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Joint Covert and Secure Communication for SWIPT Assisted CNOMA Systems

IEEE internet of Things Journal 2025年

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

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LATTE: A Real-time Lightweight Attention-based Traffic Accident Anticipation Engine

arXiv

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arXiv 2025年

作者： Zhang, Jiaxun Guan, Yanchen Wang, Chengyue Liao, Haicheng Zhang, Guohui Li, Zhenning State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental Engineering University of Macau China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Civil Environmental and Construction Engineering University of Hawaii at Manoa HonoluluHI United States State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau China

Accurately predicting traffic accidents in real-time is a critical challenge in autonomous driving, particularly in resource-constrained environments. Existing solutions often suffer from high computational overhead or fail to adequately address the uncertainty of evolving traffic scenarios. This paper introduces LATTE, a Lightweight Attention-based Traffic Accident Anticipation Engine, which integrates computational efficiency with state-of-the-art performance. LATTE employs Efficient Multiscale Spatial Aggregation (EMSA) to capture spatial features across scales, Memory Attention Aggregation (MAA) to enhance temporal modeling, and Auxiliary Self-Attention Aggregation (AAA) to extract latent dependencies over extended sequences. Additionally, LATTE incorporates the Flamingo Alert-Assisted System (FAA), leveraging a vision-language model to provide real-time, cognitively accessible verbal hazard alerts, improving passenger situational awareness. Evaluations on benchmark datasets (DAD, CCD, A3D) demonstrate LATTE’s superior predictive capabilities and computational efficiency. LATTE achieves state-of-the-art 89.74% Average Precision (AP) on DAD benchmark, with 5.4% higher mean Time-To-Accident (mTTA) than the second-best model, and maintains competitive mTTA at a Recall of 80% (TTA@R80) (4.04s) while demonstrating robust accident anticipation across diverse driving conditions. Its lightweight design delivers a 93.14% reduction in floating-point operations (FLOPs) and a 31.58% decrease in parameter count (Params), enabling real-time operation on resource-limited hardware without compromising performance. Ablation studies confirm the effectiveness of LATTE’s architectural components, while visualizations and failure case analyses highlight its practical applicability and areas for enhancement. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Performance Enhancement and Secrecy Analysis of Hybrid RF/FSO Communication Links Under Combined κ-μ Fading and Exponential Turbulence Conditions

Performance Enhancement and Secrecy Analysis of Hybrid RF/FS...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Deepak Upadhyay Prakhar Prakhar Rashmy Moray Rashmi Sharma Nookala Venu Shipra Shukla Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Roorkee Institute of Technology Pune India Symbiosis Institute of Management Studies (SIMS) A Constituent of Symbiosis International Deemed University Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India Dept. of CSE (Data Science) G. L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In this study, the secrecy performance evaluation of hybrid RF/FSO communication systems over κ-μ fading and exponential turbulence channels are conducted. We analyse the secure information rate and evaluate the throughput and secrecy capacity of the system through simulations. The results indicate that the proposed hybrid system improves reliability and security by combining adaptive modulation, beamforming, and encryption to combat interference and secure data transmission. Geographical heat maps help to visualize the influence of environmental factors on system performance. Federated learning in hybrid RF/FSO systems have been investigated in detail through simulations and numerical results to show the considerable improvements in communication reliability and performance that the increased redundancy and robustness of the hybrid scheme provides. This helps gain valuable insights into a better design of wireless networks in the future by highlighting the benefits of hybrid communication techniques and simulation-driven design to enhance performance and security across diverse complex environments and conditions.

关键词： Fading channels Analytical models Adaptation models Array signal processing Atmospheric modeling Computational modeling Modulation Throughput Encryption Security

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The Role of Blockchain Technology in Enhancing Network Security

The Role of Blockchain Technology in Enhancing Network Secur...

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Automation and Computation (AUTOCOM), International Conference on

作者： Lovish Dhingra Loitongbam Sanayai Meetei Varun Ojha M K Lodi A.C Santha Sheela Ankita Agarwal Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Centre for Internet of Things Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Adjunct Faculty School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Blockchain technology has received much attention for its digital storage and security applications. Blockchain technology has many benefits regarding network security, including decentralization, immutability and transparency, which can significantly increase the level of protection for networks. Decentralization is one of the key advantages that blockchain can bring to network security. Conventional network security systems have control of data, which is all centralized in front of a single entity, making them easily attacked. On the other hand, blockchain technology decentralizes data control and therefore, the distribution of potential hacking in nodes makes it almost impossible to hack or breach the entire network. Specifically, the blockchain is immutable; its construction protects against any data modification occurring on the network when no vote by a majority of nodes permits a change. It renders cybercriminals nearly incapable of modifying the network data, which adds an extra layer of protection. Transparency is another key feature of blockchain technology since all participants in the network will have access to view any transaction conducted on the network. This functionality helps monitor any unauthorized or suspicious activity on the network, providing a chance to be vigilant and address the issue.

