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检索条件"任意字段=23rd International Conference on Distributed Computing and Networking, ICDCN 2022"

共 140 条记录，以下是61-70 订阅

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An Energy Dispatch Mechanism Based on Double Auction with Multiple Rounds for a Microgrid 23

An Energy Dispatch Mechanism Based on Double Auction with Mu...

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： Liu, Weirong Ma, Cheng Cheng, Yijun Jiang, Fu Rong, Jieqi Peng, Jun School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China

ISBN: (纸本)9781665494571

In a microgrid, distributed energy resources (DERs) can supply energy for consumers. How to schedule energy between DERs and consumers and reduce the dependence on the main power grid is of extreme importance for a microgrid. In this paper, a multi-round double auction mechanism is proposed to achieve coordinated and optimal energy dispatch, which allows multiple sellers to trade energy with multiple buyers through adjusting prices dynamically in each round until the buyers' bids are not less than the sellers' asks. Different from existing auctions, the excessive energy of one seller can be divided and traded to multiple buyers, and one buyer can purchase the energy from multiple sellers. The simulation results show that the proposed mechanism significantly reduces auction rounds and the dependence on the main power grid and improves auction utility when compared with the existing double auction. © 2021 IEEE.

关键词： Schedules Smart cities Energy resources Simulation Microgrids distributed power generation Resource management

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computing Amplification Factors for Influence on Social Network based on Learning of Behaviors and Interacted Knowledge Graph 23

Computing Amplification Factors for Influence on Social Netw...

引用

作者： Nguyen, Hien D. Tran, Quan M. Pham, Vuong T. Huynh, Tai University of Information Technology Faculty of Computer Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Kyanon Digital Ho Chi Minh City Viet Nam Sai Gon University Faculty of Information Technology Ho Chi Minh city Viet Nam Ostrava Czech Republic

ISBN: (纸本)9781665494571

This presentation proposes a metric to estimate the influence of users and communities on Social Media Networks through the combining of Knowledge Graph and Deep Learning approaches. An unsupervised deep learning model is constructed to learn and explore the behavior of users based on Variational Graph Autoencoder (VGAE). It enhances to automatically extract from the relationships among users. The model is robust to unseen data and takes no labeling effort. The experiments show significant performance and promising results which are competitive and outperforms some well-known Graph-convolutional-based. The proposed approach is applied to measure the influence of users on the practical social network. This sudy also presents an architecture of a system to mange campaigns of digital marketing on social network. © 2021 IEEE.

关键词： Knowledge engineering Deep learning Measurement Social networking (online) Smart cities Computer architecture Data models

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Rethinking Deep Packet Inspection Design and Deployment in the era of SDN and NFV 23

Rethinking Deep Packet Inspection Design and Deployment in t...

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作者： Mustapha, Hanan Djahel, Soufiene Perry, Philip Zhang, Zonghua Manchester Metropolitan University Glasgow United Kingdom Ulster University Glasgow United Kingdom Huawei France Research Center Paris France

ISBN: (纸本)9781665494571

With the advent of Software-Defined networking (SDN) and Network Functions Virtualization (NFV), the design and deployment of DPI (Deep Packet Inspection) must be reconsidered. The programmability, global visibility and centralized control of SDN, as well as the NFV enabled lightweight service creation and migration, have potential to empower the capability of DPI tools. On the other hand, dynamic environments make the deployment of DPI challenging. Although it has been validated that some security functions like firewall, and Intrusion Detection System (IDS) can be implemented in SDN controllers or NFV, it remains unclear whether or not DPI can be done in the similar way considering its sophisticated interactions with the network traffic packets, especially for the stateful protocols and encrypted traffic. In other words, the design and deployment of DPI in an SDN and NFV architecture would not be as straightforward. Therefore, this paper aims to shed the light on the challenges facing DPI design and deployment in the context of SDN and NFV and propose a solution to overcome them. © 2021 IEEE.

