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检索条件"任意字段=3rd International Conference on Distributed Computing and Internet Technology"

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Cloud-Network Resource Perception and Modeling technology Based on Software-Defined computing First Network 3

Cloud-Network Resource Perception and Modeling Technology Ba...

引用

3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Gong, Xiaomin Bai, Chenchen Ren, Shuangyin Wang, Jingchao Wang, Chunjiang Institute of Systems Engineering Academy of Military Sciences People's Liberation Army Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350372052

The computing First Network (CFN) is a novel network paradigm that efficiently allocates and schedules computing force, storage resources, and network capacity across cloud, edge, and device domains. Leveraging the comprehensive network perspective offered by Software-Defined Networking (SDN) and precise assessment of distributed computing resources, this paper introduces an architecture known as Software-Defined computing First Network (SD-CFN). This architecture forms the basis for modeling computing and network resources and designing a new representation of network topology. Ultimately, the proposed architecture and the novel network topology representation undergo comprehensive analysis and validation. © 2023 IEEE.

关键词： Network topology

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Exploring Strategies for Privacy-Preserving Machine Learning in distributed Environments 3

Exploring Strategies for Privacy-Preserving Machine Learning...

引用

3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Dodda, Suresh Kumar, Anoop Kamuni, Navin Ayyalasomayajula, Madan Mohan Tito IIT Roorkee India AI-ML BITS Pilani WILP United States Aspen University PhoenixAZ United States

ISBN: (纸本)9798350372120

Machine Learning (ML) with distributed privacy preservation is growing in significance as it focuses on facilitating multi-party learning without requiring actual data sharing. This is especially helpful for companies that want to work together but are unable to do so because of ethical, regulatory, or budgetary constraints on sharing data. In order to address these issues, this study examines three privacy-preserving algorithms: regularized logistic regression with Differential Privacy (DP), stochastic gradient descent (SGD) with differentially private updates, and a distributed Lasso that distributes gradients among data centers. The study emphasizes the relationship between error rate and privacy through these algorithms. In order to improve error rates for large datasets, both DP algorithms modify their sensitivity dependent on the amount of data, highlighting the significance of training data volume in model performance in the study. Results demonstrate that using the SGD;error rate can be reduced by employing random projections in advance. © 2024 IEEE.

关键词： Large datasets

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internet of Things with 5G technology: A Critical Review 3

Internet of Things with 5G Technology: A Critical Review

引用

3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Chahar, Suman Kaur, Kawaljit University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9798350399264

The rapid development of technology has revolutionized the way people live their lives. This technological change has been made possible by the emergence of the so-called "smart concepts,"which have increased global connectivity. One such idea is the internet of Things (IoT), which makes it possible to monitor distant devices and transmit value directly across participants. By 2025, the number of connected devices could reach 75 billion, almost tripling the current figure. This paper provides an in-depth review of the current state of IoT and 5G technologies, their architectures, and the challenges associated with their security. In order to provide safe communication in 5G networks, it tries to uncover unresolved research questions and problems. The paper also provides a critical evaluation of current security protocols used in 5G networks and suggests future directions for research. Overall, The paper gives a thorough review of the state of IoT and 5G technologies today, as well as the security issues they face. © 2023 IEEE.

关键词： internet of things

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Reinforcement Learning-Based Dynamic Voltage and Frequency Scaling for Energy-Efficient computing 3

Reinforcement Learning-Based Dynamic Voltage and Frequency S...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Panda, Prasanta Tripathy, Aruna Charan Bhuyan, Kanhu Tcs Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798350318609

The surge in demand for energy-efficient computing has spurred the exploration of cutting-edge techniques to optimize power consumption in modern computing systems. Though the traditional implementation of Dynamic Voltage and Frequency Scaling (DVFS) has proven effective, their traditional implementations lack adaptability, limiting their ability to fully exploit dynamic workload variations. This research presents an innovative solution by integrating proposed Reinforcement Learning (RL) algorithms into DVFS, addressing the limitations of conventional methods. The proposed RL algorithm employs Q-Iearning, a model-free RL technique, to iteratively learn the optimal policy for adjusting CPU voltage and frequency. Our customized algorithm enables autonomous real-time adjustments of voltage and frequency levels, showcasing a remarkable 20% power saving compared to conventional DVFS. The model's adaptability is evident in its capacity to achieve optimal configurations across diverse workloads, emphasizing RL's potential for enhancing energy efficiency in computing systems. As energy efficiency gains paramount importance, this research underscores the pivotal role of RL, in achieving sustainable and optimal performance in computing systems. © 2024 IEEE.