关键词： computer hacking Smart contracts Organizations Network security Blockchains Protection Cyberattack Monitoring Identity management systems Immune system

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Associations of occupation categories with cardiovascular diseases and all-cause mortality: an analysis of NHANES 2005-2014

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BMC Public Health 2025年第1期25卷 1-10页

作者： Chi, Haiyang Zhou, Jia Li, Chao Lu, Yuhuan Xie, Can He, Baoyu Ke, Wei School of Applied Sciences Macao Polytechnic University 999078 Macao Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University 999078 Macao Chongqing Yubei Maternal and Child Health Hospital Chongqing 40120 China Guang Ming District Center for Disease Control and Prevention Guangdong Province Shenzhen 518106 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau 999078 Macao

Background: Evidence on the effects of occupation categories on cardiovascular disease (CVD) progression and longevity has mostly come from cross-sectional studies, which limits our understanding of the pathogenesis of CVD. This study aimed to evaluate the associations of occupation categories with CVD and all-cause mortality in the American population. Methods: We analyzed data from the 2005–2014 U.S. National Health and Nutrition Examination Survey (NHANES), a large-scale public health survey representative of the U.S. population, linked to mortality data obtained in 2019. To evaluate the association between occupation categories and the risks of cardiovascular disease (CVD) and all-cause mortality, we estimated Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs). Results: Among 11,102 participants (aged 20–85 years, 53.99% male), 132 died from CVD among the total 478 deaths during a median follow-up of 9.9 years. In multivariable-adjusted models, the occupation categories were significantly associated with CVD mortality, with the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of 3.95(1.94–8.04) for construction and clean-up industry, 2.51(1.15–5.52) for sales and service industry, 2.49(1.04–5.95) for business and management, and 2.98(1.56,5.71) for others. For all-cause mortality, only construction and clean-up industry and sales and service industry were positively associated with all-cause mortality, and HRs (95% CIs) were 2.05(1.33–3.16) and 1.64(1.12–2.41). Both working hours in the previous week and the number of months worked exhibited varying degrees of dose-response relationships with CVD and all-cause mortality. Conclusions: Occupation categories were found to be significantly associated with the risk of CVD and all-cause mortality. Future research could incorporate different work properties into specific prevention strategies for these outcomes. © The Author(s) 2025.

关键词： All-cause mortality Cardiovascular mortality Cohort study Occupation categories

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An efficient hybrid system for anomaly detection in social networks

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Cybersecurity 2021年第1期4卷 136-146页

作者： Md.Shafiur Rahman Sajal Halder MdAshraf Uddin Uzzal Kumar Acharjee Department of Computer Science and Engineering Dhaka International UniversityDhakaBangladesh Department of Computer Science and Engineering Jagannath UniversityDhakaBangladesh Department Computer Science and Information Technology RMIT UniversityMelbourneAustralia Internet Commerce Security Laboratory Federation University AustraliaBallarat VIC 3350Australia

Anomaly detection has been an essential and dynamic research area in the data mining.A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and *** social network refers to a forum used by different groups of people to express their thoughts,communicate with each other,and share the content *** social networks also facilitate abnormal activities,spread fake news,rumours,misinformation,unsolicited messages,and propaganda post malicious ***,detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social *** this paper,we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree(C5.0),Support Vector Machine(SVM)and Naïve Bayesian classifier(NBC)for classifying normal and abnormal users in social *** have extracted a list of unique features derived from users’profile and *** two kinds of dataset with the selected features,the proposed machine learning model called DT-SVMNB is *** model classifies users as depressed one or suicidal one in the social *** have conducted an experiment of our model using synthetic and real datasets from social *** performance analysis demonstrates around 98%accuracy which proves the effectiveness and efficiency of our proposed system.

