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Predicate encryption with selective-opening security for receivers: formal definition, generic construction, and concrete instantiations for several primitives

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Designs, Codes, and Cryptography 2024年第6期92卷 1477-1500页

作者： Tseng, Yi-Fan Liu, Zi-Yuan Tso, Raylin Department of Computer Science National Chengchi University Zhinan Rd. Taipei116011 Taiwan Division of Electrical Engineering and Computer Science Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

With the rise of cloud computing, multi-user scenarios have become a common setting for data sharing nowadays. The conservative security notion might not be sufficient for such a data sharing model. As a response to this challenge, there has been significant research targeting security against receiver selective-opening (RSO) attacks. However, we found that none of these studies discuss RSO security specifically for predicate encryption (PE)—an encryption mechanism naturally designed for multi-user data sharing. This manuscript first formalizes the RSO security for PE. We then present a generic PE construction that achieves RSO security based on the simulation-based definition. Our work also features several instantiations for various predicate families, including attribute-based encryption for the monotone span program, which is known as one of the most expressive PE. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Access control

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Enhanced Far-Field Localization Scheme Using Multi-RIS and Efficient Beam Sweeping

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Progress In Electromagnetics Research C 2024年 140卷 163-175页

作者： Alhafid, Abdulrahaman Kh. Younis, Sedki Ali, Y. E. Mohammed Electrical Engineering Department College of Engineering University of Mosul Mosul Iraq Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer and Communications Engineering Department College of Engineering Nawroz University KRG Duhok Iraq

Future 6G networks are anticipated to use reconfigurable intelligent surfaces (RISs) because of their capability to expand coverage, provide a customizable wireless environment, increase localization accuracy, etc. In this paper, RIS-aided localization is considered with orthogonal frequency division multiplexing (OFDM) and single-input single-output (SISO) downlink system in millimeter-wave (mmWave). An efficient beam sweeping (EBS) scheme is proposed accomplished by an RIS to scan the area of interest and estimate the direction of the user equipment (UE), i.e., the signal’s angle of departure (AoD). The AoD with the measured signal time of arrival (ToA), from the RIS to the UE, is used to estimate the UE position. The ToA measurements can be obtained by exploiting the OFDM signal, while the beam sweeping can be obtained by carefully designing the RIS phase profile. The first step of the proposed EBS scheme is to scan the whole area of interest with equally spaced beam angles for coarse estimation of AoD. Then, based on this estimation, the RIS is reconfigured to sweep a slight angle’s range by narrow beams to refine the AoD estimation. Besides, a multi-RIS scenario is proposed, and leveraging the EBS and the consensus fusion method is used to obtain accurate position estimation. Simulation results demonstrate that the proposed EBS in single and multi-RIS scenarios enhances positioning accuracy compared to linear beam sweeping (LBS) methods. Also, the impact of increasing the number of RIS elements and number of sweeping beams, as well as the number of RISs, is investigated thoroughly via numerical simulations. Furthermore, the achievable localization accuracy is assessed using the positioning error bound (PEB). © 2024, Electromagnetics Academy. All rights reserved.

关键词： Millimeter waves

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Signal-to-Noise Ratio Enhancements Enabling 1.6-km Outdoor Visible Light Communication Using Vehicle Lights and Cameras

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IEEE Access 2025年 13卷 84580-84592页

作者： Barber, Don E. Kennedy, Sean M. McEachen, John C. Tummala, Murali United States Naval Academy Electrical and Computer Engineering Department AnnapolisMD21402 United States Naval Postgraduate School Electrical and Computer Engineering Department MontereyCA93943 United States

Light-based communications offer a promising alternative to alleviate congestion in crowded radio frequency bands and extend communication frequencies towards the THz range. This paper addresses the challenge of strong background interference in outdoor visible light communication (VLC) and presents novel techniques to enable VLC using existing vehicle lights and cameras, focusing on physical layer enhancements for signal-to-noise ratio (SNR) improvement. These enhancements to commodity hardware-based VLC systems apply to vehicular communication, outdoor Internet-of-Things (IoT) devices, and mobile personal communications devices. New techniques, including preamble-based digital area filtering, optical magnification, and polarization filtering, are experimentally validated at distances up to 1650 m, demonstrating SNR improvements at unprecedented distances over four times farther than previously reported for outdoor VLC. Preamble-based digital area filtering across the camera sensor significantly improves SNR. Compact optical magnification increases both selectivity and array gain, providing over 10 dB of SNR gain at 1.6 km. Polarization filtering provides up to 4 dB of SNR gain even with an unpolarized signal source, and attenuating filters improve SNR at shorter ranges. These results demonstrate the viability of low-cost, long-distance VLC using commodity hardware and provide a foundation for future research on robust VLC for intelligent transportation systems and connected networks. © 2013 IEEE.

