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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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An Analysis of Edge computing with Multi Access in 5-G technology 3

An Analysis of Edge Computing with Multi Access in 5-G Techn...

引用

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

作者： Gupta, Sitaram Sonia Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

The demands of the rapidly expanding internet. However, since most end users have confined storage space and limiting computing power, it lately evolved into a natural issue how to execute computationally complex apps on resource-bound consumers. The emerging fifth generation (5G) corporation can benefit from Mobile Edge computing (MEC) innovation upgrade portable assets by facilitating figure concentrated applications, handling a lot of information prior to sending them to the cloud, finding distributed computing assets inside the radio access organization (RAN) close to versatile clients, and giving setting mindful administrations utilizing RAN information. In reality, the paradigm shift from 4G to 5G is accompanied by the introduction of new technical concepts. We first give a comprehensive introduction of MEC technology and some of its possible applications. The most recent research on MEC integration with upcoming technologies that will be employed in 5G and beyond is then is summarized. © 2023 IEEE.

关键词： Mobile edge computing

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distributed Blockchain-SDN Models for Robust Data Security in Cloud-Integrated IoT Networks 3

Distributed Blockchain-SDN Models for Robust Data Security i...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Haritha, Koya Vellela, Sai Srinivas Roja, D. Vuyyuru, Lakshma Reddy Malathi, Nallapu Dalavai, Lavanya Dept. of CSE-Data Science Chalapathi Institute of Technology AP Guntur522016 India Dept. of CSE KoneruLakshmaiah Education Foundation AP Vaddeswaram India Dept. of CSE-DS KKR & KSR Institute of Technology and Sciences AP Guntur522017 India

ISBN: (纸本)9798331532420

Blockchain and SDN are combined in the "DistB-SD Cloud" architecture to secure and execute cloud-integrated IoT data. Cloud computing Management and Services, distributed Secure Blockchain Methodology, SDN Environment, and Data Extraction comprise the architecture. SDN-enabled devices safely gather and process sensor data in the data plane, while OpenFlow and OpenStack handle network component communication in the control plane. The application layer improves network administration with dynamic configuration and data analytics. distributed nodes and hash chains ensure system integrity in blockchain-based access control and ledger maintenance. Blockchain and SDN improve security, privacy, and scalability by resisting attacks. The architecture outperforms OpenFlow-based SDN models in throughput and bandwidth, proving its stability under different network conditions. The study shows that the suggested "DistB-SD Cloud" achieves increased throughput (22.1 KB/s) and bandwidth (1.9 GB/s) when nodes and packet arrival rates increase, making it a feasible solution for safe and scalable cloud computing in IoTbased systems. © 2024 IEEE.

关键词： Cloud security

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Construction and Application of a Cloud computing Based Basic Education Information Service Platform 3

Construction and Application of a Cloud Computing Based Basi...

引用

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

作者： Yu, Xiaodong Northwest Minzu University Lanzhou China

ISBN: (纸本)9798350318609

Conventional classification method has good performance when applied to small datasets;however, their applicability to unknown data sets is weak. To serve education information services in the cloud computing (CC) environment, this study presented a cross-source education information classification model based on cloud computing technology. This work outlined mode of CC service and its mode of deployment and provided a processing method of multisource information depend on CC centre integrated with the features of information distribution in CC environment. First, function and structure of CC platform were analyzed. Second, this research provided a summary of feature extraction process of educational information utilizing technology of data mining and provided an approach for classifying educational information depend on text features by analyzing different types of educational information resources. Finally, a model for classifying cross-source academic information in a cloud computing setting was developed. The comparison of experiments proved that the approach presented in this paper has the ability of efficiently categorizing multisource educational information under CC platform. This approach outperformed other traditional categorization method of Support Vector Machine (SVM) in terms of efficiency and also accuracy. © 2024 IEEE.

关键词： Cloud computing

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Optimization Analysis of Blockchain Fragmentation by the Revenue Incentive Algorithm for Edge computing 3

Optimization Analysis of Blockchain Fragmentation by the Rev...

引用

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

作者： Yin, Jing College Of Artificial Intelligence Wuhan Technology And Business University Wuhan China

ISBN: (纸本)9798350318609

The optimization analysis of the fragmentation is critical in blockchain fragmentation, however it has an issue with erroneous performance positioning. The typical Decision tree algorithm is unable to address the phase limit issue in blockchain fragmentation, and the result is insufficient. As a result, a The gain incentive algorithm for edge calculation-based optimization analysis of blockchain sharding is provided, and the optimization analysis of blockchain sharding is assessed. To begin, the evolution strategy theory is used to discover the influencing elements, and the indicators are split based on the optimization analysis of the fragmentation's needs to decrease interference factors in the optimization analysis of the fragmentation. The evolution strategy theory is then used to create a The gain incentive algorithm for edge calculation optimization analysis of the fragmentation scheme, and the outcomes of the optimization analysis of the fragmentation are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the gain incentive algorithm for edge calculation outperforms the standard Decision tree algorithm in terms of optimization analysis of the fragmentation accuracy and time of influencing variables. © 2024 IEEE.

关键词： Blockchain

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Improving LoRa Low-Power Wide-Area Networks with Double Deep-Q Learning-Based Resource Allocation 3

Improving LoRa Low-Power Wide-Area Networks with Double Deep...

