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Reflection Optimization for Covert Ambient Backscatter Systems Under Two Jamming Patterns

IEEE Internet of Things Journal 2025年

作者： Xie, Yuanai Wen, Yaoyao Zhang, Xiao Lai, Pan Liu, Zhixin Pan, Haoyuan Chan, Tse-Tin South-Central Minzu University School of Computer Science Wuhan430074 China Yanshan University School of Electrical Engineering Qinhuangdao066004 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

Ambient backscatter communication (ABC) enables low-cost and energy-efficient connectivity for Internet of Things (IoT) devices by leveraging ambient radio-frequency (RF) signals. However, the passive nature and open wireless medium of ABC systems make them vulnerable to detection by unauthorized receivers (wardens). To mitigate this risk, covert communication, which conceals transmissions by embedding them within noise, offers a promising security enhancement for ABC systems. This paper proposes a jammer-assisted reflection coefficient optimization framework to enhance the covertness and reliability of ABC systems with an endogenous warden and an external jammer. Specifically, we consider two distinct jamming patterns: uniformly distributed and truncated exponentially distributed artificial noise power. We derive closed-form expressions for both the outage probability of the backscatter link and the minimum detection error rate at the warden under these jamming patterns. Based on these expressions, we determine the optimal reflection coefficients that maximize the effective covert rate while satisfying a predefined covertness constraint. Additionally, we introduce the concept of jamming cost to evaluate the efficiency and applicability of different jamming patterns in terms of the required jamming power to achieve a desired level of covertness. Numerical results validate the effectiveness of the proposed optimization framework and reveal that while uniform jamming provides stronger covertness and lower jamming cost, truncated exponential jamming achieves a lower outage probability. These findings provide key insights for designing secure and efficient ABC systems across diverse IoT deployment scenarios. © 2014 IEEE.

关键词： Ambient backscatter communication artificial noise covert communication covert rate jamming patterns reflection coefficient optimization

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VFGCN: A Vertical Federated Learning Framework With Privacy Preserving for Graph Convolutional Network

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Gang Li, Qingming Liu, Ximeng Yan, Xiaoran Dong, Qingkuan Wu, Huiwen Kong, Xiangjie Zhou, Li Zhejiang Lab HangZhou 310000 China Zhejiang University College of Computer Science and Technology Hangzhou 310058 China Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Big Data Fuzhou 350108 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an 710071 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou 310023 China Dalian University of Technology School of Software Dalian 116620 China

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information;however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional Network (VFGCN) surpasses existing privacy-preserving methodologies. © 2004-2012 IEEE.

关键词： Federated learning GCN homomorphic encryption privacy-preserving

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Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches

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Procedia computer science 2025年 252卷 53-62页

作者： Milind Shah Kajal Parmar Priyanka Padhiyar Naina Parmar Monali Parikh Dhruvansh Patni Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa 388421 India Assistant Professor Department of Information Technology Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

关键词： Real Time Networking Multimedia Challenges Innovations Intelligent Networking

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Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Microgrid Control 3

Deep Learning-Based Forecasting of Solar Photovoltaic Genera...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, K. Ankith Garg, Mohan Shah, Manita D. Ahmed, Kalyandurg Rafeeq Agarwal, Trapty Saraswat, Vivek Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control is a new approach to accurately predicting solar photovoltaic generation in micro grids. These feature renewable energy such as Solar PV and are a responsive approach for providing efficient, sustainable power solutions. In contrast, solar PV power production is not dispatch able because it depends on the changing weather and other factors. The new method applies deep learning algorithms, mainly artificial neural networks, to historical data to accurately forecast solar PV output. The latter sorts the variation due to a broad array of meteorological and operational parameters, including temperature, humidity level, cloud cover, and shading Moreover, the model can forecast results online and keep learning with new data for real-time micro grid control. The deep learning-based method has shown superior performance to traditional forecasting methods in improving the precision of PV generation forecasts. Benefits include more efficient energy management, better integrating renewable sources into the micro grid, and lower costs. This means that the Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control could significantly improve the reliability and efficiency of micro grid operations and promote a more sustainable and resilient energy system. © 2025 IEEE.

关键词： Marketing

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Intelligent Energy Management System for Microgrids with Energy Storage Integration 3

Intelligent Energy Management System for Microgrids with Ene...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nanda, Simranjeet Kumar, Maddu Naveen Baxi, Paramjit John, Monoo Manaswini, R. Yadav, Dhananjay Kumar Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

The items are a software solution, it is designed for micro grids with energy storage integration to monitor, control, and best utilize available grid resources. Also called a mini-grid, the micro grid is a distributed energy system consisting of local small-scale generation and storage facilities that could operate independently from the main grid in an emergency. Micro grids are frequently paired with solar panels and wind turbines, making them more sustainable. The capacity of energy storage systems in micro grids has been playing an important role, as renewable energy in devices with temporal availability generates the need for their more efficient use. Based on real-time data, the items apply energy transmission and distribution management algorithms to control where the power is used in a micro grid. The items continuously monitor the demand and supply of energy to investigate the ideal combination of renewable generation and stored energy for peak shaving. In doing so, the micro grid can run optimally and minimize energy waste without running up bills. The items can also predict how and when individuals use energy, thus allowing better scheduling of the available resources. This enables the items to conduct predictive maintenance on those micro grid components to long and function as efficiently as possible. © 2025 IEEE.