关键词： Energy efficiency

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An Efficient DDoS Attack Detection using Attention based Hybrid Model in Blockchain based SDN-IoT 3

An Efficient DDoS Attack Detection using Attention based Hyb...

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3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Ananthan, Bhuvanesh Pradeepa, A.Shiny Selvi, A.Sugirtha University of the Cumberlands Department of Information Technology Williamsburg United States PSN College of Engineering and Technology Department of Electrical and Electronics Engineeing Tirunelveli India

ISBN: (纸本)9798350372120

Due to the advancement of IoT (internet of Things) has created a new technology and enhanced modelling capabilities, contributing to the evolution of modern standards of living. The prevalence of insecure and portable devices in the IoT platform has led to a significant rise in cyberattacks. Various research endeavors have been undertaken to identify potential security threats. However, these efforts have encountered challenges such as limitations in storage capacity, high computation costs, system failures, and increased latency. The fundamental programming and centralized features integrated into the SDN (Software Defined Networking) serve as a solution to streamline network management, make abstraction of the network, facilitate the smooth evolution of networks, and potentially address the intricate challenges linked with IoT environment. Despite these advantages, security concerns continue to present a noteworthy impact dilemma for IoT. Notably, the attack DDoS (distributed Denial of Service) emerge as one of the most prominent security threats within IoT systems. This paper delves into a comprehensive attack examination in both IoT and SDN environments. To fortify SDN-IoT technology security, the combination of BC (Blockchain) is explored. Additionally, the Attention based Convolutional long short term memory (At-C-L) is employed to enhance the detection. The proposed attack detection model differentiated the normal and DDoS attacks on the InSDN database and achieved a better accuracy of 98.3%. © 2024 IEEE.

关键词： internet of things

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Complexity Dependencies-Based Optimal Tasks Offloading and computing Resource Allocation Scheme in Industrial internet of Things 3

Complexity Dependencies-Based Optimal Tasks Offloading and C...

引用

3rd international conference on computing, Communication, Perception and Quantum technology, CCPQT 2024

作者： Du, Weiwei Jing, Tao Li, Xuehan Zhang, Boyang Li, Yang Wang, Yang Gao, Bo Zhu, Minghao School of Electronics and Information Engineering Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Economics and Management Beijing Jiaotong University Beijing China

ISBN: (纸本)9798331528386

The evolution of the Industrial internet of Things (IIoT) is essential for fostering the growth of smart manufacturing. In the realm of smart manufacturing, the interconnectivity of production devices (PDs) has emerged as a new production paradigm. In this paradigm, each PD is required to manage various production subtasks that possess interdependencies. Precisely representing the dependencies among these subtasks is critical for reflecting the production status in the smart factories. However, existing studies typically concentrate on the internal dependencies (IDs) among subtasks, overlooking the significant aspect of interconnectivity. As a result, there is an absence of comprehensive descriptions regarding the dependencies among subtasks across different devices. This paper introduces a dependency model for complex subtasks sets, integrating both IDs and external dependencies (EDs) to provide a precise representation of the interdependencies among subtasks. Additionally, considering the constraints posed by limited computing resources on Industrial Edge Servers (IESs), we formulate an optimization problem aimed at the optimal allocation of resources to minimize the cost of system. To address this optimization problem, we propose the proximal policy optimization (PPO) based dependent complex tasks for offloading and computing resources allocation (DOCRA) algorithm, which significantly reduces the cost of system in comparison with alternative solutions. © 2024 IEEE.

关键词： Smart manufacturing

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Towards A Modular End-To-End Machine Learning Benchmarking Framework 3

Towards A Modular End-To-End Machine Learning Benchmarking F...

