2020 IEEE/ACM Symposium on Edge Computing (SEC)最新文献_第4页

Poster: Dependency-Aware Operator Placement of Distributed Stream Processing IoT Applications Deployed at the Edge 海报:部署在边缘的分布式流处理物联网应用的依赖感知运营商位置

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00022

Alireza Mohtadi, Julien Gascon-Samson

引用次数: 0

Task Management for Cooperative Mobile Edge Computing 协同移动边缘计算的任务管理

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00051

Li-Tse Hsieh, Hang Liu, Yang Guo, Robert Gazda

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引用次数: 1

Poster: Data-Aware Edge Sampling for Aggregate Query Approximation 海报:用于聚合查询近似的数据感知边缘采样

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00021

Joel Wolfrath, A. Chandra

引用次数: 0

Edge-Stream: a Stream Processing Approach for Distributed Applications on a Hierarchical Edge-computing System 边缘流:一种分层边缘计算系统上分布式应用的流处理方法

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00009

Xiaoyang Wang, Zhe Zhou, Ping Han, Tong Meng, Guangyu Sun, Jidong Zhai

引用次数: 7

Open Questions for Next Generation Chatbots 下一代聊天机器人的开放性问题

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00050

Winson Ye, Qun Li

引用次数: 0

Message from the Program Co-Chairs 项目联合主席致辞

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/ds-rt.2011.5

G. Theodoropoulos

引用次数: 1

A Cross-Layer Optimization Framework for Distributed Computing in IoT Networks 面向物联网分布式计算的跨层优化框架

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00067

Bodong Shang, Shiya Liu, Sidi Lu, Y. Yi, Weisong Shi, Lingjia Liu

{"title":"A Cross-Layer Optimization Framework for Distributed Computing in IoT Networks","authors":"Bodong Shang, Shiya Liu, Sidi Lu, Y. Yi, Weisong Shi, Lingjia Liu","doi":"10.1109/SEC50012.2020.00067","DOIUrl":"https://doi.org/10.1109/SEC50012.2020.00067","url":null,"abstract":"In Internet-of-Thing (IoT) networks, enormous low-power IoT devices execute latency-sensitive yet computationintensive machine learning tasks. However, the energy is usually scarce for IoT devices, especially for some without battery and relying on solar power or other renewables forms. In this paper, we introduce a cross-layer optimization framework for distributed computing among low-power IoT devices. Specifically, a programming layer design for distributed IoT networks is presented by addressing the problems of application partition, task scheduling, and communication overhead mitigation. Furthermore, the associated federated learning and local differential privacy schemes are developed in the communication layer to enable distributed machine learning with privacy preservation. In addition, we illustrate a three-dimensional network architecture with various network components to facilitate efficient and reliable information exchange among IoT devices. Moreover, a model quantization design for IoT devices is illustrated to reduce the cost of information exchange. Finally, a parallel and scalable neuromorphic computing system for IoT devices is established to achieve energy-efficient distributed computing platforms in the hardware layer. Based on the introduced cross-layer optimization framework, IoT devices can execute their machine learning tasks in an energy-efficient way while guaranteeing data privacy and reducing communication costs.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122767802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Poster: Scaling Up Deep Neural Network optimization for Edge Inference† 海报:扩展边缘推理的深度神经网络优化†

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00025

Bingqian Lu, Jianyi Yang, Shaolei Ren

引用次数: 2

Exploring the Design Space of Efficient Deep Neural Networks 探索高效深度神经网络的设计空间

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00043

Fuxun Yu, Dimitrios Stamoulis, Di Wang, Dimitrios Lymberopoulos, Xiang Chen

引用次数: 0

Towards Online Learning and Concept Drift for Offloading Complex Event Processing in the Edge 边缘复杂事件处理的在线学习与概念漂移研究

2020 IEEE/ACM Symposium on Edge Computing (SEC) Pub Date : 2020-11-01 DOI: 10.1109/SEC50012.2020.00024

João Alexandre Neto, Jorge C. B. Fonseca, Kiev Gama

引用次数: 2