关键词： Marketing

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Bi-Linear Alternative Least Squares and Maximum Likelihood based MIMO Communication System

Bi-Linear Alternative Least Squares and Maximum Likelihood b...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Laith Hussein Jasim Manjunatha B N Sahithi Godavarthi Thamizhkani. B C. Sushama Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of CSE (Artificial Intelligence and Machine Learning) R L Jalappa Institute of Technology Doddaballapur India Department of Computer Science and Engineering (CS) CVR College of Engineering Hyderabad India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In the recent years, the field of wireless communication networks have changed dramatically due to Multiple-Input Multiple-Output (MIMO) radar and communication system. MIMO system uses multiple antennas at both ends, transmitter and receiver. The deployment of huge MIMO technology shows enormous progresses in communication systems with extraordinary improvements in energy and spectral efficiencies. Even though, signal processing and channel estimation become challenging tasks in massive MIMO system. To overcome these challenges in this research proposed Orthogonal Matching Pursuit (OMP) with Bi-Linear Alternative Least Squares (BALS) method for channel estimation and Maximum Likelihood (ML) for signal detection. Initially, Channel State information (CSI) is considered as input data. Then, channel estimation is performed through OMP with BALS and signal detection is performed using ML. From the results, the proposed BALS model attained better outcomes compared to existing OMP-based Linear Minimum Mean Square Error (LMMSE) in terms of Signal-to-Noise Ratio (SNR) by achieving -10 decibels (dB) correspondingly.

关键词： Maximum likelihood estimation Maximum likelihood detection Spectral efficiency Transmitters Channel estimation Matching pursuit algorithms Massive MIMO Signal detection Channel state information Signal to noise ratio

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Analysis of Convolutional Neural Network Architectures for Image Denoising and Restoration 3

Analysis of Convolutional Neural Network Architectures for I...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Mukul Gupta, Rahul Frank, Anilloy Augustine Agarwal, Trapty Mahajan, Vibhor Priyadarshini, Sushree B. B. Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Convolutional Neural Networks have performed excellently in image processing applications such as Image Demising and Restoration. CNN's performance and accuracy depend on its design and architecture. This work analyzes delousing and image restoration using different CNN architectures. In this experiment, three popular CNN architectures, U-Net, Res Net, and Dense Net, are trained on different noise levels. In this section, we analyze each parameter for the proposed CNN models, i.e., filter size and depth, etc., to evaluate performance variations due to these hyper parameters. Our results indicate that with a longer network and large filter sizes, it may be possible to deny the images more accurately for restoration. This development set opened as we experimented with fine-tuning the other tasks (DE noising and restoration) using pretrained models. We provide empirical evidence that has shown that incorporating pretrained models can result in a substantial improvement in the performance of CNNs with a limited amount of data. This work represents a complete investigation of CNN architectures, such as DE noising and restoration, and explains why specific engineering choices are effective. These results can be useful in enhancing accuracy and performance for image processing tasks using CNN models. © 2025 IEEE.

关键词： Internet

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Hybrid Deep Learning Methods for Enhancing Parkinson's Disease Early Detection

Hybrid Deep Learning Methods for Enhancing Parkinson's Disea...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S. Vijaya Amala Devi K. Vijayalakshmi R. Santhana Krishnan J. Relin Francis Raj R. Umesh N. Soundiraraj Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor function. Early detection is critical for effective intervention, yet traditional diagnostic methods often face limitations in accuracy and timeliness. This paper proposes a hybrid deep learning model combining EfficientNet, BiLSTM, and GANs to improve PD detection accuracy using medical images and sequential data. EfficientNet is employed for extracting features from MRI scans, while BiLSTM is used to analyze temporal data, such as gait and speech patterns, to capture the progressive changes associated with PD. Additionally, GANs are utilized to augment the dataset, addressing the challenges of data scarcity and imbalance. The fusion of spatial and temporal features enables a more comprehensive understanding of PD, improving diagnostic performance. Bayesian optimization is applied for hyperparameter tuning, and K-fold cross-validation ensures robust model evaluation. The results demonstrate that the hybrid model outperforms single-modal approaches in both classification and regression tasks, offering enhanced accuracy in detecting PD and predicting disease severity. Furthermore, interpretability techniques like Grad-CAM and attention mechanisms are incorporated to provide insights into the model's decision-making process. This work presents a promising approach for automating PD diagnosis and lays the groundwork for further research in multi-modal deep learning for disease detection.

关键词： Deep learning Accuracy Parkinson's disease Bidirectional long short term memory Predictive models Feature extraction Data augmentation Data models Optimization Medical diagnostic imaging

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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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Stock Market Growth and Decline Using ARIMA Model and Sentiment Analysis 3

Stock Market Growth and Decline Using ARIMA Model and Sentim...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Biswas, Sandipan Roy, Bhaskar Singh, Sujit Kumar Nitin, Nitin Sahoo, Saroj Kumar Sarangi, Sanjaya Kumar Brainware University Dept. of CSE-AI Kolkata India Asansol Engineering College Dept. of CSE(AIML) Asansol India Parul University Faculty of Information Technology and Computer Science Vadodara India Akhilesh Das Gupta Institute of Professional Studies Department of CSE Delhi India Centurion University of Technology and Management Department of CSE Bhubaneswar India Utkal University Dept. of Computer Science Bhubaneswar India

ISBN: (纸本)9798331523893

Researchers have been developing and entering the technological era since the 18th century. Before everyone else, social networks brought people from distant nations together. Social network applications such as Reddit, Twitter, Facebook, Instagram, LinkedIn, and others are examples of associate degree programs. Each application has a unique purpose. Investors who want to buy stocks take use of this social networking information. They stay up with a few companies thanks to this information. In order to assess people's sentiments regarding a certain stock, we will be using stock-related news data in this instance. Using TEXTBLOB sentiment analysis, this analysis will provide feedback on the corporate so that we may be prepared to see a rise or fall in relation to investors or firm performance. © 2025 IEEE.

关键词： ARIMA sentiment analysis Stock Market TEXTBLOB Time Series

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