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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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Two-factor distributed Authentication Scheme for Cloud Storage 9

Two-factor Distributed Authentication Scheme for Cloud Stora...

引用

9th international conference on Computer and Communication systems (ICCCS)

作者： Han, Yunxia Xu, Chunxiang Li, Shanshan Lu, Jingjie Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9798350350227;9798350350210

distributed authentication plays a crucial role in cloud storage, requiring users to authenticate their identity to a set of key management servers. This process guarantees that only authenticated users gain access to cryptographic keys stored in these servers for decrypting outsourced data. However, existing distributed authentication schemes rely solely on passwords, posing a critical weakness due to the recent surge in password-based attacks. Once passwords are compromised, adversaries can impersonate users, gaining unauthorized access to key management servers and acquiring cryptographic keys. To address this vulnerability, a recent multi-factor scheme has been proposed that introduces a distributed authentication mechanism for each popular factor such as passwords, Universal 2nd Factor (U2F), and email. Users could select multiple factors and utilize their corresponding mechanisms when authenticating to servers. Nevertheless, a simple combination of these authentication mechanisms incurs substantial overheads. In this paper, we propose a combined verification mechanism for passwords and U2F, allowing servers to efficiently verify both factors. Based on the mechanism, we present a two-factor distributed authentication for cloud storage, dubbed TDAC. In TDAC, users could authenticate to key management servers through a single password entry and a touch on their U2F. Experiments show that TDAC is efficient in terms of computation and communication overhead.

关键词： Cloud storage distributed authentication passwords U2F.

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Binary Offloading in Multi-Access Edge computing systems: Deep Reinforcement Learning Approach

Binary Offloading in Multi-Access Edge Computing Systems: De...

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2024 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2024

作者： Basheer, Salman Nalband, Abdul Haq REVA University School of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350364040

In Multi-Access Edge computing systems, A UE provide tasks to edge nodes, these edge nodes may get over-whelmed in edge computing, resulting in processing lag or task dropouts. This is an issue because, particularly in a decentralized system, it might be challenging for each device to determine whether to offload duties and to which node. With the variable load dynamics at edge nodes in mind, this work tackles the problem of offloading tasks to reduce costs. It presents a deep reinforcement learning technique allowing UE to independently decide which tasks to offload without having to know what other UE have decided or what task models they need to know. To improve this we incorporate Gated Recurrent Unit(GRU), dueling and double DQN techniques, leading to simulation results of almost 400 ms average delay and average energy usage of 220 Joules and the runtime taking 9.45 minutes to train. © 2024 ieee.

关键词： Reinforcement learning

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On development of a pervasive computing aided assistive systems for the elderly in Internet of Things 2

On development of a pervasive computing aided assistive syst...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yao, Di Xing, Yutao Luo, Chenyao Song, Huaju Beijing100083 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China School of Environmental Science Nanjing XiaoZhuang University Nanjing210017 China

ISBN: (纸本)9781510689053

With the rapid development of sensors, wireless communication, and computing technology, Internet of Things (IoT) has gained significant research interest. In particular, increasing people-oriented applications and systems have been developed to improve the quality of life with intelligent services. In this paper, we propose a situational awareness system based on pervasive computing. In response to the current situation of a large number of elderly people living alone or lacking care, an intelligent assistance system model for elderly people living alone is proposed, aiming to solve problems such as forgetting to turn off lights caused by memory decline, falls caused by mobility impairment, and loss of dementia elderly people. Furthermore, we adopt the OSGi architecture to implement the proposed system. In addition, a virtual device module is designed to shield underlying device heterogeneity ensuring seamless collaboration between devices and services. To further enhance the system's capabilities, optical sensors are integrated for intelligent health monitoring, enabling real-time tracking of vital signs such as heart rate and respiratory rate, as well as early detection of potential health anomalies. To make services transparent to users, we explore the integration methods of distributed sensors and intelligent devices in pervasive computing environments. Experimental results indicate that the system can effectively improve the comfort and safety of elderly people's lives, enhance the convenience of daily affairs and risk prevention and control capabilities. Therefore, the developed system not only provides a technical framework for intelligent assistance systems for the elderly, but also has significant implications for the application of pervasive computing in the field of social welfare. © 2025 SPIE.

