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

共 3169 条记录，以下是161-170 订阅

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Realizing Digital Product Passports with Crowdsourcing Principles: The Case of Sustainable Smart Grids

Realizing Digital Product Passports with Crowdsourcing Princ...

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international conference on distributed computing in sensor systems (dcoss)

作者： Konstantinos Voulgaridis Thomas Lagkas Dimitris Karampatzakis Vasileios Argyriou Panagiotis Sarigiannidis Department of Computer Science International Hellenic University Kavala Campus Greece Department of Networks and Digital Media Kingston University Kingston upon Thames U.K Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

Due to constant technological advancements, Smart Grids have the potential to significantly reduce energy consumption, while providing efficient energy management. However, their implementation requires a thorough collection of data related to procedures of energy generation and distribution, as well as the inclusion of Circular Economy (CE) principles to ensure sustainability. Considering the impact of Industry 5.0, Digital Product Passports (DPPs) are a sufficient solution for the recording, presentation, and circularity of the respective data, combined with suitable crowdsourcing strategies for the integration of real-time data provided by consumers. This paper explores the potential implementation of DPPs, infused with crowdsourcing principles, for smart grid integration, through the design of a fundamental CE framework, which is further supported by a systematic literature review and a reference use- case scenario of its operational flow.

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distributed pseudonym mechanism based on Consortium Blockchain 23

Distributed pseudonym mechanism based on Consortium Blockcha...

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Baohui Wang, Miaomiao Chen, Shiyou Rui, Lanlan Yang, Yang Gao, Zhipeng Beijing University of Posts and Telecommunications National Engineering Laboratory for Disaster Backup and Recovery Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9781665494571

Crowd sensing is a way to obtain multiple sensing data onto users or mobile devices and is widely used in industrial Internet, smart city, smart medical, etc. However, when users upload sensing data involving sensitive information such as a user identity and location, there will be risks of data privacy leakage and user tracking. For the problem of this scenario, we propose a blockchain-based pseudonym generation mechanism, which uses pseudonyms to realize user anonymity and user identity authentication. The average speed of pseudonym generation and authentication in the simulation experiment is about 0.1s, the experimental results verify the availability and effectiveness of the distributed pseudonym mechanism. © 2021 ieee.

关键词： Privacy Data privacy Smart cities distributed ledger Authentication Mobile handsets sensors

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IRS-Aided Caching with Channel Power-Delay-Profile via DDPG and Self-Supervision Learning 9

IRS-Aided Caching with Channel Power-Delay-Profile via DDPG ...

引用

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

作者： Huang, Huang Zhou, Fasheng Guangzhou Univ Sch Elect & Commun Engn Key Lab On Chip Commun & Sensor Chip Guangdong Hi Guangzhou Peoples R China

ISBN: (纸本)9798350350227;9798350350210

The 5G information age requires a large number of marginal nodes distributed around users to provide digital services. By caching and processing data at edge nodes close to users, edge caching technology can effectively collaborate with cloud computing and edge computing to improve the transmission efficiency as well as network response time. Meanwhile, intelligent reflective surface (IRS) is a promising technology for achieving high reductions in hardware cost and power consumption as compared to traditional relaying systems. It provides reliable and scalable backhaul transmission for base stations and access points in ultra-dense networks, which is in line with the requirements of 5G green technology. Therefore, in this paper, we propose a collaborative edge caching method that effectively combines IRS-aided wireless communication and channel power-delay-profile to design joint caching decisions in a centralized manner through limited cache capacity, improving the quality of content delivery in information transmission. Then we proposed a deep deterministic policy gradient learning method based on the framework of deep reinforcement learning along with self-supervision learning to predict IRS phases and collaborative caching strategies. The numerical simulation results show that the proposed algorithm has better convergence speed and learning accuracy than that of the existing algorithms, and is expected to be applied for future massive and dynamical content delivery applications.

关键词： Deep deterministic policy gradient (DDPG) edge caching intelligent reflecting surface average power-delay-profile(APDP) spectrum efficiency (SE) self-supervision learning

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Scaling Machine Learning at the Edge-Cloud: A distributed computing Perspective

Scaling Machine Learning at the Edge-Cloud: A Distributed Co...

