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A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing

Digital Communications and Networks 2024年第3期10卷 645-654页

作者： Xu Li Gwanggil Jeon Wenshuo Wang Jindong Zhao School of Computer and Control Engineering Yantai UniversityYantai264005China Department of Embedded Systems Engineering Incheon National UniversityAcademyro-119Incheon22012Republic of Korea

The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network,so it has promoted the applications of crowd-sensing services in different fields,but also brings more privacy security challenges,the most commom which is privacy *** a privacy protection technology combining data integrity check and identity anonymity,ring signature is widely used in the field of privacy ***,introducing signature technology leads to additional signature verification *** the scenario of crowd-sensing,the existing signature schemes have low efficiency in multi-signature ***,it is necessary to design an efficient multi-signature verification scheme while ensuring *** this paper,a batch-verifiable signature scheme is proposed based on the crowd-sensing background,which supports the sensing platform to verify the uploaded multiple signature data efficiently,so as to overcoming the defects of the traditional signature scheme in multi-signature *** our proposal,a method for linking homologous data was presented,which was valuable for incentive mechanism and data *** results showed that the proposed scheme has good performance in terms of security and efficiency in crowd-sensing applications with a large number of users and data.

关键词： 5G network Crowd-sensing Privacy protection Ring signature Batch verification

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Integer Linear Programming Based Design of Deadlock-Free Routing for Chiplet-Based systems 37

Integer Linear Programming Based Design of Deadlock-Free Rou...

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Liu, Shuang Radetzki, Martin University of Stuttgart Embedded Systems Institute of Computer Architecture and Computer Engineering Stuttgart Germany

ISBN: (纸本)9798350377569

Chiplet-based systems have become prominent in large systems-on-Chips (SoCs) as a means to mitigate increasing design costs. However, the integration of multiple chiplets introduces new challenges in the interconnection network, potentially leading to deadlocks. In this paper, we propose an Integer Linear Programming (ILP) based design approach to address this issue. Our method considers various design factors for deadlock-free routing, such as topology, latency, load balancing, path diversity, and fault tolerance, applicable to both general-purpose chiplets and application-specific chiplets. It facilitates the determination of optimal turn restrictions for general-purpose chiplets or constructs optimal deadlock-free routing paths for application-specific chiplets if the communication patterns are known. The results demonstrate the capability of the method to find optimal solutions under various design considerations. © 2024 IEEE.

关键词： Integer linear programming

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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time systems

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ACM Transactions on Design Automation of Electronic systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第6期44卷 2054-2066页

作者： Pfeiffer, Kilian Balaskas, Konstantinos Siozios, Kostas Henkel, Jorg Karlsruhe Institute of Technology Chair for Embedded Systems Karlsruhe76131 Germany University of Patras Department of Computer Engineering and Informatics Patras26504 Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki54124 Greece

In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tackled this problem on an algorithm level, e.g., by letting constrained devices train a subset of the server neural network (NN) model. However, it has been observed that these techniques are not effective w.r.t. accuracy. Importantly, they make simplistic assumptions about devices’ resources via indirect metrics, such as multiply accumulate (MAC) operations or peak memory requirements. We observe that memory access costs (that are currently not considered in simplistic metrics) have a significant impact on the energy consumption. In this work, for the first time, we consider on-device accelerator design for FL with heterogeneous devices. We utilize compressed arithmetic formats and approximate computing, targeting to satisfy limited energy budgets. Using a hardware-aware energy model, we observe that, contrary to the state of the art’s moderate energy reduction, our technique allows for lowering the energy requirements (by 4× ) while maintaining higher accuracy. © 1982-2012 IEEE.

关键词： Budget control

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A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

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computers, Materials & Continua 2024年第12期81卷 3561-3582页

作者： Jianyong Wang Mingliang Gao Qilei Li Hyunbum Kim Gwanggil Jeon School of Electrical and Electronic Engineering Shandong University of TechnologyZibo255000China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK Department of Embedded Systems Engineering Incheon National UniversityIncheon22012Republic of Korea

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public *** counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and *** approaches vary in terms of supervision techniques,network architectures,and model ***,most crowd counting methods rely on fully supervised learning,which has proven to be ***,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often *** a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical *** paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision *** summarize and critically analyze the key methods in these two categories,highlighting their strengths and ***,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark *** believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

关键词： Crowd counting density estimation convolutional neural network(CNN) un/semi-supervised learning

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Energy-Efficient Functional Safety Design Methodology Using ASIL Decomposition for Automotive Cyber-Physical systems

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IEEE Transactions on Reliability 2024年 1-23页