关键词： Assistive technology

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Energy-Efficient Edge computing Architectures for AI Workloads: A Comparative Analysis in Cloud-Driven Environments

Energy-Efficient Edge Computing Architectures for AI Workloa...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Anand, Abhijeet Goel, Shruti Panesar, Gurpreet Singh Punajab Mohali India

ISBN: (纸本)9798350376470

In the landscape of cloud-driven environments, the convergence of artificial intelligence (AI) workloads with edge computing architectures holds promise for optimizing computational efficiency and minimizing latency. This paper conducts a comprehensive comparative analysis of energy-efficient edge computing architectures tailored specifically for AI workloads. Beginning with an overview of the escalating importance of edge computing within the realm of AI-driven applications, we underscore the necessity for architectures that effectively balance computational prowess with energy conservation. Subsequently, we scrutinize various existing paradigms of edge computing, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and more, assessing their suitability for accommodating AI workloads and delving into the intricate interplay between performance, energy consumption, and scalability. Our analysis encompasses a diverse array of architectural approaches, spanning fog computing, mobile edge computing (MEC), and distributed edge computing, and scrutinizes their efficacy in facilitating AI inference tasks across a spectrum of deployment scenarios. Another thing we look at is ways to make edge settings run faster and use less energy, like task sharing, resource allocation, and job timing. AI jobs can be done with edge computing that doesn't use a lot of power. Through in-depth case studies and a full review of the empirical literature, we show the pros and cons of each design model. This study not only helps people make better decisions, but it also moves cloud-based possibilities forward. © 2024 ieee.

关键词： Cloud computing architecture

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Smart energy modeling in buildings with distributed energy generation by using Discrete Wavelet Transform LSTM method 9

Smart energy modeling in buildings with distributed energy g...

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9th international conference on Electrical Energy systems (ICEES)

作者： Ishwarya, S. Akash, S. Kathireshwar, R. Keerthana, B. Keerthivasan, R. M Kumarasamy Coll Engn Elect & Elect Engn Karur India

ISBN: (纸本)9798350348033

In this paper, we use solar energy resources. It is well known that electricity is usually produced by coal, which is at the extreme end in us cities take millions of years to recover from the burning of coal that has a negative effect on us Environment Today, electricity has become an important part of everyone's daily life. Sun is renewable resource are now fully available and have no harmful effects on us environment. Buildings account approximately 33% of the total energy used, and 40% of direct and indirect energy use global CO2 discharge. As energy consumption increases worldwide, managing energy use by encouraging on-site renewable energy can help meet this demand. This paper proposes a deep learning method based on the Discrete Wavelet Transform and Long Short-Term Memory (DWT-LSTM) and timescheduling method for the added need of modeling and energy management for buildings. The combination of deep learning algorithm, energy storage and energy scheduling will reduce the energy demand and sends the extra generated energy to the grid.

关键词： Maximum Power Point Tracking Discrete Wavelet Transform Long Short -Term Memory PhotoVoltaic panels Deep learning algorithm

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A Novel Adaptive Calibration Method for distributed Roadside Millimeter-Wave Radar Pairs 27

A Novel Adaptive Calibration Method for Distributed Roadside...

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27th ieee international conference on Intelligent Transportation systems, ITSC 2024

作者： Li, Chengmin Wang, Junhua Fu, Ting Yao, Bo Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai200092 China

ISBN: (纸本)9798331505929

A large number of roadside Millimeter-Wave Radars (MMRs) serve Intelligent Transportation systems (ITS), where adaptive calibration is a crucial foundation for long-term roadside ITS services. In distributed radar pairs, overlapping regions between two radars complement each other's information, thereby rapidly expanding coverage. We propose a novel adaptive distributed radar pair calibration method, which considers the similarity between trajectory and road geometry, and vehicle motion consistency, employing a stepwise calibration approach in a simplified transformation model. The mean of residuals standard deviation (MRSD) metric is applied to assess the calibration effectiveness in both lateral and longitudinal directions. The results demonstrate that our proposed algorithm not only achieves high accuracy but also exhibits relatively efficient convergence speed, and we elucidate the mechanism of each module in the final ablation experiment. The dataset in this paper is available online at: https://***/tuqing123/radarpairs-calibration-data-share © 2024 ieee.

