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检索条件"任意字段=17th International Conference on Collaborative Computing: Networking Applications and Worksharing"

共 952 条记录，以下是91-100 订阅

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Ubi-Interact 20

Ubi-Interact

引用

17th EAI international conference on Mobile and Ubiquitous Systems - computing, networking and Services (MobiQuitous)

作者： Weber, Sandro Dyrda, Daniel Ludwig, Marian Klinker, Gudrun Tech Univ Munich Munich Germany

ISBN: (纸本)9781450388405

Ubi-Interact is a framework designed to facilitate interactive applications consisting of a variety of individual systems and devices that are distributed over a (local) network. It is designed to allow easy integration of devices and exchange of data based on extendable common data formats. It features edge computing capabilities, allowing to analyze and transform device data. the same computing modules can also be used to manage an arbitrary number of devices in an abstract fashion, allowing ad-hoc handling of (dis-)connecting devices and making devices with similar components/capabilities interchangeable while lowering the necessity to re-implement parts of the overall system behaviour. Specification and implementation of these applications should be modular, extendable and re-usable so users can share and improve on the work of others. Performance, re-usability of once established capabilities and easy integration of devices are the main points of interest for developing this framework.

关键词： Human-Computer Interaction Distributed applications Reactive Systems Edge computing Mixed Reality Internet of things Machine Learning Robotics

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IAM - Interpolation and Aggregation on the Move: collaborative Crowdsensing for Spatio-temporal Phenomena 20

IAM - Interpolation and Aggregation on the Move: Collaborati...

引用

17th EAI international conference on Mobile and Ubiquitous Systems - computing, networking and Services (MobiQuitous)

作者： Du, Yifan Sailhan, Francoise Issarny, Valerie Inria Paris Paris France CNAM Paris Paris France

ISBN: (纸本)9781450388405

Crowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. this limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized post-processing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. the evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces -and may even eliminate- the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach.

关键词： Crowdsensing Ubiquitous Sensing Pervasive computing Aggregation Interpolation Opportunistic Relay

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collaborative computing Based on Truthful Online Auction Mechanism in Internet of things 17th

Collaborative Computing Based on Truthful Online Auction Mec...

引用

17th EAI international conference on collaborative computing: networking, applications, and worksharing, CollaborateCom 2021

作者： Wu, Bilian Chen, Xin Jiao, Libo School of Computer Science Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9783030926373

With the increasingly diverse and complex demands of the Internet of things (IoT) devices, terminal equipments have been unable to effectively meet their quality of service (QoS). To resolve this issue, the resource allocation strategy for edge-cloud collaborative computing has been seen as a promising scheme by offloading computation-intensive tasks from IoT devices to edge servers or cloud data center. In this paper, we study the resource collaborative scheduling problem and formulate a truthful online auction mechanism in the mobile edge computing (MEC) system. We propose the objective problem of maximizing the long-term average revenue, subjecting to the task queue stability constraint. Furthermore, we apply Lyapunov optimization techniques to deal with this objective problem, which can be solved without prior information. So as to derive subproblems optimal solutions and obtain effective resource allocation strategy, a revenue maximization online auction (RMOA) algorithm is designed. theoretical analysis shows that the RMOA algorithm can achieve optimal system revenue approximately while ensuring the stability of the MEC system. In addition, simulation results indicate the effectiveness of the RMOA algorithm and verify the influence of various parameters. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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CASE: Predict User Behaviors via collaborative Assistant Sequence Embedding Model 17th

CASE: Predict User Behaviors via Collaborative Assistant Se...

