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Deep Reinforcement Learning-based Physical Layer Security Framework for Internet of Medical Things

IEEE Transactions on Consumer Electronics 2024年

作者： Razaq, Mian Muaz Jiao, Yan Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, Fangjie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China Department of Electrical and Computer Engineering University of Waterloo ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

The Internet of Medical Things (IoMT) is transforming modern healthcare information systems by connecting a diverse array of medical devices and sensors. However, significant security and privacy challenges arise when handling confidential medical data during transmission. This paper addresses these challenges by proposing a Physical Layer Security (PLS) framework integrated with Cell-Free Massive Multiple Input Multiple Output (CF-mMIMO) to enhance security in IoMT environments. The framework introduces a safe zone (SZ), a protected area surrounding legitimate healthcare devices to prevent access by eavesdroppers. This spatial segmentation enables precise beamforming within the SZ while amplifying artificial noise (AN) outside it, significantly boosting the secrecy rate. Additionally, the framework dynamically selects communication devices based on channel quality and orthogonality, optimizing network resources, reducing inter-user interference, and ensuring high-quality communication in densely deployed healthcare settings. Simulation results confirm that our approach adapts to and leverages the spatial dynamics of eavesdroppers, maintaining high secrecy rates even in scenarios with increased eavesdropper presence, thus keeping sensitive medical data secure and unreadable to unauthorized entities. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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RDVC: Efficient Deep Video Compression with Regulable Rate and Complexity Optimization

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IEEE Transactions on Multimedia 2025年

作者： Wei, Xiaojie Lin, Jielian Xu, Jiawei Gao, Wei Zhao, Tiesong Putian University School of Mechanical Electrical & Information Engineering PutianFujian China Shenzhen Graduate School Peking University School of Electronic and Computer Engineering Shenzhen518055 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Deep video coding has paved a way to break through the performance bottleneck of reigning hybrid video coding. However, unlike hybrid video codecs, existing deep video codecs cannot offer both flexible rates and regulable complexities within one single codec, which limits their applications. In this paper, we propose a Regulable Deep Video Codec (RDVC) to address the above issue. First, we propose an Adaptive Feature Compression (AFC) network that generates variable rates while ensuring Rate-Distortion (RD) performance. The network introduces a two-stage coarse-to-fine rate adjustment that can be controlled by a user-specified rate level. Second, we propose a Spatio-Temporal Feature Propagation (STFP) mechanism to provide high-quality reference information for AFC process. Third, we also utilize slimmable convolutional components in our framework to adjust decoding complexity constrained by user configuration. Experimental results demonstrate that RDVC can adjust the codec structure flexibly according to different user configurations while maintaining advanced performance. On average, it reduces the bit-per-pixel (bpp) by 9.35%/58.12% while maintaining the same PSNR/MS-SSIM as the reference software VTM-13.2. Sourcecode is available at: https://***/WXJ0001/RDVC. © 2025 IEEE.

关键词： Signal distortion

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Knowledge Graph Completion with Low-Dimensional Gated Hierarchical Hyperbolic Embedding

SSRN

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SSRN 2024年

作者： Fang, Yan Liu, Xiaodong Lu, Wei Pedrycz, Witold Lang, Qi Yang, Jianhua School of Control Science and Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

Knowledge Graph Embedding aims to advance the learning performance of knowledge graphs by deliberating the complexity of hierarchical data structures in a finite-dimensional space. Most traditional Euclidean models struggle to accurately describe and navigate the intricate hierarchical relationships between nodes. The hyperbolic embedding methods, particularly in conjunction with graph neural networks, offer promise in effectively modeling complex hierarchies in a limited-dimensional space. These methods can accommodate more information about nodes under the same conditions but are also more susceptible to interference from complex neighborhood features. This paper introduces HyperGatE, which novelty combines a hyperbolic graph hierarchical neighbor structure based on a gated filter and a hyperbolic isometric rotation model for knowledge graph completion. By analyzing the hierarchical structure of the knowledge base and using a gated mechanism to form multi-layer embedding networks of entity-relational paths, the gated filter selects diverse uneven multi-hop relations within the entity neighborhood and measures them through graph attention networks. Furthermore, the simplified hyperbolic isometric model allows for uncovering hierarchical structures in the data. HyperGatE experimentally surpasses the state-of-the-art Euclidean and hyperbolic space models in high and low dimensions across four well-known data, namely WN18RR, FB15k-237, NELL-995, and Kinship. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Texture-based Feature Extraction and Machine Learning Model for the Detection of Acute Lymphoblastic Leukemia