作者： Xie, Guoqi Peng, Hao Huang, Jing Li, Renfa Li, Keqin Department of Computer Engineering College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Key Laboratory for Embedded and Cyber-Physical Systems of Hunan Province Changsha 410082 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Department of Computer Science State University of New York New Paltz NY 12561 USA

Automotive cyber-physical systems (ACPS) are typical cyber-physical systems because of the joint interaction between the cyber part and physical part. Functional safety requirement (including response time and reliability requirements) for an ACPS function must be assured for safe driving. Auto industry is cost-sensitive, power-sensitive, and environment-friendly. Energy consumption affects the development efficiency of the ACPS and the living environment of people. This paper solves the problem of optimizing the energy consumption for an ACPS function while assuring its functional safety requirement (i.e., energy-efficient functional safety for ACPS). However, implementing minimum response time, maximum reliability, and minimum energy consumption is a conflicting problem. Consequently, solving the problem is a challenge. In this paper, we propose a three-stage design process toward energy-efficient functional safety for ACPS. The topic problem is divided into three sub-problems, namely, response time requirement verification (first stage), functional safety requirement verification (second stage), and functional safety-critical energy consumption optimization (third stage). The proposed energy-efficient functional safety design methodology is implemented by using automotive safety integrity level decomposition, which is defined in the ACPS functional safety standard ISO 26262. Experiments with real-life and synthetic ACPS functions reveal the advantages of the proposed design methodology toward energy-efficient functional safety for ACPS compared with state-of-the-art algorithms. IEEE

关键词： Energy utilization

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Joint Hybrid Beamforming and RIS Phase Shift Design for RIS-Enabled Mmwave ISAC System

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9149-9164页

作者： Wang, Lu Abanto-Leon, Luis F. Asadi, Arash Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Technical University of Darmstadt Department of Computer Science Darmstadt64289 Germany TU Delft Embedded Systems Group DelftCD 2628 Netherlands

Empowering cellular networks with augmented sensing capabilities is a key research area in sixth generation (6 G) communication systems. Recently, we have witnessed a plethora of efforts to devise solutions that integrate sensing capabilities into communication systems, i.e., integrated sensing and communication (ISAC), especially at millimeter-wave (mmWave) frequencies with large bandwidths. Despite the challenges posed by severe path loss and blockage at mmWave bands, this work incorporates reconfigurable intelligent surfaces (RISs) into ISAC systems to enhance both sensing and communication. To comply with the waveform utilized in current cellular systems, this paper studies mmWave orthogonal frequency-division multiplexing (OFDM) ISAC systems in the presence of RISs. Specifically, we jointly design the hybrid beamforming and RIS phase shifts to guarantee the sensing functionalities via minimizing the beampattern mean squared error at RIS, subject to signal-to-interference-plus-noise (SINR) and power constraints. The non-convexity of the investigated problem poses a challenge which we address by proposing a solution based on the penalty method and manifold-based alternating direction method of multipliers (ADMM). Simulation results demonstrate that both sensing and communication capabilities improve when the RIS is adequately designed. In addition, we discuss the tradeoff between sensing and communication. © 2025 IEEE.

关键词： Mean square error

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Supervised Local Training With Backward Links for Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 854-867页

作者： Guo, Wenzhe Fouda, Mohammed E. Eltawil, Ahmed M. Salama, Khaled Nabil King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia University of California Center for Embedded and Cyber-Physical Systems IrvineCA92612 United States

The restricted training pattern in the standard backpropagation (BP) requires end-to-end error propagation, causing large memory costs and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacles by completely cutting off the backward path between modules and isolating their gradients. These methods prevent information exchange between modules and result in inferior performance. This work proposes a novel local training algorithm, BackLink, which introduces intermodule backward dependence and facilitates information to flow backward along with the network. To preserve the computational advantage of local training, BackLink restricts the error propagation length within the module. Extensive experiments performed in various deep convolutional neural networks demonstrate that our method consistently improves the classification performance of local training algorithms over other methods. For example, our method can surpass the conventional greedy local training method by 6.45% in accuracy in ResNet32 classifying CIFAR100 and recent work by 2.58% in ResNet110 classifying STL-10 with much lower complexity. Analysis of computational costs reveals that small overheads are incurred in GPU memory costs and runtime on multiple GPUs. Our method can lead up to a 79% reduction in memory cost and 52% in simulation runtime in ResNet110 compared to the standard BP. Therefore, our method could create new opportunities for improving training algorithms toward better efficiency for real-time learning applications. © 2023 IEEE.

关键词： Deep neural networks

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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