关键词： Protocols Firewalls (computing) Intrusion detection Telecommunication traffic Inspection Network function virtualization Cryptography

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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作者： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. The use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 IEEE.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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2024 IFIP networking conference, IFIP networking 2024

2024 IFIP Networking Conference, IFIP Networking 2024

引用

23rd international Federation for Information Processing on networking conference, IFIP networking 2024

ISBN: (纸本)9783903176638

The proceedings contain 108 papers. The topics discussed include: mapping wireless networks into digital reality through joint vertical and horizontal learning;RO-crate for testbeds: automated packaging of experimental results;unlocking IoT potential: empowering LoraWAN for secure, distributed smart water management;reputation-based trustworthiness degree in interference-variable vehicular networks;quality of service-driven overlapping cooperative NOMA scheme;evaluating localization algorithms in IoT networks under jamming attacks;isochrons in photonic oscillators: a paradigm shift in positioning;FlexBoT: a scalable architecture for multi-application supporting BoT environments with application shifting at runtime;enabling user intent-based network path adaptation for live volumetric streaming;and industry use case viability study with performance models for satellite computing networks.

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers in Smart Grid 23

Joint Energy and Computation Workload Management for Geo-Dis...

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作者： Li, Zizhen Wang, Ran Zhu, Kun Yi, Changyan Liu, Linfeng Niyato, Dusit Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Posts and Telecommunications Nanjing China Nanyang Technological University Singapore

ISBN: (纸本)9781665494571

With the increasing demands of data computation and storage for cloud-based services, the energy demand of data centers (DCs) is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the power load pressure of DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, benefiting both SGs and DCs at large. However, the geo-distributed property of DCs and SGs, as well as the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, to model the interactions between data center operator (DCO) and SGs, a Stackelberg game is formulated. The SG offers the DCO an electricity selling price to maximize the benefit. The DCO allocates the computation workload to geo-distributed DCs, aiming to minimize the costs of energy consumption and carbon emissions. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the proposed scheme, showing that it can achieve considerable cost savings over counterparts. The proposed approach and obtained results provide useful insights for helping the DCO developing rational management strategies. © 2021 IEEE.

关键词： Data centers Costs Smart cities Computational modeling Games Carbon dioxide Data models

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Towards an automatic distribution system - Issues and challenges

Towards an automatic distribution system - Issues and challe...

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IASTED international conference on Parallel and distributed computing and Networks, as part of the 23rd IASTED international Multi-conference on Applied Informatics

作者： Oudshoorn, Michael J. Fuad, Mohammad M. Deb, Debzani Department of Computer Science Montana State University Bozeman MT 59717 United States

There is great potential if one could effectively harness the computation power of the plethora of low priced powerful yet under-utilized workstations connected by high-speed networks. In recent years, rapid improvement of advanced hardware and progress in networking technology is also making PC clusters a competitive platform for parallel computing. Traditionally, cluster programming has been performed with message passing libraries. As the performance, especially the latency, of the communication network improves, the expectation to support a shared-memory programming model on clusters is reviving. In this paper, we put forward a system having both a distributed shared memory cluster and heterogeneous computers as system nodes which can support automatic distribution, migration, communication and coordination of multithreaded applications on a heterogeneous platform. This will be a creative application-driven experimental infrastructure that will explore challenging problems in distribution management and control, in middleware integration and deployment, in software architecture and software engineering.

关键词： distributed computer systems

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distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

引用

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 IEEE.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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Collaborative Random Forests Learning 23

Collaborative Random Forests Learning

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作者： Foucade, Yohan Bennani, Younes Aabbou, Zakaria LIPN-CNRS Umr 7030 Université Sorbonne Paris Nord LaMSN La Maison des Sciences Numèriques France

ISBN: (纸本)9781665494571

Collaborative learning is an emerging field of machine learning. In this framework, multiple learning algorithms try to learn from a distributed database. The main idea is to improve the performance of each algorithm by exchanging information about the local structures of the data, without disclosing sensitive information. It is related to ensemble learning and federated learning. In this work, we presents a collaborative supervised learning approach. We used classification trees as a base algorithm, although the framework is generic and could be implemented with a wide class of algorithms. The process is divided into two steps: a local and a collaboration step. The local step uses a classical supervised learning algorithm, locally and independently on each dataset, which will result in obtaining multiple classifiers for each of those bases. During the collaboration step, each algorithm will collaborate with its remote counterparts. Our approach has been tested on several datasets and has shown promising results. © 2021 IEEE.

关键词： Machine learning algorithms Smart cities Supervised learning Collaboration distributed databases Collaborative work Classification algorithms

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distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. This paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation Data integration Process control distributed databases Data models

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