关键词： Signaling

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Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5023-5036页

作者： Wang, Kaidi Ding, Zhiguo So, Daniel K. C. Ding, Zhi The University of Manchester Department of Electrical and Electronic Engineering ManchesterM13 9PL United Kingdom University of Manchester Department of Electronic and Electrical Engineering United Kingdom Khalifa University Department of Computer and Information Engineering United Arab Emirates University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This paper investigates wireless federated learning in data heterogeneous scenarios, where device selection usually leads to a degradation in learning performance. This paper is motivated by the fact that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-independent and identically distributed (non-IID) datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence, which is further exacerbated with low device participation. To mitigate weight divergence, an age-weighted FedSGD algorithm is designed in this paper to scale local gradients according to the previous device selection results. Furthermore, by revealing the relationship between device participation and latency, an energy consumption minimization problem is formulated accordingly, which consists of resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derive the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that i) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and ii) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing device participation. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Vision-based Robotic Arm Control for Screwdriver Bit Placement Tasks

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Sensors and Materials 2024年第3期36卷 1003-1018页

作者： Lin, Cheng-Jian Lin, Pei-Jung Shih, Chi-Huang Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%;(2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle;and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Robotic arms

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Build to Win: electrical Hardware Opportunities for STEM Education 14

Build to Win: Electrical Hardware Opportunities for STEM Edu...

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14th IEEE Integrated STEM Education Conference, ISEC 2024

作者： Monagle, Daniel Ponce, Eric A. Leeb, Steven B. Saathoff, Erik K. Brooks, Dylan E. Krause, Thomas C. Green, Daisy H. Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science United States University of Hawai'i Department of Electrical and Computer Engineering Manoa United States

ISBN: (纸本)9798350352801

engineering outreach and introductory courses are essential for motivating and training the next generation of capable engineers. Accessibility and portability of the infrastructure for a STEM course is critical for spreading STEM education and motivation efforts. The ubiquity of student personal computers and laptops provides a convenient platform for introducing computer programming, but the relatively cumbersome equipment deemed necessary for a hands-on electronics learning environment contributes to the lack of electronic circuit hardware exposure for many K-12 students and university undergraduates. This paper presents the relatively low-cost, versatile, electronics platform we have developed at MIT upon which our new, hands-on introductory STEM electrical engineering course has been built and taught in recent years. This paper focuses its discussion on the power supply and measurement systems we have produced that allow students and instructors to engage in hands-on electric circuit exercises and projects without the requirement for expensive laboratory infrastructure. Example demonstrations of introductory laboratory exercises using our platform are shown. © 2024 IEEE.

关键词： Students

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Trajectory-Prediction Techniques for Unmanned Aerial Vehicles (UAVs): A Comprehensive Survey

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1867-1910页

作者： Shukla, Pushpak Shukla, Shailendra Singh, Amit Kumar Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Uttar Pradesh Prayagraj India National Institute of Technology Computer Science and Engineering Department Bihar Patna India

Unmanned Aerial Vehicles (UAVs) have witnessed remarkable significance in diverse sectors, ranging from environmental monitoring, infrastructure inspection, disaster response, wildlife conservation, surveillance, and reconnaissance missions. It is crucial to predict their future states to enable UAVs' safe and efficient operation in dynamic environments. UAV trajectory planning is a crucial aspect of UAV operations, as it determines how the drone will navigate, perform tasks, and avoid obstacles. UAVs can be operated with varying degrees of autonomy, and they can be controlled by humans or autonomously via onboard autopilot software. While existing research has extensively focused on trajectory planning methodologies for UAVs, there is a noticeable gap in the literature concerning the integration of predictive capabilities into trajectory planning, highlighting the need for a comprehensive review of methodologies in UAV trajectory prediction connected with the associated realm of trajectory planning. This article provides a comprehensive and comparative analysis of trajectory prediction methods tailored for autonomous UAVs. Beginning with a precise problem definition and algorithm categorization, our study delves into evaluating methodologies rooted in conventional mathematical models, classical machine learning, deep learning, and reinforcement learning models. © 1998-2012 IEEE.