引用

3rd international Workshop on Testing distributed internet of Things Systems-TDIS

作者： Bayer, Robert Robroek, Ties Tozun, Pinar IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9798400715266

Machine learning (ML) benchmarks are crucial for evaluating the performance, efficiency, and scalability of ML systems, especially as the adoption of complex ML pipelines, such as retrieval-augmented generation (RAG), continues to grow. These pipelines introduce intricate execution graphs that require more advanced benchmarking approaches. Additionally, collocating workloads can improve resource efficiency but may introduce contention challenges that must be carefully managed. Detailed insights into resource utilization are necessary for effective collocation and optimized edge deployments. However, existing benchmarking frameworks often fail to capture these critical aspects. We introduce a modular end-to-end ML benchmarking framework designed to address these gaps. Our framework emphasizes modularity and reusability by enabling reusable pipeline stages, facilitating flexible benchmarking across diverse ML workflows. It supports complex workloads and measures their end-to-end performance. The workloads can be collocated, with the framework providing insights into resource utilization and contention between the concurrent workloads.

关键词： Benchmarking Deep Learning Edge computing

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Oral Disease Image Detection System Using Transfer Learning 3

Oral Disease Image Detection System Using Transfer Learning

引用

3rd international conference on Digital Transformation and Applications, ICDXA 2024

作者： Teo, Amos Hua An Goh, Ching Pang Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

This paper presents a comprehensive approach to detect oral disease using image detection method. Oral disease is usually checked with the presence of dentist, however, with the trend of dentist growing slower and slower, a solution is needed to overcome such problem. Our methodology uses the image recognition technique to visually detect any possible oral disease from the uploaded picture. There are total seven oral disease will be tested in this research which are calculus, data caries, gingivitis, hypodontia, mouth ulcer, oral cancer and discoloration. The system achieved a validation accuracy rate of at least 80% in detecting specified oral disease. However, certain limitations were identified, such as challenges to obtain sufficient datasets from the internet. This paper also discusses the implications and prospects of extending this approach to accommodate a wider range of oral disease that can be visually examined. © 2024 IEEE.

关键词： Image classification

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Analysis of Revolutionizing IoT Solutions through Cloud computing Integration 3

Analysis of Revolutionizing IoT Solutions through Cloud Comp...

引用

3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Abbas, Syed Imran Walia, Ranjan Chandigarh University CSE-APEX Punjab Mohali India Chandigarh University CSE-APEX and UCRD Punjab Mohali India

ISBN: (纸本)9798350396157

This research work investigates the significant potential for increasing productivity and promoting innovation in a range of sectors presented by the combination of DevOps, Cloud computing, and internet of Things IoT. The transformational influence of this convergence is presented quantitatively in this research. Our findings highlighted the efficiency improvements of the integrated strategy in the field of IoT deployment, showing a noteworthy 71.67% decrease in deployment time when compared to traditional techniques. Furthermore, the integration demonstrated its potential for cost reductions by enabling a 37.5% decrease in expenses through dynamic resource scaling. This connection enabled real-time data analysis, which led to an astounding 91.67% decrease in reaction time when compared to conventional data analysis techniques. This demonstrates how it may provide real-time insights that are essential for internet of Things applications. This study offers strong quantitative proof that the confluence of IoT, cloud computing, and DevOps is a route to increased productivity and creativity that has the ability to transform sectors and propel advancement in the digital age. © 2024 IEEE.

关键词： internet of things

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Data Analysis and Decision Support Effect of Big Data Mining in Energy internet Cyber-Physical Systems 3

Data Analysis and Decision Support Effect of Big Data Mining...

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3rd international conference on Electronic Informationtechnology and Smart Agriculture, ICEITSA 2023

作者： Zheng, Kangzhen Qi, Hongtao Ni, Yanyan Tang, Xu Marketing Service Center State Grid Anhui Electric Power Co. Ltd. Anhui Hefei China

ISBN: (纸本)9798400716775

The construction of energy internet can realize the massive collection of load-side data, and the local utilization of load-side data through edge computing devices can effectively reduce the pressure of cloud computing and communication system, but the computing and solving ability of edge devices is worse than that of cloud, and too many computing tasks of edge devices may also affect the overall scheduling. Therefore, this paper combines big data mining technology to analyze the data analysis and decision support in the cyber-physical systems of energy internet, and studies the optimal scheduling method of energy internet in cloud edge environment around the multi-energy complementary process and accurate demand response of energy internet. The research results show that the energy internet information model based on information physical fusion takes into account the plug-and-play characteristics of distributed devices in the network, and at the same time reflects the multi-time scale characteristics of energy internet, taking into account the autonomy of specific devices in the network and so on. From the experimental evaluation, it can be seen that big data mining has a good effect on data analysis and decision support in energy internet cyber-physical systems. © 2023 ACM.

关键词： internet of Energy

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