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检索条件"任意字段=5th IEEE International Conference on Distributed Computing in Sensor Systems"

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An Approach on Modelling of distributed Overhead Lines for Transient Studies

An Approach on Modelling of Distributed Overhead Lines for T...

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Communications, Information, Electronic and Energy systems (CIEES), international conference on

作者： Mirsad Madesko Selma Grebovic Belmin Adilovic Vahid Helac Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

With the increase in the need to maintain uninterrupted power supply, due to sensitive devices in modern industry, more and more attention is paid to the protection of distribution lines in atypical construction conditions. Taking into account that lightning strikes are a frequent and main cause of power interruptions, it is necessary to take into account all the factors that can affect the improvement of the performance of distribution lines. this paper introduces a novel approach by incorporating tower grounding resistance, height, type, surge arrester, and flashover models in conditions of high soil resistance without physical protection. the proposed modelling procedure is validated through simulations on the 35 kV line "Kotor - Lovćen" in Montenegro, characterized by inadequate protection and high soil resistance, using the EMTP software and adhering to ieee 1410 - 2004 standards.

关键词： Resistance Arresters Grounding Biological system modeling Poles and towers Lightning Soil Protection Surges Transient analysis

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GTP-Force: Game-theoretic Trajectory Prediction through distributed Reinforcement Learning

GTP-Force: Game-Theoretic Trajectory Prediction through Dist...

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ieee Internatonal conference on Mobile Adhoc and sensor systems (MASS)

作者： Negar Emami Antonio Di Maio Torsten Braun Institute of Computer Science University of Bern Switzerland

this paper introduces Game-theoretic Trajectory Prediction through distributed reinForcement learning (GTP-Force), a system that tackles the challenge of predicting joint pedestrian trajectories in multi-agent scenarios. GTP-Force utilizes decentralized reinforcement learning agents to personalize neural networks for each competing player based on their non-cooperative preferences and social interactions with others. By identifying the Nash Equilibria, GTP-Force accurately predicts joint trajectories while minimizing overall system loss in non-cooperative environments. the system outperforms existing state-of-the-art trajectory predictors, achieving an average displacement error of 0.19m on the Eth+UCY dataset and 80% accuracy on the Orange dataset, which is -0.01m and 5% better than the best-performing baseline, respectively. Additionally, GTP-Force considerably reduces the model size of social mobility predictors compared to approaches with classical game theory.

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Crowdsourced Edge: A Novel Networking Paradigm for the Collaborative Community 16

Crowdsourced Edge: A Novel Networking Paradigm for the Colla...

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16th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Kundig, Stephan Angelopoulos, Constantinos Marios Kuppannagari, Sanmukh R. Rolim, Jose Prasanna, Viktor K. Univ Geneva Comp Sci Dept Geneva Switzerland Bournemouth Univ Fac Sci & Technol Bournemouth Dorset England Univ Southern Calif Ming Hsieh Dept Los Angeles CA 90007 USA

ISBN: (纸本)9781728143514

Edge computing established paradigms are prone to implicate solely powerful server-like edge nodes, in static or semistatic topologies, of centrally-controlled edge networks. In this paper, leveraging upon recent technological advancements and trends, we introduce a novel networking paradigm employing resources provided by independent crowd peers, within a zone of local proximity, to establish collaborative networks for edge computing. We call this paradigm the Crowdsourced Edge. We detail the architecture and characteristics of this novel paradigm, highlighting its unique characteristics and specific challenges, while also positioning it vis-a-vis the existing edge computing concretisations. Finally, we demonstrate the Crowdsourced Edge functionality by presenting an ongoing use case regarding a video-enhanced object search.

关键词： edge computing crowdsourced systems distributed networking

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Design and Implementation of distributed Cloud Monitoring Big Data Storage Based on Zabbix 5

Design and Implementation of Distributed Cloud Monitoring Bi...