关键词： Trajectory Autonomous aerial vehicles Data models Predictive models Internet of Things Drones Wireless sensor networks Trajectory planning Safety Optimization

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Analyzing the Effectiveness of Voice-Based User Interfaces in Enhancing Accessibility in Human-computer Interaction 13

Analyzing the Effectiveness of Voice-Based User Interfaces i...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Jha, Roshan Fahim, Md Forkan Hossain Hassan, Md Al Mahedi Rai, Chulindra Islam, Md Minhazul Sah, Rahul Kumar Department of Computer Science and Engineering Bengaluru India Department of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798350305463

Voice-based user interfaces (VUIs) represent a promising avenue for enhancing accessibility in humancomputer interaction (HCI). This research paper investigates the effectiveness of VUIs in addressing accessibility challenges for individuals with disabilities. We examine the benefits and challenges associated with VUIs, considering their impact on diverse user groups. Through an exploration of relevant literature and empirical studies, we assess the potential of VUIs to improve accessibility and inclusivity in technology. The research contributes insights into the strengths and limitations of VUIs, highlighting key considerations for designers, developers, and policymakers striving to create more accessible HCI solutions. © 2024 IEEE.

关键词： User interfaces

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An integrated approach for prediction of magnitude using deep learning techniques

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Neural Computing and Applications 2024年第27期36卷 16991-17006页

作者： Joshi, Anushka Raman, Balasubramanian Mohan, C. Krishna Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Telangana Sangareddy502285 India

Timely estimation of earthquake magnitude plays a crucial role in the early warning systems for earthquakes. Despite the inherent danger associated with earthquake energy, earthquake research necessitates extensive parameter estimation and predictive techniques to account for uncertain trends in earthquake waveforms when determining earthquake magnitudes using a single station. This study introduces an effective solution to tackle the issue through the automatic magnitude deep network (AMagDN) model. The proposed model includes long short-term memory (LSTM), a bidirectional LSTM, an autocorrelation attention mechanism, and a machine learning block that can capture detailed information from the seismic waveform recorded during an earthquake. The unique feature of this model is the use of multivariate time series waveforms derived from recorded accelerograms specifically tailored to their energy significance with magnitude and seven fusion tabular parameters involving source and geospatial features. The proposed model’s training, validation and testing are done using independent 15014, 1287 and 3448 records maintained by the Kyoshin network, Japan, for moderate to great impact earthquakes between 5.5 and 8.0 (MJMA). A comparative study shows that the proposed model outperforms recent state-of-the-art models and common linear relations, reducing mean absolute prediction error by 40% from the second-best model. The multi-stations data are also used for successfully forecasting the magnitudes of two significant earthquakes of 7.7 and 7.3 magnitude (MJMA) using the proposed model. The reliable prediction capabilities of the proposed model for both single and multi-station data clearly demonstrate its utility in reducing earthquake hazards. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Long short-term memory

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LSTM-Based Resource Prediction for Disaggregated RAN in 5G Non-Terrestrial Networks 2

LSTM-Based Resource Prediction for Disaggregated RAN in 5G N...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Tsegaye, Henok B. Tshakwanda, Petro M. Worku, Yonatan M. Sacchi, Claudio Christodoulou, Christos Devetsikiotis, Michael University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331530099

The growing demand for advanced beyond 5G connectivity solutions explores the deployment of end-to-end 5G Non- Terrestrial Networks (NTNs) in cloud-native environments. With the increasing reliance on mobile communications, leveraging next-generation radio access network (NG-RAN) architectures with functional splits has become essential. In the 5G NTN network, some split NG-RAN components can be moved to the satellite node to improve connectivity and resilience. This paper investigates advanced beyond 5G connectivity by deploying end-to-end 5G Non-Terrestrial Networks (NTNs) in cloud-native environments, focusing on Low Earth Orbit (LEO) satellites operating in regenerative mode. Specifically, it explores the implementation of F1 and E1 interface splits within such networks. The first architecture extends the F1 interface over the satellite radio interface (F1 over SRI), linking terrestrial central units (gNB-CU) with satellite-based distributed units (gNB-DU). The second architecture incorporates both F1 and E1 splits, facilitating connections between terrestrial control plane units (gNB-CUCP) and user plane units (gNB-CUUP) on the satellite via the E1 interface over SRI (F1-E1 over SRI). The study's primary goal is to predict the resource utilization-specifically CPU, memory, and bandwidth-of gNB-DU and gNB-CUUP functioning as satellite payloads. Employing Long-Short-Term Memory (LSTM) neural networks, this research aims to enhance network resilience by enabling proactive monitoring and resource allocation decisions, addressing the significant computational and bandwidth demands of payloading gNB-CUUP compared to gNB-DU. © 2024 IEEE.

关键词： 5G mobile communication systems

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