引用

17th EAI international conference on collaborative computing: networking, applications, and worksharing, CollaborateCom 2021

作者： He, Fei Jin, Canghong Wu, Minghui Zhejiang University City College Hangzhou310015 China Zhejiang University Hangzhou310027 China

ISBN: (纸本)9783030926373

the order of behaviors implies that sequential patterns play an important role of the user-behavior prediction problem. Traditional behavior-prediction models use large-scale static matrices which ignore sequential information. Moreover, although Markov chains and deep learning methods consider sequential information, they still suffer the problems of the behavior uncertainty and data sparsity in real life scenarios. In this paper, we propose a collaborative-assistant sequence embedding prediction (named CASE) model as a solution to address these shortcomings. the idea is to mine sequential behavior patterns with strong intention-expressing ability based on a collaborative selector, and construct original behavior graph and intent determination graph (IDG), following which we predict user behavior based on graph embedding and recurrent neural networks. the experiments on three public datasets demonstrate that CASE outperforms many advanced methods based on a variety of common evaluation metrics. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Embeddings

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Toward Dispersed computing: Cases and State-of-the-Art 17

Toward Dispersed Computing: Cases and State-of-The-Art

引用

17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Yuan, Sen Xia, Geming Chen, Jian Yu, Chaodong Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China

ISBN: (纸本)9781665406680

With the growth of IoT and latency-sensitive applications (e.g., virtual reality, autonomous driving), massive amounts of data are generated at the network and the edge. Such proliferation drives the development of dispersed computing as a promising complementary paradigm to cloud computing and edge computing. Dispersed computing can leverage in-network computing resources to provide lower latency guarantees and more reliable computing power support than the cloud computing. On the other hand, dispersed computing shows excellent performance in highly dynamic and heterogeneous environments. this paper introduces the concepts of dispersed computing and demonstrates the potential of dispersed computing in terms of providing low-latency services and adapting to highly dynamic and heterogeneous environments through several cases. To better grasp the current state of research in dispersed computing, several major research directions and advances are also presented in the hope of attracting the attention of the community and inspiring more researches to promote the implementation of dispersed computing.

关键词： dispersed computing cloud computing edge computing Internet of things(IoT) latency

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Multi-Agent Deep Reinforcement Learning in Flying Ad-Hoc Networks for Delay-Constrained applications 17

Multi-Agent Deep Reinforcement Learning in Flying Ad-Hoc Net...

引用

17th international conference on Future Networks and Communications / 19th international conference on Mobile Systems and Pervasive computing / 12th international conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022

作者： Grasso, Christian Raftopoulos, Raoul Schembra, Giovanni DIEEI University of Catania Italy CNIT Research Unit of Catania Italy

In the near future, 6G networks will be able to provide its users with heterogeneous tailored end-to-end network services for edge AI-powered applications. However, in many 6G use case scenarios there may not be structured network infrastructures. One of the most appealing solutions to meet this problem is to bring computing, networking and storage facilities on site by means of UAVs. the target of this paper is the definition of a management framework for Flying Ad-Hoc Networks (FANET) aimed at bringing edge computing to remote areas, guaranteeing low latency to ground devices with heterogeneous requirements in terms of mean latency. the proposed framework allows zero-touch management of different network slices realized by partitioning the computing Elements (CE) installed on each UAV of the FANET and wireless links connecting them to each other. An Inter-Slice Orchestrator is in charge of deciding this resource partition, while an Intra-Slice Orchestrator manages horizontal offload among UAVs for each slice via Reinforcement Learning, in order to reduce end-to-end latency and delay jitter. © 2022 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the conference Program Chairs.

关键词： Reinforcement learning

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Cooperative Offloading Based on Online Auction for Mobile Edge computing 17th

Cooperative Offloading Based on Online Auction for Mobile Ed...

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17th international conference on Wireless Algorithms, Systems, and applications (WASA)

作者： Zheng, Xiao Shah, Syed Bilal Hussain Nawaf, Liqaa Rana, Omer F. Zhu, Yuanyuan Gan, Jianyuan Shandong Univ Technol Sch Comp Sci & Technol Zibo Peoples R China Manchester Metropolitan Univ Sch Comp & Math Manchester Lancs England Cardiff Metropolitan Univ Comp Sci Sch Technol Cardiff S Glam Wales Cardiff Univ Sch Comp Sci & Informat Cardiff S Glam Wales Dalian Univ Technol Sch Software Dalian 116620 Peoples R China

