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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Sahar Jenhani Hassène Gritli Jyotindra Narayan Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia RISC Lab Department of Computing Imperial College London UK Chair of Digital Health Universität Bayreuth Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a novel robust control strategy specifically designed for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot, focusing on position control. Our approach addresses several key challenges, including state constraints, parameter uncertainties, solid and viscous frictions, and external disturbances. We suggest a composite controller that combines a nonlinear controller with a linear state feedback controller in order to successfully address these issues. By utilizing a quadratic Lyapunov function, we derive the expression of the nonlinear control law and establish the Linear Matrix Inequality (LMI) conditions for computing the matrix gain of the linear controller, ensuring robust stabilization of the exoskeleton robot to the desired position. These conditions are derived through the application of advanced mathematical techniques such as the matrix inversion lemma, the Young inequality, the Schur complement, and the S-procedure lemma. Numerical results show the effectiveness of our suggested control law, demonstrating the continuous convergence of the intended angular position to the real one, even when external disturbances and uncertainties are present. Overall, our research not only presents a novel methodology for robust control but also delivers promising results that underscore the efficacy of our approach in achieving stable and reliable performance for 1-DoF knee exoskeleton robots.

关键词： Robust control Uncertain systems State feedback Uncertainty Exoskeletons Position control Solids Linear matrix inequalities Reliability Robots

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SCRM: Secure and Controllable Similarity Retrieval in Multi-user Settings

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IEEE Internet of Things Journal 2025年

作者： Li, Yingying Li, Feng Hou, Gaopan Guan, Yu Liu, Zhiquan Xie, Qi Hangzhou Normal University School of Mathematics Hangzhou311121 China Singapore Management University School of Computing and Information Systems Singapore178902 Singapore Xidian University Hangzhou Institute of Technology Zhejiang Hangzhou311231 China Jinan University College of Cyber Security Guangzhou510632 China Hangzhou Normal University Key Laboratory of Cryptography of Zhejiang Province Hangzhou311121 China

cloud computing has become an essential paradigm for facilitating large-scale and privacy-preserving encrypted image retrieval in Internet of Things (IoT) environments. However, existing encrypted image retrieval schemes face challenges in balancing retrieval efficiency and data security, which hinders their practical adoption. On one hand, retrieval-efficient schemes based on secure k-Nearest Neighbor (kNN) are prone to known-plaintext attacks;on the other hand, highly secure schemes based on homomorphic encryption often suffer from excessive computational and storage overhead. Furthermore, supporting multi-user environments and enforcing fine-grained access control over query users are critical challenges in IoT-based retrieval systems. To tackle these issues, we propose a Secure and Controllable similarity Retrieval scheme in Multi-user settings (SCRM), which achieves a practical trade-off between efficiency and security while enabling multi-user management. First, we design an efficient and privacy-preserving similarity computation method that is resilient against known-plaintext attacks. Second, we introduce a key conversion protocol that enables similarity retrieval in multi-user settings without requiring key sharing. Third, we integrate attribute-based encryption to enforce fine-grained access control and trace query users who may leak decryption keys. A correctness analysis confirms that SCRM ensures accurate similarity retrieval while supporting access control. Furthermore, a formal security analysis demonstrates that SCRM effectively protects data privacy against known-plaintext attacks. Finally, extensive experiments on real-world image dataset validate the efficiency and effectiveness of SCRM. © 2014 IEEE.

关键词： Authentication

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Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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cloud computing market segmentation 13

Cloud computing market segmentation

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13th International Conference on Software Technologies, ICSOFT 2018

作者： Wu, Caesar Buyya, Rajkumar Ramamohanarao, Kotagiri Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9789897583209

The topics of cloud pricing models and resources management have been receiving enormous attention recently. However, very few studies have considered the importance of cloud market segmentation. Moreover, there is no a better, practical and quantifiable solution for a cloud service providers (CSP) to segment cloud market. We propose a novel solution that combines both hierarchical clustering and time series forecasting on the basis of the classical theory of market segmentation. In comparison with some traditional approaches, such as nested, analytic, Delphi, and strategy-based approaches, our method is much more effective, flexible, measurable and practical for CSPs to implement their cloud market strategies by rolling out different pricing models. Our tested results and empirical analysis show that our solution can efficiently segment cloud markets and also predict the market demands. Our primary goal is to offer a new solution so that CSPs can tail its limited cloud resources for its targeted market or cloud customers. Copyright © 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

关键词： Forecasting

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Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments 19

Microservices-based IoT application placement within heterog...