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5th Annual international conference on Data Science and Business Analytics, ICDSBA 2021

作者： Jin, Chengming Yao, Zhenxian Tao, Jin Shao, Shiqi Liaoning Electric Power Energy Development Group Co. Ltd Liaoning China

ISBN: (纸本)9781665445900

With the rapid development of information technology, cloud monitoring has become a new form of service provided by cloud service providers for consumers. the cloud monitoring technology can not only show the content that users are interested in to consumers in the form of strong readability, but also directly warn them in the case of abnormal. therefore, on the basis of understanding the advantages of Zabbix technology, this paper studies the platform design of distributed cloud monitoring and the key technologies of big data storage, and carries out experimental verification and analysis on them. © 2021 ieee.

关键词： distributed cloud

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distributed Self-Orchestration System based on Home 5G Concept

Distributed Self-Orchestration System based on Home 5G Conce...

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ieee international conference on Cloud Networking (CLOUDNET)

作者： Cheikh Saliou Mbacke Babou Yasunori Owada Masugi Inoue Kenichi Takizawa Toshiaki Kuri National Institute of Information and Communications Technology Tokyo Japan National Institute of Information and Communications Technology Sendai Miyagi Japan

Autonomous orchestration in distributed systems is a solution that attracts attention in 5G and beyond. Indeed, proposing a distributed orchestrator system with an Edge Cloud network can ensure autonomous management and service continuity, even when remote resources are not reachable. this feature is one of the key advantages of fifth-generation (5G and beyond) cellular networks, as stated by 3GPP (release 18), particularly regarding Proximity-based Service (ProSe), service management, and so on. therefore, there is an urgent need to address the current gap in the edge cloud system to achieve greater proximity and distributed self-management, thereby providing services closer to the users. this is crucial for meeting the requirements of real-time applications such as gaming, Virtual Reality, augmented reality, vehicle-based services, etc. this paper proposes a self-orchestrator system within the concept of Home 5G architecture, based on 3-tier edge cloud networks, known as Home Edge computing (HEC). We validate our proposal using a Vehicle-to-vehicle-to-Home Server (V2V2H) use case to evaluate the latency of content sharing between end-devices and the HEC server.

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Spatially distributed System for Monitoring of Fields Technical Condition in Mineral Resources Sector 24

Spatially Distributed System for Monitoring of Fields Techni...

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24th international conference on Soft computing and Measurements, SCM 2021

作者： Zolotov, Oleg I. Ilyushina, Aljona N. Novozhilov, Igor M. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint Petersburg Russia

ISBN: (纸本)9781665439749

the paper is devoted to the development of a monitoring system for physical processes in objects of mineral resource complex (for example, coal deposits). Based on the results of the analysis of existing monitoring systems for coal deposits, authors developed mathematical model of physical processes in coal deposits, as well as the system for monitoring of hazardous gas concentrations caused by physical processes in coal deposits. Authors identified adjustment coefficient for converting the ratio of sensor resistance to gas concentration. Authors designed full-scale model for carrying out research, developed a program for plotting graphs based on read parameters from sensors for convenient data presentation, developed a program for collecting and storing data from sensors in a file. © 2021 ieee.

关键词： Coal deposits

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Comprehensive Framework for Facilitating the Deployment of distributed On-Premise Analytics Applications in Resource-Constraint Environments

Comprehensive Framework for Facilitating the Deployment of D...

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ieee international conference on Automation Science and Engineering (CASE)

作者： Nicolai Schoch Pascal Becker Virendra Ashiwal Andrew Habib ABB Corporate Research Center Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Data Analytics (DA) and Machine Learning (ML) algorithms today are inside many applications and systems. they provide state-of-the-art anomaly detection, pattern recognition, health monitoring, predictive maintenance, or help to optimize and calibrate parameter combinations. However, they usually require strong compute resources. It is therefore a challenge to deploy the corresponding algorithms on edge devices or in embedded systems, such as on cloud-decoupled drives and drive systems, as these usually have strict resource constraints. In this work, we look at the deployment of DA/ML algorithms on combinations of resource-constraint edge and device environments. We present our framework for the structured and guided definition, setup, distribution, and deployment of such DA/ML algorithms. A guided workflow leads a user through algorithm specification and configuration. While doing so, it extracts information, which is necessary for the distribution and deployment of the DA/ML algorithm to a suitable device-edge combination. With this solution, a DA/ML algorithm can be executed for sensor data processing and, in the ML context, for inference, on device-edge combinations without permanent cloud connection, e.g., on a drive/gateway combination on a cruise ship, so that it can be used for real-time anomaly detection or pattern analysis.