关键词： Anomaly Detection Machine Learning Hybrid Anomaly Detection Social Networks

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Utilizing the dropped packets for data delivery in VANETs

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The Journal of China Universities of Posts and Telecommunications 2013年第3期20卷 48-52页

作者： SONG Chao LIU Ming GONG Hai-gang CHEN Gui-hai CAO Jian-nong School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering Shanghai Jiao Tong University Internet and Mobile Computing Laboratory Department of Computing Hong Kong Polytechnic University

Timely and cost-efficient multi-hop data delivery among vehicles is essential for vehicular ad-hoc networks （VANETs）, and various routing protocols are envisioned for infrastructure-less vehicle-to-vehicle （V2V） communications. Generally, when a packet （or a duplicate） is delivered out of the routing path, it will be dropped. However, we observe that these packets （or duplicates） may also be delivered much faster than the packets delivered along the original routing path. In this paper, we propose a novel tree based routing scheme （TBRS） for ultilizing the dropped packets in VANETs. In TBRS, the packet is delivered along a routing tree with the destination as its root. And when the packet is delivered out its routing tree, it won＇t be dropt immediately and will be delivered for a while if it can arrive at another branch of the tree. We conduct the extensive simulations to evaluate the performance of TBRS based on the road map of a real city collected from Google Earth. The simulation results show that TBRS can outperform the existing protocols, especially when the network resources are limited.

关键词： routing vanet opportunistic forwarding

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Optimizing Deep Learning Inference to Detect PCB Soldering Defects 4

Optimizing Deep Learning Inference to Detect PCB Soldering D...

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4th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2022

作者： Tham, Mau-Luen Chong, Boon Yoong Tan, Yu Han Wong, Yen Khai Chean, Swee Ling Tan, Wei Kun Department of Electrical and Electronic Engineering Malaysia Department of Internet Engineering and Computer Science Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Rapid and accurate defect detection in printed circuit board (PCB) manufacturing plays a vital role in the quality control (QC) of consumer electronic products. Automated visual inspection is gaining increased popularity owing to low production cost and high portability. Prior works rely on a combination of image subtraction and processing techniques whereas the recent works utilize the generalization capability of deep learning. The former operates poorly in a mismatch condition of image pairs whereas the latter consumes higher computation time and processing power. In this paper, we propose a YOLOv4-based PCB detection framework which detects six types of defects. To attain production-ready capability, we optimize the models using the Intel OpenVINO toolkit. Experiment results reveal that the optimized models yield good accuracy for defect detection with significant execution speed on low-power embedded platform. © 2022 IEEE.

关键词： Printed circuit boards

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Applying Deep Learning to Individual and Community Health Monitoring Data:A Survey

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International Journal of Automation and computing 2018年第6期15卷 643-655页

作者： Zhen-Jie Yao Jie Bi Yi-Xin Chen Beijing Advanced Innovation Center for Future Internet Technology Beijing University of Technology Department of Computer Science and Engineering Washington University in St.Louis

In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records （EHR）, electrocardiography （ECG）, electroencephalogram （EEG）, community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.

关键词： Deep learning healthcare electronic health records (EHR) neural networks survey.

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A Multiple Energy Conversion Channels Fusion Grid-Connected Inverter for Large-Scale PV Active Power Injection Preventing Power Loss Caused by Insufficient MPP PV Voltage

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IEEE Transactions on Power Electronics 2025年

作者： Pang, Ying Yang, Hao Ji, Yanju Zhang, Xinhao Zhu, Yongji Li, Gang Xiang, Zeng Gong, Cheng Qiu, Shilin Zhou, Haigen Wong, Man-Chung Lin, Jun Jilin University State Key Laboratory of Deep Earth Exploration and Imaging College of Instrumentation and Electrical Engineering Changchun130026 China Jilin University College of Instrumentation and Electrical Engineering Changchun130021 China Jilin University Department of Laboratory Animals College of Animal Sciences Changchun China Tongda Electromagnetic Energy Co. Ltd. Changsha410000 China University of Macau State Key Laboratory of Analog and Mixed-Signal VLSI Macao999078 China University of Macau Institute of Microelectronics Macao999078 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Macao999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Macao999078 China

In the context of the increasing global demand for renewable energy and the rapid development of large-scale photovoltaic (PV) power generation, efficient grid connection of PV energy has become crucial. However, existing gridconnected inverters (GCI) face issues such as high-power losses issue of inductive GCI (IGCI) or limited operating ranges issue of capacitive GCI (CGCI). This paper introduces a multiple energy conversion channels fusion GCI (MC-FGCI) for largescale PV power injection. First, MC-FGCI is proposed based on a new topology circuit configuration. Second, an equivalent circuit model of IGCI and CGCI is introduced to calculate the critical point for channel switching. The IGCI and CGCI efficient operating boundaries are determined based on the critical point. Third, based on the efficient operating boundaries, a finite state machine (FSM) based method is proposed to choose the optimal VSI for high-efficiency grid connection. Finally, experimental results show that compared to previous multiple parallel IGCI (MP-IGCI), the proposed MC-FGCI saves around 11% power loss by eliminating the lossy DC/DC link. Compared to MP-CGCI, the proposed MC-FGCI expands the active power injection range under low maximum power point (MPP) voltage. © 1986-2012 IEEE.

关键词： Equivalent circuits

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