ISBN: (纸本)9783031192104;9783031192111

In the field of edge computing, collaborative computing offloading, in which edge users offload tasks to adjacent mobile devices with rich resources in an opportunistic manner, provides a promising example to meet the requirements of low latency. However, most of the previous work has been based on the assumption that these mobile devices are willing to serve edge users, with no incentive strategy. In this paper, an online auction-based strategy is proposed, in which both users and mobile devices can interact dynamically with the system. the auction strategy proposed in this paper is based on an online approach to optimize the long-term utility of the system, such as start time, length and size, resource requirements, and evaluation valuation, without knowing the future. Experiments verify that the proposed online auction strategy achieves the expected attributes such as individual rationality, authenticity and computational ease of handling. In addition, the index of theoretical competitive ratio also indicates that the proposed online mechanism realizes near-offline optimal long-term utility performance.

关键词： Online auction strategy collaborative computing offloading Long-term utility

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End-to-end QoS Management in Self-Configuring TSN Networks 17

End-to-end QoS Management in Self-Configuring TSN Networks

引用

17th IEEE international conference on Factory Communication Systems (WFCS) - Communication in Automation

作者： Garbugli, Andrea Bujari, Armir Bellavista, Paolo Univ Bologna Bologna Italy

ISBN: (纸本)9781665424783

Industrial networked computing environments are expected to serve a wide range of applications with heterogeneous Quality-of-Service (QoS) requirements. this capability demands for novel, QoS-aware network management and configuration techniques resilient in the face of network changes. State-of-the-art approaches only focus on aspects related to the management of network devices. In this work, we move a step further, proposing an end-to-end QoS management approach in Time-Sensitive networking (TSN) compliant networks, capable of handling reconfiguration events e.g., link-drop. Shedding some light on our proposal, we first discuss its functional building blocks, successively validating the approach on a real TSN testbed.

关键词： time-sensitive networking management reconfiguration

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Context-aware Graph collaborative Recommendation Without Feature Entanglement 17th

Context-aware Graph Collaborative Recommendation Without Fea...

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17th EAI international conference on collaborative computing: networking, applications, and worksharing, CollaborateCom 2021

作者： Gu, Tianyi Li, Ping Huang, Kaiwen Southwest Petroleum University Chengdu China

ISBN: (纸本)9783030926342

Inheriting from the basic idea of latent factor models like matrix factorization, current collaborative filtering models focus on learning better latent representations of users and items, by leveraging the expressive power of deep neural networks. However, the settings where rich context information is available pose difficulties for the existing neural network based paradigms, since they usually entangle the features extracted from both IDs and other additional data (e.g., contexts), which inevitably destroy the original semantics of the embeddings. In this work, we propose a context-aware collaborative recommendation framework called CGCR to integrate contextual information into the graph-based embedding process. Our model converts the bipartite graph to a homogeneous one by placing the users and items in the identical feature space. As our method is free of feature crosses, it can preserve the semantic independence on the embedding dimensions and thus improves the interpretability of neural collaborative filtering. We use generalized matrix factorization as the matching function so that the model can be trained in an efficient non-sampling manner. We further give two examples of CGCR: LGC with linear graph convolutional networks and LGC+ with attention mechanism. Extensive experiments on five real-world public datasets indicate that the proposed CGCR models significantly outperform the state-of-the-art methods on the Top-K recommendation task. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Graph neural networks

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Optimizing microservices with hyperparameter optimization 17

Optimizing microservices with hyperparameter optimization

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17th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Hai Dinh-Tuan Katsarou, Katerina Herbke, Patrick Tech Univ Berlin Serv Ctr Networking Berlin Germany

ISBN: (纸本)9781665406680

In the last few years, the cloudification of applications requires new concepts and techniques to fully reap the benefits of the new computing paradigm. Among them, the microservices architectural style, which is inspired by service-oriented architectures, has gained attention from both industry and academia. However, decomposing a monolith into multiple microservices also creates several challenges across the application's lifecycle. In this work, we focus on the operation aspect of microservices, and present our novel proposal to enable self-optimizing microservices systems based on grid search and random search techniques. the initial results show our approach is able to optimize the latency performance of microservices to up to 10.56%.

关键词： microservices optimization hyperparameter optimization grid search random search

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