关键词： Cloud computing Machine learning algorithms Prediction algorithms Inference algorithms Real-time systems Pattern analysis Marine vehicles Anomaly detection Monitoring Predictive maintenance

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A sensor Predictive Model for Power Consumption using Machine Learning

A Sensor Predictive Model for Power Consumption using Machin...

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ieee international conference on Cloud Networking (CLOUDNET)

作者： Nalveer Moocheet Brigitte Jaumard Pierre thibault Lackis Eleftheriadis Computer Science and Software Engineering Concordia University Montreal Canada Ericsson GAIA Montreal Canada Ericsson Research AI Machine Reasoning & Hybrid AI Stockholm Sweden

Reducing the power consumption of computing devices remains a challenge for the data center industry. In 2022, it represents approximately 2% of global electricity consumption and 1% of global greenhouse gas emissions. In addition, data centers must integrate the 5G and B5G challenges into their strategies, by increasing the computing resources available to face higher-quality service constraints. Indeed, 5G and B5G future networks are increasingly software-oriented and therefore, rely heavily on cloud computing to process large amounts of data from multiple sources in *** research works on energy management have been proposed to ensure a reduction of the energy consumed by the various components of a data center (e.g., software, computing devices, or cooling systems). However, to optimize the energy consumption of computing devices (e.g., virtual machines/container operations), it is essential to have an accurate model for predicting power consumption. thus, we propose in this study a new sensor predictive model to predict the dynamic power consumption of cloud computing devices with high *** proposal takes advantage of the various sensors that are now embedded in physical machines, or more generally in cloud server machines, as well as Performance Monitoring Counters to implement a Machine Learning power prediction *** performance evaluation results confirm that our power consumption prediction models outperform previous literature models in terms of accuracy. Indeed, our best model achieves a R 2 score of 93.6% which is higher than the compared baseline model by 21.1%.

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Utilizing Blockchain for distributed Machine Learning based Intrusion Detection in Internet of things 16

Utilizing Blockchain for Distributed Machine Learning based ...

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16th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Cheema, Muhammad Asaad Qureshi, Hassaan Khaliq Chrysostomou, Chrysostomos Lestas, Marios Natl Univ Sci & Technol NUST Islamabad Pakistan Frederick Univ Nicosia Cyprus

ISBN: (数字)9781728143514

ISBN: (纸本)9781728143514

In this paper, we present a distributed machine learning based intrusion detection system in Internet of things (IoT) utilizing Blockchain technology. In particular, spectral partitioning is proposed to divide the IoT network into autonomous systems (AS) enabling traffic monitoring for intrusion detection (ID) to be performed by the selected AS border area nodes in a distributed manner. the ID system is based on machine learning, where a support-vector machine algorithm is trained using prominent IoT data sets and detection of the attackers is provided. Furthermore, the integrity of the attackers' list is offered by utilizing Blockchain technology, which enables a distributed sharing of the attackers' information among the AS border area nodes of the Blockchain network. Simulations are performed to evaluate different aspects of the proposed IoT system and demonstrate the potential of integrating machine learning based ID to a distributed spectral partitioned Blockchain network.

关键词： Blockchain Spectral Partitioning Machine Learning Intrusion Detection Internet of things

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BuildEnVR: An Immersive Analysis System for Environmental Field

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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international conference on Parallel and distributed systems (ICPADS)

作者： Zhenghan Zhou Kebin Liu Yantong Xie Hangxu Jin Shi Liu Ruiqing Wang Haitian Zhao Borong Lin Xiaofang Mu Hui Qi Global Innovation Exchange Tsinghua University Fuzhou Fuyao Institute for Advanced Study School of Architecture Carnegie Mellon University School of Architecture Tsinghua University College of Computer Science and Technology Taiyuan Normal University Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Amidst global warming and escalating extreme weather events, indoor environmental quality’s impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR’s intuitiveness and precision, and it is suitable for both experts and general users.

关键词： Wireless communication Wireless sensor networks three-dimensional displays Buildings Psychology Data visualization Cognitive load Real-time systems Space heating Meteorology Climate change Global warming Environmental factors Immersive learning

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