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Accountable Distributed Access Control With Privacy Preservation for Blockchain-Enabled Internet of Things Systems: A Zero-Trust Security Scheme

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IEEE Internet of Things Journal 2025年第11期12卷 17936-17947页

作者： Fang, He Xu, Li Nan, Guoshun Zheng, Danyang Zhao, Haitao Wang, Xianbin Fujian Normal University College of Computer and Cyber Security Fuzhou350007 China Beijing University of Posts and Telecommunications National Engineering Research Center for Mobile Network Technologies Beijing100876 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China Nanjing University of Posts and Telecommunications Department of Wireless Communication Key Lab of Jiangsu Province Nanjing210003 China Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet-of-Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication;an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks;and an adaptive reputation update strategy, which is proposed for evaluating the nodes' behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information. © 2014 IEEE.

关键词： Differential privacy

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A Fast Scalable Hardware Priority Queue and Optimizations for Multi-Pushes

A Fast Scalable Hardware Priority Queue and Optimizations fo...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Samuel Collinson Allan Bai Oliver Sinnen Department of Electrical Computer and Software Engineering Parallel and Reconfigurable Computing Lab University of Auckland New Zealand

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Priority queues (PQ) are an essential data structure for many important algorithms. Hardware implementations of priority queues can accelerate such algorithms significantly. Usually a choice has to be made between very fast (fixed-size) hardware PQs or scalable PQs. Fast hardware queues can provide single cycle push and pop operations, but are limited in size. Scalable hardware queues use embedded memory to achieve scalability, but thereby compromise on speed. This paper proposes a fast and scalable hardware priority queue that can be used for general purpose. It is based on a modular and flexible hybrid design, using a shift register queue combined with a heap-based queue. In the best case, i.e. when only the first queue is needed, push and pop operations only take a single cycle. In an experimental evaluation on a Xilinx FPGA we demonstrate that performance of the hybrid queue maintains a good balance between performance and scalability even for applications where the size of the working set of data can have large variance. We further propose an optimisation of the heap-based queue to support workloads that repeat several consecutive push operations followed by a single pop operation, which is the workload, for instance, in state space search or ray-tracing.

关键词： Distributed processing Scalability Sociology Ray tracing Shift registers Data structures Hardware

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Quality of Service (QoS)-Driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertain...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ziliang Xiong Arvi Jonnarth Abdelrahman Eldesokey Joakim Johnander Bastian Wandt Per-Erik Forssén Department of Electrical Engineering Computer Vision Laboratory LiU Sweden Husqvarna Group Huskvarna Sweden Visual Computing Center KAUST Saudi Arabia Zenseact Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

关键词： Training computer vision Uncertainty Conferences Estimation Probability distribution Pattern recognition

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Metaverse Services: The Way of Services Towards the Future

Metaverse Services: The Way of Services Towards the Future

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Xu, Xiaofei Sheng, Quan Z. Benatallah, Boualem Chen, Zhong Gazda, Robert Saddik, Abdulmotableb Ei Singh, Munindar P. Faculty of Computing Harbin Institute of Technology China Macquarie University School of Computing NSW2109 Australia Dublin City University School of Computing Ireland Peking University School of Computer Science China Wireless Networking Lab Interdigital United States University of Ottawa School of Electrical Engineering and Computer Science Canada North Carolina State University Department of Computer Science United States

ISBN: (纸本)9798350304855

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed. © 2023 IEEE.

关键词： Internet of things

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Investigating the Use of Ray Tracing for Signal-Level Radar Simulation in Space Surveillance 5

Investigating the Use of Ray Tracing for Signal-Level Radar ...

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5th International Conference on Information Communication and Signal Processing, ICICSP 2022

作者： Martin, Mogamat Yaaseen Winberg, Simon Gaffar, Yunus Abdul MacLeod, David University of Cape Town Department of Electrical Engineering Cape Town South Africa Advanced Computer Engineering Lab Centre for High Performance Computing Cape Town South Africa

ISBN: (纸本)9781665485890

The growing number of objects in the Low Earth Orbit is becoming increasingly concerning to astronomers and space missions - particularly due to the limitations in tracking accuracy due to the availability and placements of surveillance sensors. This paper connects to this issue, presenting the development of an open-source, ray-traced, signal-level pulsed radar simulator for use in exploring and planning space monitoring solutions without using real-time data feeds. The simulator was also developed around the use of the NVIDIA® OptiX™ engine to accommodate its ray-tracing features and accelerate performance. This was designed to aid astronomers and researchers in space situational awareness applications through the simulation of radar designs for orbital surveillance experiments. The developed tool was also compared against a more streamlined application that used point-model approximations for quick-look simulations, and the trade-offs between both simulators were evaluated. While the use of ray tracing resulted in significant speed costs, it was found that the algorithm also introduced more realistic results relative to point-model simulations - providing various advantages in scenarios involving shadowing and multiscatter. © 2022 IEEE.

关键词： Ray tracing

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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

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Digital Communications and Networks 2022年第6期8卷 900-910页

作者： M.S.Mekala Gautam Srivastava Ju H.Park Ho-Youl Jung Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38544South Korea RLRC LAb for Autonomous Vehicle Parts and Materials Innovation Yeungnam UniversityGyeongsan38544South Korea Department of Math and Computer Science Brandon UniversityBrandonCanada Department of Electrical Engineering Yeungnam UniversityGyeongsanSouth Korea Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and *** services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and *** issues lead to computation and communication overhead of servers and computation *** address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and ***,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication ***,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge *** simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

关键词： Edge computing Adaptive trust weight(ATW)model Quotient user-centric coeval-learning(QUCL)mechanism Deep learning Service reliability

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds

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IEEE Access 2025年 13卷 85507-85525页

作者： Kočić, Veljka Lukač, Niko Rožajac, Dž Schweng, Stefan Gollob, Christoph Nothdurft, Arne Stampfer, Karl Ser, Javier Del Holzinger, Andreas University of Natural Resources and Life Sciences Vienna Human-Centered AI Lab Institute of Forest Engineering Department of Ecosystem Management Climate and Biodiversity Vienna1190 Austria TU Graz Institute of Human-Centered Computing Graz8010 Austria University of Maribor Faculty of Electrical Engineering and Computer Science Maribor2000 Slovenia Institute of Forest Growth Department of Ecosystem Management Climate and Biodiversity University of Natural Resources and Life Sciences Vienna Vienna1190 Austria TECNALIA Derio48160 Spain Department of Mathematics Leioa48940 Spain

Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount. © 2013 IEEE.

关键词： k-nearest neighbors

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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