2018 IEEE International Conference on Edge Computing (EDGE)最新文献_第3页

An Energy-Aware IoT Femtocloud System 能源感知物联网飞云系统

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00015

Hend K. Gedawy, Karim Habak, Khaled A. Harras, M. Hamdi

引用次数: 9

Message from the IEEE EDGE 2018 Chairs 来自IEEE EDGE 2018主席的信息

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/edge.2018.00005

引用次数: 0

[Title page i] [标题页i]

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/edge.2018.00001

引用次数: 0

Semi-Autonomous Industrial Robotic Inspection: Remote Methane Detection in Oilfield 半自主工业机器人检测:油田甲烷远程检测

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-07-01 DOI: 10.1109/EDGE.2018.00010

R. S. Filho, Ching-Ling Huang, Bo Yu, Raju D. Venkataramana, A. El-Messidi, Dustin Sharber, John Westerheide, N. Alkadi

{"title":"Semi-Autonomous Industrial Robotic Inspection: Remote Methane Detection in Oilfield","authors":"R. S. Filho, Ching-Ling Huang, Bo Yu, Raju D. Venkataramana, A. El-Messidi, Dustin Sharber, John Westerheide, N. Alkadi","doi":"10.1109/EDGE.2018.00010","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00010","url":null,"abstract":"Robots have been increasingly used in industrial applications. They usually operate along with other robots and human supervisors in complex tasks such as industrial assets inspection, monitoring and maintenance. Even though fully autonomous robotics applications are still work-in-progress, supervised semi-autonomic operation of robots in industrial applications are going mainstream. They promote overall cost reduction, efficiency, accuracy and safety of human workers. These systems combine human-in-the-loop, semi-autonomous robots, edge computing and cloud services to achieve the automation of complex industrial tasks. This paper is a first in series where we describe a robotic platform developed within BHGE and GE-GRC, discussing its use in one example of industrial inspection case study for remote methane inspection in oilfield. We outline the requirements for the system, sharing the experience of our design and implementation trade-offs. In particular, the synergy among the semi-autonomous robots, human supervisors, model-based edge controls, and the cloud services is designed to achieve the responsive onsite monitoring and to cope with the limited connectivity, bandwidth and processing constraints in typical industrial setting.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121547022","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: 5

Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices? 现有的知识转移技术对边缘设备的深度学习有效吗?

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-06-11 DOI: 10.1145/3220192.3220459

Ragini Sharma, Saman Biookaghazadeh, Baoxin Li, Ming Zhao

{"title":"Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices?","authors":"Ragini Sharma, Saman Biookaghazadeh, Baoxin Li, Ming Zhao","doi":"10.1145/3220192.3220459","DOIUrl":"https://doi.org/10.1145/3220192.3220459","url":null,"abstract":"With the emergence of edge computing paradigm, many applications such as image recognition and augmented reality require to perform machine learning (ML) and artificial intelligence (AI) tasks on edge devices. Most AI and ML models are large and computational-heavy, whereas edge devices are usually equipped with limited computational and storage resources. Such models can be compressed and reduced for deployment on edge devices, but they may lose their capability and not perform well. Recent works used knowledge transfer techniques to transfer information from a large network (termed teacher) to a small one (termed student) in order to improve the performance of the latter. This approach seems to be promising for learning on edge devices, but a thorough investigation on its effectiveness is lacking. This paper provides an extensive study on the performance (in both accuracy and convergence speed) of knowledge transfer, considering different student architectures and different techniques for transferring knowledge from teacher to student. The results show that the performance of KT does vary by architectures and transfer techniques. A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student. But other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131801480","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: 39

Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN 基于轻量级CNN的实时人体检测边缘服务

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-04-24 DOI: 10.1109/EDGE.2018.00025

S. Nikouei, Yu Chen, Sejun Song, Ronghua Xu, Baek-Young Choi, Timothy R. Faughnan

{"title":"Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN","authors":"S. Nikouei, Yu Chen, Sejun Song, Ronghua Xu, Baek-Young Choi, Timothy R. Faughnan","doi":"10.1109/EDGE.2018.00025","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00025","url":null,"abstract":"Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real-time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource-limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (L-CNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed L-CNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real-world surveillance video streams. The experimental study has validated the design of L-CNN and shown it is a promising approach to computing intensive applications at the edge.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114756297","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: 108

Towards Mission-Critical Control at the Edge and Over 5G 实现边缘和5G以上的关键任务控制

2018 IEEE International Conference on Edge Computing (EDGE) Pub Date : 2018-03-06 DOI: 10.1109/EDGE.2018.00014

P. Skarin, William Tarneberg, Karl-Erik Årzén, M. Kihl

{"title":"Towards Mission-Critical Control at the Edge and Over 5G","authors":"P. Skarin, William Tarneberg, Karl-Erik Årzén, M. Kihl","doi":"10.1109/EDGE.2018.00014","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00014","url":null,"abstract":"With the emergence of industrial IoT and cloud computing, and the advent of 5G and edge clouds, there are ambitious expectations on elasticity, economies of scale, and fast time to market for demanding use cases in the next generation of ICT networks. Responsiveness and reliability of wireless communication links and services in the cloud are set to improve significantly as the concept of edge clouds is becoming more prevalent. To enable industrial uptake we must provide cloud capacity in the networks but also a sufficient level of simplicity and self-sustainability in the software platforms. In this paper, we present a research test-bed built to study mission-critical control over the distributed edge cloud. We evaluate system properties using a conventional control application in the form of a Model Predictive Controller. Our cloud platform provides the means to continuously operate our mission-critical application while seamlessly relocating computations across geographically dispersed compute nodes. Through our use of 5G wireless radio, we allow for mobility and reliably provide compute resources with low latency, at the edge. The primary contribution of this paper is a state-of-the art, fully operational test-bed showing the potential for merged IoT, 5G, and cloud. We also provide an evaluation of the system while operating a mission-critical application and provide an outlook on a novel research direction.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125960835","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: 55