Hend K. Gedawy, Karim Habak, Khaled A. Harras, M. Hamdi
Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose a system that leverages the idle compute cycles in a group of heterogeneous mobile and IoT devices that can be clustered to form an edge femtocloud. At the heart of this system, we formulate a task assignment and scheduling problem that strives to maximize the computational throughput of the constructed femtocloud while maintaining the energy consumption below an operator specified threshold. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve this problem. We implement a prototype of our system and use it to evaluate its performance. Our results demonstrate the system's ability to utilize the available compute capacity of a group of mobile and IoT devices while adhering to pre-specified energy constraints. Compared to other schedulers, our scheduler achieves up to 40% performance improvement.
{"title":"An Energy-Aware IoT Femtocloud System","authors":"Hend K. Gedawy, Karim Habak, Khaled A. Harras, M. Hamdi","doi":"10.1109/EDGE.2018.00015","DOIUrl":"https://doi.org/10.1109/EDGE.2018.00015","url":null,"abstract":"Mobile and IoT devices are becoming increasingly capable computing platforms that are often underutilized. In this paper, we propose a system that leverages the idle compute cycles in a group of heterogeneous mobile and IoT devices that can be clustered to form an edge femtocloud. At the heart of this system, we formulate a task assignment and scheduling problem that strives to maximize the computational throughput of the constructed femtocloud while maintaining the energy consumption below an operator specified threshold. Due to the NP-Completeness of this scheduling problem, we design a set of heuristics to solve this problem. We implement a prototype of our system and use it to evaluate its performance. Our results demonstrate the system's ability to utilize the available compute capacity of a group of mobile and IoT devices while adhering to pre-specified energy constraints. Compared to other schedulers, our scheduler achieves up to 40% performance improvement.","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"24 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":"128496425","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}
{"title":"Message from the IEEE EDGE 2018 Chairs","authors":"","doi":"10.1109/edge.2018.00005","DOIUrl":"https://doi.org/10.1109/edge.2018.00005","url":null,"abstract":"","PeriodicalId":396887,"journal":{"name":"2018 IEEE International Conference on Edge Computing (EDGE)","volume":"23 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":"115604090","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}
R. S. Filho, Ching-Ling Huang, Bo Yu, Raju D. Venkataramana, A. El-Messidi, Dustin Sharber, John Westerheide, N. Alkadi
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.
{"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}
Ragini Sharma, Saman Biookaghazadeh, Baoxin Li, Ming Zhao
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.
{"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}
S. Nikouei, Yu Chen, Sejun Song, Ronghua Xu, Baek-Young Choi, Timothy R. Faughnan
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.
边缘计算允许更多的计算任务发生在网络边缘的分散节点上。如今，许多对延迟敏感的关键任务应用程序都可以利用这些边缘设备来减少时间延迟，甚至可以通过它们的现场存在来实现实时在线决策。智能监控中的人体目标检测、行为识别和预测都属于这一类，大量视频流数据的转换会耗费宝贵的时间，给通信网络带来巨大压力。人们普遍认为，视频处理和目标检测是计算密集型的，并且过于昂贵，无法由资源有限的边缘设备来处理。本文受深度可分离卷积和单镜头多盒检测器(SSD)的启发，提出了一种轻量级卷积神经网络(L-CNN)。通过缩小分类器的搜索空间，将重点放在监控视频帧中的人类物体上，本文提出的L-CNN算法能够以边缘设备负担得起的计算工作量检测行人。使用openCV库在边缘节点(Raspberry PI 3)上实现了一个原型，并在实际监控视频流中实现了令人满意的性能。实验研究验证了L-CNN的设计，并表明它是一种有前途的边缘计算密集型应用方法。
{"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}
P. Skarin, William Tarneberg, Karl-Erik Årzén, M. Kihl
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.
{"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}
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