关键词： Metadata

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An Innovative Deep Learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 ieee.

关键词： Predictive maintenance

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Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance computing 47

Signal Processing Based Method for Real-Time Anomaly Detecti...

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47th ieee-Computer-Society Annual international conference on Computers, Software, and Applications (COMPSAC)

作者： Dey, ArwIavo Islam, Tanzima Phelps, Chase Kelly, Christopher Texas State Univ Dept Comp Sci San Marcos TX 78666 USA Brookhaven Natl Lab Comp Sci Initiat Long Isl City NY USA

ISBN: (纸本)9798350326970

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in CHIMBUKO, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with CHIMBUKO traces.

关键词： Real-time anomaly detection in HPC Signal based anomaly detection Fast Fourier Transform CHIMBUKO

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Joint Task Offloading and Resource Allocation Optimisation for BS-impaired UAV-Assisted Edge computing 6

Joint Task Offloading and Resource Allocation Optimisation f...

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6th international conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Dong, Aofei Hu, Yaowen Mei, Zhixin Feng, Kuiyuan Chongqing Normal Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9798350377859;9798350377842

With the continuous advancement of wireless communication technologies, the negative impact of base station (BS) impairments on network performance is becoming more and more significant. In this paper, we explore how task offloading and resource allocation optimisation can be achieved through Unmanned Aerial Vehicle (UAV)-assisted edge computing in the context of BS impairments. This study proposes a UAV-assisted vehicular edge computing architecture that aims to provide the required Quality of Service (QoS) for computationally intensive and delay-sensitive applications. We provide an in-depth study of the task offloading and resource allocation problem with the aim of maximising the benefit gained by the vehicle by offloading tasks. This benefit is quantified by measuring the weighted sum of task completion time and energy consumption. We are faced with a mixed integer nonlinear programming (MINLP) problem that involves the joint optimisation of the task offloading decision, the uplink transmission power of the mobile vehicle, and the computational resource allocation on the UAV. Given the complexity of the problem, finding an optimal solution is both difficult and unrealistic for large-scale networks. To address this challenge, we employ a decomposition strategy that splits the original problem into two subproblems: a resource allocation (RA) problem that fixes the task offloading decision, and a task offloading (TO) problem that optimises the optimal value function corresponding to the RA problem. We solve the RA problem using convex optimisation and proposed convex optimisation techniques and apply genetic algorithms to solve the TO problem. The results of simulation experiments show that our proposed algorithm is close to the optimal solution in terms of performance and significantly improves the vehicle unloading benefits when compared to the conventional methods. This suggests that UAV-assisted edge computing can effectively optimise task offloading and resou

关键词： base station unmanned aerial vehicle task offloading resource allocation mixed integer nonlinear programming

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Holonic Learning: A Flexible Agent-based distributed Machine Learning Framework 23

Holonic Learning: A Flexible Agent-based Distributed Machine...

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23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Esmaeili, Ahmad Ghorrati, Zahra Matson, Eric T. Purdue University West Lafayette United States

Ever-increasing ubiquity of data and computational resources in the last decade have propelled a notable transition in the machine learning paradigm towards more distributed approaches. Such a transition seeks to not only tackle the scalability and resource distribution challenges but also to address pressing privacy and security concerns. To contribute to the ongoing discourse, this paper introduces Holonic Learning (HoL), a collaborative and privacy-focused learning framework designed for training deep learning models. By leveraging holonic concepts, the HoL framework establishes a structured self-similar hierarchy in the learning process, enabling more nuanced control over collaborations through the individual model aggregation approach of each holon, along with their intra-holon commitment and communication patterns. HoL, in its general form, provides extensive design and flexibility potentials. For empirical analysis and to demonstrate its effectiveness, this paper implements HoloAvg, a special variant of HoL that employs weighted averaging for model aggregation across all holons. The convergence of the proposed method is validated through experiments on both identically and independently distributed (IID) and Non-IID settings of the standard MNIST dataset. Furthermore, the performance behaviors of HoL are investigated under various holarchical designs and data distribution scenarios. The presented results affirm HoL's prowess in delivering competitive performance particularly, in the context of the Non-IID data distribution. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Edge computing

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