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international conference on distributed computing in sensor systems (dcoss)

作者： Fabrizio Marozzo Alessio Orsino Domenico Talia Paolo Trunfio DIMES University of Calabria Rende Italy

The widespread diffusion of Internet of Things (IoT) devices has led to an exponential growth in the volume of data generated at the edge of the network. With the rapid spread of machine learning (ML)-based applications, performing compute and resource-intensive learning tasks at the edge has become a critical issue, resulting in the need for scalable and efficient solutions that can overcome the resource constraints of edge devices. This paper analyzes the problem of scaling ML applications and algorithms at the edge-cloud continuum from a distributed computing perspective. In particular, we first highlight the limitations of traditional distributed architectures (e.g., clusters, clouds, and HPC systems) when running ML applications that use data generated at the edge. Next, we discuss how to enable traditional ML algorithms combining the benefits of edge computing, such as low-latency processing and privacy preservation of personal user data, with those of cloud computing, such as virtually unlimited computational and storage capabilities. Our analysis provides insights into how properly separated parts of a ML application can be deployed across edge-cloud architectures in order to optimize its execution. More-over, examples of ML applications and algorithms appropriately adapted for the edge-cloud continuum are shown.

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A Fault-Tolerant distributed Air-to-Ground Communication Architecture for Urban Air Mobility

A Fault-Tolerant Distributed Air-to-Ground Communication Arc...

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international conference on distributed computing in sensor systems (dcoss)

作者： Shashini Thamarasie Wanniarachchi Volker Turau Institute of Telematics (IfT) Hamburg University of Technology (TUHH) Hamburg Germany

With the increasing demand for urban passenger transportation, the concept urban air mobility (UAM) has gained a lot of research interest. One idea is to employ fully autonomous air crafts. That is, using unmanned and not remotely piloted aerial vehicles as a means of mass transit. Clearly, fully autonomous air taxis constitute a safety critical, time sensitive application. Efficient information management is a prominent requirement and the infrastructure for sensing and fast, reliable communication is the key to mitigate any harm in the aforesaid approach. Nevertheless, failures in such a system are inevitable and need to be addressed. This work investigates the significant aspect of building a fault-tolerant UAM communication network. We introduce a concept of distributed ground station architecture and a protocol for role delegation to improve system availability. This assures an uninterrupted service continuation fulfilling the high standards of safety requirements in an UAM system.

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Approach to Acoustic Drone Detection System Development 12

Approach to Acoustic Drone Detection System Development

引用

12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Kochan, Roman Hots, Nataliya Fabirovskyy, Sergiy Storozh, Volodymyr Kosterov, Alexander Ozirkovskyy, Leonid Helesh, Anna Arkadii, Shulhai Kochan, Volodymyr Strykhalyuk, Bohdan Mykyichuk, Mykola Demydov, Ivan University of Bielsko-Biala Bielsko-Biala42-300 Poland Lviv Polytechnic National University Lviv79013 Ukraine State Scientific Research Institute for Metrology of Measuring and Control Systems Sistema Lviv79000 Ukraine I. Horbachevsky Ternopil State Medical University Ternopil46001 Ukraine West Ukrainian National University Ternopil46003 Ukraine

ISBN: (纸本)9798350358056

An analysis of drone detection methods was carried out in this paper. There is a reasoned choice of the optimal method for implementation based on the analysis of acoustic signals generated by drones. The set of methods of acoustic signal analysis is presented. These methods provide the measurement of drone coordinates and complement each other depending on the flight conditions. The structure of a distributed sensor network oriented on the online detection of drones and their tracking has been synthesized. The developed system provides the appropriate complexity as well as large flexibility and scalability. © 2023 ieee.

关键词： Acoustic signal Distributive sensor network Drone detection

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Intelligent System for distributed Deep Learning Training Traffic Optimization 24

Intelligent System for Distributed Deep Learning Training Tr...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Lu, Weipeng Wu, Shuang Ou, Junhui Niu, Bing He, Jin Song, Kevin Zheng, Xinwei Ustc Hefei China Vmware Beijing China