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12th IEEE/ACM International Conference on Utility and cloud computing, UCC 2019

作者： Pallewatta, Samodha Kostakos, Vassilis Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9781450368940

Fog computing paradigm has created innovation opportunities within Internet of Things (IoT) domain by extending cloud services to the edge of the network. Due to the distributed, heterogeneous and resource constrained nature of the Fog computing nodes, Fog applications need to be developed as a collection of interdependent, lightweight modules. Since this concept aligns with the goals of microservices architecture, efficient placement of microservices-based IoT applications within Fog environments has the potential to fully leverage capabilities of Fog devices. In this paper, we propose a decentralized microservices-based IoT application placement policy for heterogeneous and resource constrained Fog environments. The proposed policy utilizes the independently deployable and scalable nature of microservices to place them as close as possible to the data source to minimize latency and network usage. Moreover, it aims to handle service discovery and load balancing related challenges of the microservices architecture. We implement and evaluate our policy using iFogSim simulated Fog environment. Results of the simulations show around 85% improvement in latency and network usage for the proposed microservice placement policy when compared with cloud-only placement approach and around 40% improvement over an alternative Fog application placement method known as Edge-ward placement policy. Moreover, the decentralized placement approach proposed in this paper demonstrates significant reduction in microservice placement delay over centralized placement. © 2019 Association for computing Machinery.

关键词： Internet of things

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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Edge affinity-based management of applications in fog computing environments 19

Edge affinity-based management of applications in fog comput...

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12th IEEE/ACM International Conference on Utility and cloud computing, UCC 2019

作者： Mahmud, Redowan Ramamohanarao, Kotagiri Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9781450368940

Fog computing overcomes the limitations of executing Internet of Things (IoT) applications in remote cloud datacentres by extending the computation facilities closer to data sources. Since most of the Fog nodes are resource constrained, accommodation of every IoT application within Fog environments is very challenging. Hence, we need to efficiently identify which set of applications should be deployed in Fog. It becomes even more complicated when the application characteristics in terms of urgency, size and flow of inputs are considered simultaneously. The necessity of time-optimized execution further intensifies the application management problem. In this work, we propose a policy for Fog environments that distributes application management tasks across the gateway and the infrastructure level. It classifies and places applications according to their Edge affinity. Edge affinity of an application denotes the relative intensity of different attributes coherent with its characteristics such as user-defined deadline, amount of data per input and sensing frequency of IoT devices, which are required to be addressed within Fog environments to meet its Quality of Service (QoS). The proposed policy also minimizes the service delivery time of applications in Fog infrastructure. Its performance is compared with existing application management policies in both iFogSimsimulated and FogBus-based real environments. The experiment results show that our policy outperforms others in combined QoS enhancement, network relaxation and resource utilization. © 2019 Copyright held by the owner/author(s).

关键词： Internet of things

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A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained computing

arXiv

引用

arXiv 2023年

作者： Moore, Ervin Imteaj, Ahmed Rezapour, Shabnam Hadi Amini, M. Florida International University MiamiFL33199 United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Laboratory Southern Illinois University Carbondale United States Smart Decision-Making for Network-Centric Systems Florida International University Florida United States

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users’ sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance. © 2023, CC BY.

关键词： Digital storage

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Workflow-as-a-service cloud platform and deployment of bioinformatics workflow applications

arXiv

引用

arXiv 2020年

作者： Hilman, Muhammad H. Rodriguez, Maria A. Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

Workflow management systems (WMS) support the composition and deployment of workflow-oriented applications in distributed computing environments. They hide the complexity of managing large-scale applications, which includes the controlling data pipelining between tasks, ensuring the application's execution, and orchestrating the distributed computational resources to get a reasonable processing time. With the increasing trends of scientific workflow adoption, the demand to deploy them using a third-party service begins to increase. Workflow-as-a-service (WaaS) is a term representing the platform that serves the users who require to deploy their workflow applications on third-party cloud-managed services. This concept drives the existing WMS technology to evolve towards the development of the WaaS cloud platform. Based on this requirement, we extend cloudBus WMS functionality to handle the workload of multiple workflows and develop the WaaS cloud platform prototype. We implemented the Elastic Budget-constrained resource Provisioning and Scheduling algorithm for Multiple workflows (EBPSM) algorithm that is capable of scheduling multiple workflows and evaluated the platform using two bioinformatics workflows. Our experimental results show that the platform is capable of efficiently handling multiple workflows execution and gaining its purpose to minimize the makespan while meeting the budget. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling algorithms

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A Survey of Fault Tolerance in cloud computing

A Survey of Fault Tolerance in Cloud Computing

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systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Om Kolthoum Elhajjej Nouha Alyaoui Samia Dardouri Karim Chabir Research Laboratory Modeling Aanalysis Control of System (MACS) LR16ES22 National engineering school of Gabes (ENIG) University of Gabes Tunisia Computer Science and Telecommunications Department Laboratory of Modeling Analysis and Control of Systems (MACS) Higher Institute of Applied Sciences and Technology Zarroug Gafsa Gafsa University Tunisia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV’COM Laboratory-Sup’Com University of Carthage Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

This paper is designed to provide a comprehensive overview of the latest developments in fault tolerance methods for cloud computing. Maintaining high availability and reliability of cloud environments requires fault tolerance. This paper explores fault tolerance in the context of cloud computing and discusses recent challenges and innovations in the field. Moreover, it examines the ongoing research efforts to improve fault-tolerance architectures. At the end of the paper, the paper presents system-level metrics that are relevant to fault tolerance.

关键词： Measurement Surveys cloud computing Fault tolerance Technological innovation Scalability Fault tolerant systems Computer architecture Software Software reliability

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