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

作者： Madhavan, Sowmya Al-Fatlawy, Ramy Riad Irfan, Md. Mujahid Neeraj, Kumar Dineshkumar, R. Nitte Meenakshi Institute Of Technology Dept.of Ece Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Anurag University Ece Department Hyderabad India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The internet of Things (IoT) has the potential to revolutionize the way we work by incorporating essential end devices (ED) into connected all smart devices. Low-power wide-area networks (LPWANs) determine the maximum number of end devices (EDs) supported by the network and distribute data through these EDs. However, the delay and action of space are inefficient. However, implementing and training a Double Deep Q Learning (DDQL) network can be computationally intensive and involve a significant amount of data transmission, posing a risk to network security. While DDQL can optimize resource allocation based on the network, the proposed technique is essential for significantly improving actions and reducing the risk of transmitted data in LoRaWAN. It supports EDs while ensuring lower power consumption, thereby increasing network capacity. Although the proposed DDQL technique exhibits high performance compared to existing methods such as Hybrid Adaptive Data Rate (HADR), Low-Power Multi-Armed Bandit (LP-MAB) and non-destructive adaptive data rate (ND-ADR, it achieves outstanding results. The outcomes include an Energy Consumption of 0.02J, Packet Delivery Ratio (PDR) of 0.56 and a Packet Loss Ratio (PLR) of 0.72, surpassing existing methods in the context of IoT. © 2024 IEEE.

关键词： internet of things

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Reckon-ing Kubernetes at the Edge using Emulated Clusters 3

Reckon-ing Kubernetes at the Edge using Emulated Clusters

引用

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

作者： Sassi, Alessandro Jensen, Chris Mortier, Richard Politecn Milan Milan Italy Univ Cambridge Cambridge England Microsoft Res Cambridge Cambridge England

ISBN: (纸本)9798400715266

Kubernetes is the industry standard cluster orchestrator and it (or its derivatives) have been proposed for orchestrating edge network resources as used in, e.g., IOT deployments. The limited resources and unreliable connectivity in edge environments present challenges to orchestrators including K8s. Their impact is typically studied through emulation on cloud resources but these are difficult to control an d highly variable in performance. We present Reckon-K8s, a single-host container-based emulator of Kubernetes clusters over a configurable virtual network. We validate its design and implementation via calibration against a (small) physical cluster, and use it to demonstrate how cluster topology and network conditions influence performance in edge scenarios.

关键词： edge computing orchestration emulation

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English Listening and Speaking Learning System Using LINE ChatBot in Cloud computing 3

English Listening and Speaking Learning System Using LINE Ch...

引用

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

作者： Deng, Xiaoyan School Of Arts And Science Chengdu College Of University Of Electronic Science And Technology Of China Chengdu China

ISBN: (纸本)9798350318609

Cloud computing (CC) in a student learning system generates seamless access to educational resources and transfers their data to an offsite cloud server that is handled by a third-party provider. However, need to enhance the listening and speaking abilities of learners which maximizes the communication skills for professional, academic, and social success. Therefore, integrating listening and speaking development models with cloud-based learning systems is significant for comprehensive skill enhancement among students. In this research, the English listening and speaking learning system using LINE ChatBot in CC is analyzed to enhance students' abilities. Initially, 75 students are considered for participating in learning activities containing a four-week conversation exercise in English in both listening and speaking. To examine the competition's influence on language acquisition, competition characteristics are added to learning activities in a group of experiments to increase learning outcomes and motivation. With the help of LINE ChatBot environment of contextual learning, both student groups' performances are improved. Furthermore, extrinsic motivation in both control and experimental groups enhanced the learner's ability of English listening and speaking. © 2024 IEEE.

关键词： Learning systems

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Foundation Pit Support Deformation Prediction Based on Deep Echo State Network Optimized by Empirical Balance Particle Swarm Optimization 3

Foundation Pit Support Deformation Prediction Based on Deep ...

引用

3rd international conference on Artificial Intelligence, internet of Things and Cloud computing technology, AIoTC 2024

作者： Jiang, Xin Zhao, Lu Liu, Wen Wang, Xue Tao, Feng Cui, Boyang Wang, Zhenlong Technology Center CCCC Wuhan Zhixing International Engineering Consulting Co. Ltd. Wuhan China

ISBN: (纸本)9798331530280

The deformation of foundation pit excavation is influenced by various factors, exhibiting high nonlinearity and instability. To achieve more accurate prediction of foundation pit deformation, the Variational Mode Decomposition (VMD) algorithm is introduced to perform multi-modal decomposition on the deformation of foundation pit support structures, thereby reducing the non-stationarity of the data. A Deep Echo State Network (DESN) model is established to train and predict each modal sequence, with the parameters of the DESN optimized during the training process using the Empirical Balance Particle Swarm Optimization (EBPSO) algorithm. The preliminary results are obtained by summing the predicted results of each mode. To further improve the accuracy of deformation prediction, the EBPSO-DESN model is employed to predict and compensate for prediction errors. Finally, the final prediction results are obtained by superposition. Simulation results demonstrate that the VMD-EBPSO-DESN model with error correction achieves higher accuracy compared to several other models. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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

3rd IEEE International Conference on Distributed Computing a...

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

ISBN: (纸本)9798350318609

The proceedings contain 299 papers. The topics discussed include: time series modelling approach for predictive analytics;research on the recognition of accounting information distortion by random forest algorithm;reconstruction and update of 3D model of mechanical products based on 3D point cloud data;prediction of soil organic carbon content using machine learning based fuzzy C-means clustering;fault detection and classification in power transmission lines using discrete wavelet transform-based swish recurrent neural network;immersive dramatic space 3D layout using panoramic image reconstruction algorithm;multi-objective optimization of noise in a high-speed railway by a hybrid algorithm;new energy vehicle customer mining model based on machine learning algorithm;and robotization of agriculture using image processing techniques.

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