2020 IEEE International Conference on Fog Computing (ICFC)最新文献

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Fog Computing for Augmented Reality: Trends, Challenges and Opportunities 增强现实的雾计算:趋势、挑战和机遇
2020 IEEE International Conference on Fog Computing (ICFC) Pub Date : 2020-04-01 DOI: 10.1109/ICFC49376.2020.00017
S. Salman, T. Sitompul, A. Papadopoulos, T. Nolte
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引用次数: 7
[Copyright notice] (版权)
2020 IEEE International Conference on Fog Computing (ICFC) Pub Date : 2020-04-01 DOI: 10.1109/icfc49376.2020.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icfc49376.2020.00003","DOIUrl":"https://doi.org/10.1109/icfc49376.2020.00003","url":null,"abstract":"","PeriodicalId":173977,"journal":{"name":"2020 IEEE International Conference on Fog Computing (ICFC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123720726","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: 0
ICFC 2020 Committees
2020 IEEE International Conference on Fog Computing (ICFC) Pub Date : 2020-04-01 DOI: 10.1109/icfc49376.2020.00008
{"title":"ICFC 2020 Committees","authors":"","doi":"10.1109/icfc49376.2020.00008","DOIUrl":"https://doi.org/10.1109/icfc49376.2020.00008","url":null,"abstract":"","PeriodicalId":173977,"journal":{"name":"2020 IEEE International Conference on Fog Computing (ICFC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131486402","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: 0
On Decentralized Route Planning Using the Road Side Units as Computing Resources 以路边单元为计算资源的分散路径规划研究
2020 IEEE International Conference on Fog Computing (ICFC) Pub Date : 2020-04-01 DOI: 10.1109/ICFC49376.2020.00009
J. P. Talusan, Michael Wilbur, A. Dubey, K. Yasumoto
{"title":"On Decentralized Route Planning Using the Road Side Units as Computing Resources","authors":"J. P. Talusan, Michael Wilbur, A. Dubey, K. Yasumoto","doi":"10.1109/ICFC49376.2020.00009","DOIUrl":"https://doi.org/10.1109/ICFC49376.2020.00009","url":null,"abstract":"Residents in cities typically use third-party platforms such as Google Maps for route planning services. While providing near real-time processing, these state of the art centralized deployments are limited to multiprocessing environments in data centers. This raises privacy concerns, increases risk for critical data and causes vulnerability to network failure. In this paper, we propose to use decentralized road side units (RSU) (owned by the city) to perform route planning. We divide the city road network into grids, each assigned an RSU where traffic data is kept locally, increasing security and resiliency such that the system can perform even if some RSUs fail. Route generation is done in two steps. First, an optimal grid sequence is generated, prioritizing shortest path calculation accuracy but not RSU load. Second, we assign route planning tasks to the grids in the sequence. Keeping in mind RSU load and constraints, tasks can be allocated and executed in any non-optimal grid but with lower accuracy. We evaluate this system using Metropolitan Nashville road traffic data. We divided the area into 613 grids, configuring load and neighborhood sizes to meet delay constraints while maximizing model accuracy. The results show that there is a 30% decrease in processing time with a decrease in model accuracy of 99% to 92.3%, by simply increasing the search area to the optimal grid’s immediate neighborhood.","PeriodicalId":173977,"journal":{"name":"2020 IEEE International Conference on Fog Computing (ICFC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756471","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
FLIC: A Distributed Fog Cache for City-Scale Applications fllic:城市规模应用的分布式雾缓存
2020 IEEE International Conference on Fog Computing (ICFC) Pub Date : 2020-03-25 DOI: 10.1109/ICFC49376.2020.00019
Jack West, Neil Klingensmith, G. Thiruvathukal
{"title":"FLIC: A Distributed Fog Cache for City-Scale Applications","authors":"Jack West, Neil Klingensmith, G. Thiruvathukal","doi":"10.1109/ICFC49376.2020.00019","DOIUrl":"https://doi.org/10.1109/ICFC49376.2020.00019","url":null,"abstract":"We present FLIC, a distributed software data caching framework for fogs that reduces network traffic and latency. FLIC is targeted toward city-scale deployments of cooperative IoT devices in which each node gathers and shares data with surrounding devices. As machine learning and other data processing techniques that require large volumes of training data are ported to low-cost and low-power IoT systems, we expect that data analysis will be moved away from the cloud. Separation from the cloud will reduce reliance on power-hungry centralized cloud-based infrastructure. However, city-scale deployments of cooperative IoT devices often connect to the Internet with cellular service, in which service charges are proportional to network usage. IoT system architects must be clever in order to keep costs down in these scenarios. To reduce the network bandwidth required to operate city-scale deployments of cooperative IoT systems, FLIC implements a distributed cache on the IoT nodes in the fog. FLIC allows the IoT network to share its data without repetitively interacting with a simple cloud storage service, reducing calls out to a backing store. Our results displayed a less than 2% miss rate on reads. Thus, allowing for only 5% of requests needing the backing store. We were also able to achieve more than 50% reduction in bytes transmitted per second.","PeriodicalId":173977,"journal":{"name":"2020 IEEE International Conference on Fog Computing (ICFC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126179660","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: 2
Towards Auction-Based Function Placement in Serverless Fog Platforms 在无服务器雾平台中实现基于拍卖的功能放置
2020 IEEE International Conference on Fog Computing (ICFC) Pub Date : 2019-12-12 DOI: 10.1109/ICFC49376.2020.00012
David Bermbach, S. Maghsudi, Jonathan Hasenburg, Tobias Pfandzelter
{"title":"Towards Auction-Based Function Placement in Serverless Fog Platforms","authors":"David Bermbach, S. Maghsudi, Jonathan Hasenburg, Tobias Pfandzelter","doi":"10.1109/ICFC49376.2020.00012","DOIUrl":"https://doi.org/10.1109/ICFC49376.2020.00012","url":null,"abstract":"The Function-as-a-Service (FaaS) paradigm has a lot of potential as a computing model for fog environments comprising both cloud and edge nodes. When the request rate exceeds capacity limits at the edge, some functions need to be offloaded from the edge towards the cloud.In this position paper, we propose an auction-based approach in which application developers bid on resources. This allows fog nodes to make a local decision about which functions to offload while maximizing revenue. For a first evaluation of our approach, we use simulation.","PeriodicalId":173977,"journal":{"name":"2020 IEEE International Conference on Fog Computing (ICFC)","volume":"257 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120896225","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: 34
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