Texture-based Feature Extraction and Machine Learning Model ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Rangini M Sumit Pundir Manzoore Elahi M. Soudagar Daxa Vekariya Harshal Patil S. Rajkumar Assistant Professor Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

Acute Lymphocytic Leukaemia (ALL) is a dangerous malignancy that not only attacks adults but also contributes to roughly 25% of children. A precise and on-time diagnosis of cancer is a critical prerequisite for effective therapy to enhance survival rates. It is challenging to identify cancer cells from normal cells because leukemic cells seem extremely identical to normal cells beneath the microscope. Normally, morphological analysis for the detection of ALL is conducted physically on blood cells by qualified medical individuals, which has various drawbacks, like a lack of medical experts, slow analysis, and prognosis which depends on the knowledge of medical professionals. For this reason, we provide a robust method for improving the reliability of ALL identification using feature extraction and a Machine Learning (ML) model to discriminate between cancerous and healthy cells in images. To extract texture features for analysis, we employ the First Order Histogram (FOH) and the Gray-Level Co-occurrence Matrix (GLCM) in our technique. Logistic Regression (LR), Naive Bayes (NB), and Artificial Neural Network (ANN) are used in the categorizing method. The simulation results demonstrate that combining GLCM feature extraction with the ANN model yields the highest accuracy, correctly predicting 595 out of 600 data samples. The accuracy was 99.17%, which is quite remarkable.

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Digital Twin Model of Rhizophora apiculata Based on Salinity Factors

Digital Twin Model of Rhizophora apiculata Based on Salinity...

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Ying Li Xun Luo Gustavo Alomía Andrea Pilco School of Computer Science and Engineering Tianjin University of Technology Tianjin China Department of Engineering Universidad Santiago de Cali Colombia Faculty of Technical Sciences International University of Ecuador UIDE Quito Ecuador

ISBN: (数字)9798331515447

ISBN: (纸本)9798331515454

This study is based on the Attimis model of the canopy and the prop root morphology of the Rhizophora apiculata, delved into the substructures of canopy growth and complex root generation diagrams, and subsequently carried out L-system modeling. The growth process was aligned with the Logistics growth curve, and experimental data on the impact of salinity on Rhizophora apiculata growth were fitted with the parameters in the L-system productions. Ultimately, a digital twin model of Rhizophora apiculata based on salinity factors was developed. This research aims to uncover the mechanisms of how salinity changes affect Rhizophora apiculata growth, providing a scientific basis for the protection and management of mangroves.

关键词： Solid modeling Salinity (geophysical) Ecosystems Morphology Virtual reality Production Data models Digital twins Protection Logistics

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PRISE: Privacy-pReserving Image Searchable Encryption Scheme for Intelligent Vehicle Systems

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IEEE Internet of Things Journal 2025年

作者： Fu, Xuemei Yang, Laurence T. Song, Na Gao, Jinxiong Lu, Zhixing Hainan University School of Information and Communication Engineering Haikou570100 China Zhengzhou University School of Computer Science and Artificial Intelligence Zhengzhou450001 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Putian University School of Mechanical Electrical and Information Engineering Putian350001 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang43400 Malaysia