ISBN: (纸本)9798350319934

distributed training crossing multiple computing nodes and accelerators has been the mainstream solution for large model training. Precedent work on distributed deep learning (DDL) training acceleration has focused on improving the efficiency of communication and GPU resource scheduling, with little attention given to network bandwidth usage. However, due to cluster GPU fragmentation, more than one training job is frequently assigned to the same node to reduce waiting time. The competition for network bandwidth among various distributed training jobs becomes a problem that cannot be overlooked, and this competition results in a considerable reduction in training speed. We propose a system deployed in cluster nodes that includes traffic monitoring, classification, and prediction of DDL training status and implement a priority-based traffic scheduling policy that considers DDL training features. We demonstrate that this policy is beneficial compared to Linux's default policy, which can increase training speed by approximately 18%. Additionally, we increase the training speed by approximately 20% in the case of continuous background traffic by increasing the bandwidth priority during DDL training. © 2022 ieee.

关键词： Deep learning

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(POSTER) A Graph Dataset for Security Enforcement in IoT Networks : GRASEC-IoT

(POSTER) A Graph Dataset for Security Enforcement in IoT Net...

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international conference on distributed computing in sensor systems (dcoss)

作者： Djameleddine Hamouche Reda Kadri Mohamed-Lamine Messai Hamida Seba ESI Algiers Algeria Univ Lyon 2 UR ERIC Bron France UCBL CNRS LIRIS UMR5205 Villeurbanne France

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

The security of Internet of Things (IoT) networks has become a major concern in recent years, as the number of connected objects continues to grow, thereby opening up more potential for malicious attacks. Supervised Machine Learning (ML) algorithms, which require a labeled dataset for training, are increasingly employed to detect attacks in IoT networks. However, existing datasets focus only on specific types of attacks, resulting in ML-based solutions that struggle to generalize effectively. In this work, we address this limitation by introducing a new dataset that comprehensively covers most known attacks on IoT networks. We present GRASEC-IoT, a graph-based dataset specifically tailored for IoT networks, which provides structural information on attack patterns. This enables the utilization of Graph Neural Networks (GNNs), which have shown remarkable effectiveness across various domains. The dataset, its environment and scripts of attacks are publicly available [1].

关键词： Training Machine learning algorithms Machine learning Graph neural networks Internet of Things Security distributed computing

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An Energy-Efficient Route Selection Using Optimum Deterministic Data Gathering Sub-Path Finding for Wireless sensor Network 2

An Energy-Efficient Route Selection Using Optimum Determinis...

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2nd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Gupta, Tannmay Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350347456

Wireless sensor Networks of energy efficiency play an important role in determining the life of the network. Wireless sensor Network (WSN) is known for its less energy consumption property and is known as a highly resource-constrained network. The advantage of using a mobile sink is to reduce the node's energy consumption and prevent the formation of Energy in the Wireless sensor Network (WSN). Despite these advantages, all of the detected data must be collected in a given time limit, especially in the path of delay-sensitive applications. Data aggregation is necessary for wireless sensor networks that handle fields between base stations and sink nodes. In today's data collection agencies, nodes close to the sink share the sink with all other nodes and receive data from it. Data aggregation processing is used to improve the capacity and efficiency of existing systems. In previous results, the potential for data loss has impaired certain efficiencies and performance is reduced due to energy loss. To deal with this problem, a novel Optimum Deterministic Data Gathering Sub-Path Finding (ODDGSPF) algorithm sensor and geometrical characteristics of the communication disc based on the data collection path have been proposed. This maximizes the data collection by optimizing the data for transferring data acquisition sub-route selection and route optimization jointly to reduce energy consumption. Mobile Sink (MS) is to analyses the Optimum Deterministic Data Gathering Sub-Path Finding (ODDGSPF) algorithm using neighbor path or sub-path. It provides an efficient route to data collected in conjunction with the MS with the center path of the routing. The proposed method is evaluated with the parameters like throughput performance, end to end delay performance, Energy consumption and network life time. © 2023 ieee.

关键词： Energy utilization

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6th ieee international Workshop on Wireless Communications and Networking in Extreme Environments (WCNEE) Message from the Workshop Chairs

Proceedings - 18th Annual International Conference on Distri...

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Proceedings - 18th Annual international conference on distributed computing in sensor systems, dcoss 2022 2022年 XX页

作者： Balasubramaniam, Sasitharan Demirors, Emrecan Guan, Zhangyu Sklivanitis, George University of Nebraska-Lincoln United States Northeastern University United States University at Buffalo United States Florida Atlantic University United States

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