Maryam Batool, Khadija University of Sialkot Dept. of Computer Science Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain Hangzhou China College of Engineering Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates International Institute of Science Arts and Technology Dept. of Computer Sciences Gujranwala Pakistan

ISBN: (纸本)9798331532895

Software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods. © 2024 IEEE.

关键词： Agile DSDM Kanban Quality Enhancement Scrum SPM XP

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User-Friendly Software System for Geopolymer Concrete Slump and Strength Prediction 30

User-Friendly Software System for Geopolymer Concrete Slump ...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Al-Mattarneh, Hashem Rawashdeh, Adnan Ismail, Rabah Matalkah, Faris Alhussien, Nour Malkawi, Ahmad Yarmouk University Civil Engineering Department Irbid Jordan Yarmouk University Department of Information Technology Irbid Jordan Jadara University Civil Engineering Department Irbid Jordan School of Computer and Cyber Sciences Augusta University AugustaGA United States Al-Balqa Applied University Civil Engineering Department Amman Jordan

ISBN: (纸本)9798350356694

Geopolymer concrete is a complex composite material and its properties such as workability and strength are incredibly unpredictable. A current unresolved issue is the lack of finding a standardized method for predicting the properties of geopolymer binders based on the ratios of mixing. Also, employing traditional testing techniques is expensive and time-consuming. Consequently, the objective herein is to resolve such issue. This paper describes the establishment of user-friendly software system that uses response surface methodology and central composite design to predict the workability and strength of geopolymer concrete. The software has an easy-to-use interface and was created in the MATLAB environment. Data about the mix proportion of geopolymer concrete blend and its properties, like workability and strength, could be imported into the software from an Excel file. The software developed linear and nonlinear empirical regression models to relate the constituents of the geopolymer concrete using response surface methodology. In addition, the system uses residual, mean square error, F-test, significant, and square correlation coefficient as quality fit parameters to identify the optimal model. Furthermore, the system offers the user a module for forecasting the performance of a specified mixed proportion. Thirty-one geopolymer mixes were prepared and used to calibrate the software. Another four mixes were used to validate the computation of the system. The principles of graphical user interface (GUI) design have been applied herein to produce a user-friendly software. These principles include balanced layout, grouping of related data, alignment, justification, highlighting as appropriate, and navigation control. © 2024 IEEE.

关键词： Expert systems

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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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Indoor 3D Visible Light Positioning Analysis with Channel Estimation Errors 30

Indoor 3D Visible Light Positioning Analysis with Channel Es...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Bozanis, Dimitrios Evgenidis, Nikos G. Papanikolaou, Vasilis K. Bouzinis, Pavlos S. Tegos, Sotiris A. Dowhuszko, Alexis A. Diamantoulakis, Panagiotis D. Karagiannidis, George K. Department of Electrical and Computer Engineering Thessaloniki54124 Greece Germany University of Macedonia Department of Applied Informatics Thessaloniki54636 Greece Aalto University Department of Communications and Networking Espoo02150 Finland Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350337297

Since radio-frequency signals are affected signifi-cantly by multipath fading, the localization accuracy they offer cannot meet the sixth generations (6G) demanding specifications. On the other hand, visible light positioning (VLP) has emerged as a low cost alternative that takes into account the highly deterministic channel characteristics of visible light communications to provide cm-level accuracy provided that the investigated spaces are adequately covered by LEDs. However, imperfect channel state information and modeling can lead to positioning errors. In this paper, localization accuracy is quantified in terms of channel estimation errors via low complexity lateration algorithm. The channel estimation errors can be caused by a variety of issues and the provided analysis is general so that it can be easily applied to a plethora of applications. Finally, Monte Carlo simulations validate the proposed method and offer valuable insights for the design and performance bounds of VLP systems. © 2023 IEEE.

关键词： Monte Carlo methods

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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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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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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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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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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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