With the continuous advancement of intelligent vehicle technology, the image data generated by vehicles has become increasingly critical in various applications, including driver assistance, traffic monitoring, and safety warning systems. However, this growing reliance on image data also raises pressing concerns regarding its security and privacy protection. Searchable encryption technology, as an effective means to protect data security, shows significant potential for application in the field of intelligent vehicles. In this paper, we propose a novel Privacy-pReserving Image Searchable Encryption Scheme (PRISE) to address the security and privacy challenges associated with image data in intelligent vehicles. The PRISE scheme employs Multilinear Principal Component Analysis (MPCA) to extract data features and integrates symmetric encryption and matrix encryption techniques to ensure image privacy protection. To enhance search efficiency, we utilize a Mahalanobis distance-based fuzzy C-means (FCM) clustering method, which accelerates the search process on the cloud server. We conducted comprehensive experiments to evaluate our proposed scheme, and the results were consistent with our analytical findings, confirming the security of our approach. Comparative experiments with existing schemes demonstrated that our proposed method achieves higher accuracy while maintaining a comparable query time. © 2014 IEEE.

关键词： Sensitive data

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A Photonics-aided MMW OFDM Joint Radar and Communication System with Velocity Accuracy Improvement

A Photonics-aided MMW OFDM Joint Radar and Communication Sys...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Peng, Lanfeng Luo, Dongxiang Xiao, Yaoqiang Li, Fan School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou510275 China Institute of Semiconductors South China Normal University Guangzhou510631 China School of Chemistry and Chemical Engineering Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou510006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9781665461634

Photonics-aided millimeter wave (MMW) Orthogonal frequency division multiplexing (OFDM) system is a novel approach to realize the joint radar and communication system. The phase noise introduced by the laser will degrade the system performance. In this paper, we analyze the mechanism of phase noise acting on OFDM radar system. A method that combines chirp-Z transform (CZT) and compensation enabled by a reference signal is adopted to improve the velocity accuracy. RF pilot (RFP) and blind phase search (BPS) algorithm are adopted to improve communication performance. A simulation is demonstrated at 30 GHz with 2 GHz communication bandwidth. A twodimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 0.980 m/s and a communication capacity of 6.4 Gbps are obtained in the proposed joint radar and communication system. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

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Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

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IEEE Symposium on Systems and Information engineering Design, SIEDS

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Sagheer Abbas Areej Fatima Muhammad Saleem Muhammad Adnan Khan Abdulrahman Alqarafi Computer Science Department Centre for Cyber Physical Systems Khalifa University Abu Dhabi UAE Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia School of Computer Sciences National College of Business Administration & Economics Lahore Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Department of Computer Science Bahria University Lahore Pakistan Department of Computer Science LGU Lahore Pakistan School of Computer Science Minhaj University Lahore Pakistan School of Computing Skyline University College Sharjah UAE Riphah School of Computing and Innovation Riphah International University Lahore Pakistan College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner.

关键词： Energy consumption Costs Smart buildings Sociology Energy efficiency Energy management systems Surges

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An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures

An Achievable and Analytic Solution to Information Bottlenec...

引用

IEEE International Symposium on Information Theory

作者： Yi Song Kai Wan Zhenyu Liao Hao Xu Giuseppe Caire Shlomo Shamai Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Electronic and Electrical Engineering University College London London UK Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we consider a remote source coding problem with binary phase shift keying (BPSK) modulation sources, where observations are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs further compression to find the optimal trade-off between complexity and relevance. This problem can be formulated as an information bottleneck (IB) problem with Bernoulli sources and Gaussian mixture observations, for which no closed-form solution is known. To address this challenge, we propose a unified achievable scheme that employs three different compression strategies for intermediate node processing, i.e., two-level quantization, multi-level deterministic quantization, and soft quantization with tanh function. Comparative analyses with existing methods, such as the Blahut-Arimoto (BA) algorithm and the Information Dropout approach, are performed through numerical evaluations. The proposed analytic scheme is observed to consistently approach the (numerically) optimal performance over a range of signal-to-noise ratios (SNRs), confirming its effectiveness in the considered setting.

关键词： Quantization (signal) Closed-form solutions AWGN Phase modulation Source coding Approximation algorithms Binary phase shift keying

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