2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)最新文献
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00043
Lin Kang, Yanjie Qi, Wenhua Gao, Anhong Wang, Z. Dong
Both coverage and connectivity are important problems in wireless sensor network, as more and more non-orientation sensors are continuously added in to the region of interest, the size of covered component and connected component are increased, at some point, the network can achieve an entire coverage and a full connectivity, then the network percolates. In this paper, we calculate the critical density in non-orientation directional sensor network in which the orientations of the sensors are random and the sensors are deployed according to Poisson point process. We propose an approach to compute the critical density in such network, a collaborating path is proposed with the sum of field-of-view angles of two collaborating sensors being π. Then a correlated model of non-orientation directional sensing sectors for percolation is proposed to solve the coverage and connectivity problems together. The numerical simulations confirm that percolation occurs on the estimated critical densities.
{"title":"A Percolation Based Approach for Critical Density in Non-Orientation Directional Sensor Network","authors":"Lin Kang, Yanjie Qi, Wenhua Gao, Anhong Wang, Z. Dong","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00043","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00043","url":null,"abstract":"Both coverage and connectivity are important problems in wireless sensor network, as more and more non-orientation sensors are continuously added in to the region of interest, the size of covered component and connected component are increased, at some point, the network can achieve an entire coverage and a full connectivity, then the network percolates. In this paper, we calculate the critical density in non-orientation directional sensor network in which the orientations of the sensors are random and the sensors are deployed according to Poisson point process. We propose an approach to compute the critical density in such network, a collaborating path is proposed with the sum of field-of-view angles of two collaborating sensors being π. Then a correlated model of non-orientation directional sensing sectors for percolation is proposed to solve the coverage and connectivity problems together. The numerical simulations confirm that percolation occurs on the estimated critical densities.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130953847","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}
Pub Date : 2019-10-01DOI: 10.1109/iucc/dsci/smartcns.2019.00018
Multi-access Edge Computing (MEC) architecture allows network operators to open their networks to a new ecosystem and value chain, and also gained momentum in the academic side. MECN-2019 workshop is our effort to promote research aiming to address a wide spectrum of research challenges and key issues in edge computing and networking. MECN-2019 fosters a cross-disciplinary forum for scientists, engineers and researchers to discuss and exchange novel views, results, experiences and work-in-process regarding all aspects of edge computing and networking technologies, as well as to identify the emerging research topics and open issues for further researches.
{"title":"Message from the MECN 2019 Workshop Chairs","authors":"","doi":"10.1109/iucc/dsci/smartcns.2019.00018","DOIUrl":"https://doi.org/10.1109/iucc/dsci/smartcns.2019.00018","url":null,"abstract":"Multi-access Edge Computing (MEC) architecture allows network operators to open their networks to a new ecosystem and value chain, and also gained momentum in the academic side. MECN-2019 workshop is our effort to promote research aiming to address a wide spectrum of research challenges and key issues in edge computing and networking. MECN-2019 fosters a cross-disciplinary forum for scientists, engineers and researchers to discuss and exchange novel views, results, experiences and work-in-process regarding all aspects of edge computing and networking technologies, as well as to identify the emerging research topics and open issues for further researches.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"45 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130263975","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00056
Yuchen Zhang, Xin Hu, Rong Chen, Zhili Zhang, Liquan Wang, Weidong Wang
Dynamic Beam Hopping (DBH) is a crucial technology for adapting to the flexibility of different service configurations in the multi-beam satellite communications market. The conventional beam hopping method, which ignores the intrinsic correlation between decisions, only obtains the optimal solution at the current time, while deep reinforcement learning (DRL) is a typical algorithm for solving sequential decision problems. Therefore, to deal with the DBH problem in the scenario of Differentiated Services (DIFFSERV), this paper designs a multiobjective deep reinforcement learning (MO-DRL) algorithm. Besides, as the demand for the number of beams increases, the complexity of system implementation increase significantly. This paper innovatively proposes a time division multi-action selectionmethod(TD-MASM) tosolvethecurseofdimensionality problem. Under the real condition, the MO-DRL algorithm with the low complexity can ensure the fairness of each cell, improve the throughput to about 5540Mbps, and reduce the delay to about 0.367ms. The simulation results show that when the GA is used to achieve similar effects, the complexity of GA is about 110 times that of the MO-DRL algorithm.
{"title":"Dynamic Beam Hopping for DVB-S2X Satellite: A Multi-Objective Deep Reinforcement Learning Approach","authors":"Yuchen Zhang, Xin Hu, Rong Chen, Zhili Zhang, Liquan Wang, Weidong Wang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00056","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00056","url":null,"abstract":"Dynamic Beam Hopping (DBH) is a crucial technology for adapting to the flexibility of different service configurations in the multi-beam satellite communications market. The conventional beam hopping method, which ignores the intrinsic correlation between decisions, only obtains the optimal solution at the current time, while deep reinforcement learning (DRL) is a typical algorithm for solving sequential decision problems. Therefore, to deal with the DBH problem in the scenario of Differentiated Services (DIFFSERV), this paper designs a multiobjective deep reinforcement learning (MO-DRL) algorithm. Besides, as the demand for the number of beams increases, the complexity of system implementation increase significantly. This paper innovatively proposes a time division multi-action selectionmethod(TD-MASM) tosolvethecurseofdimensionality problem. Under the real condition, the MO-DRL algorithm with the low complexity can ensure the fairness of each cell, improve the throughput to about 5540Mbps, and reduce the delay to about 0.367ms. The simulation results show that when the GA is used to achieve similar effects, the complexity of GA is about 110 times that of the MO-DRL algorithm.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128867084","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}
Pub Date : 2019-10-01DOI: 10.1109/iucc/dsci/smartcns.2019.00025
{"title":"DSCI 2019 Organizing Committee","authors":"","doi":"10.1109/iucc/dsci/smartcns.2019.00025","DOIUrl":"https://doi.org/10.1109/iucc/dsci/smartcns.2019.00025","url":null,"abstract":"","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227379","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}
Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.
近年来，基于新的卷积神经网络架构的提出，在图像去噪方面取得了巨大的进展。一种有效且高效的图像去噪多级网络架构是指将潜在的干净图像从较粗的尺度恢复到较细的尺度，并通过模型的多个层次传递特征。然而，多级网络结构应用的瓶颈在于不能有效地捕获输入图像的多尺度信息，并且在图像去噪任务中忽略了精细到粗的特征融合策略。为了解决这些问题，我们提出了一种多尺度多级shuffle-CNN Via multi- attention (DnM3Net)算法，该算法将多尺度特征提取、精细到粗的特征融合策略和多级关注模块融入到图像去噪任务的新网络架构中。该方法的优点有两方面:(1)解决了多层次网络结构的多尺度信息提取问题，使其对图像去噪任务更加有效和高效。(2)在去噪和细节保留之间进行了较好的权衡，取得了令人印象深刻的性能。通过对合成高斯噪声、灰度和RGB图像的实验验证了该网络的有效性。实验结果表明，与现有的去噪方法相比，DnM3Net有效地提高了图像的定量指标和视觉质量。
{"title":"DnM3Net: Multi-Scale & Multi-Level Shuffle-CNN Via Multi-Level Attention for Image Denoising","authors":"Yue Cao, Jinhe He, Yu Zhang, Gang Lu, Shigang Liu, Xiaojun Wu","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00075","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00075","url":null,"abstract":"Recently, based on novel convolutional neural net-work architectures proposed, tremendous advances have been achieved in image denoising task. An effective and efficient multi-level network architecture for image denoising refers to restore the latent clean image from a coarser scale to finer scales and pass features through multiple levels of the model. Unfortunately, the bottleneck of applying multi-level network architecture lies in the multi-scale information from input images is not effectively captured and the fine-to-coarse feature fusion strategy to be ignored in image denoising task. To solve these problems, we propose a multi-scale & multi-level shuffle-CNN Via multi-level attention (DnM3Net), which plugs the multi-scale feature extraction, fine-to-coarse feature fusion strategy and multi-level attention module into the new network architecture in image denoising task. The advantage of this approach are two-fold: (1) It solve the multi-scale information extraction issue of multi-level network architecture, making it more effective and efficient for the image denoising task. (2) It is impressive performance because the better trade-off between denoising and detail preservation. The proposed novel network architecture is validated by applying on synthetic gaussian noise gray and RGB images. Experimental results show that the DnM3Net effectively improve the quantitative metrics and visual quality compared to the state-of-the-art denoising methods.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126372331","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00062
Zhuojin Pan, Xinlei Wei, Xuwen Dai, Zhen Luo
Aiming at the problem that the semantic segmentation algorithm has poor segmentation results in large depth of field (DOF) images, this paper proposed the concept of depth IoU (dIoU) evaluation standard. This concept based on the effective generalized IoU-loss (GIoU-loss) and the instance-level IoU (iIoU) concept proposed by Cityscapes dataset in the field of target detection. This paper introduced the image depth information into the loss function in the semantic segmentation algorithm. By using dIoU as a evaluation standard, the detection effect of the distant object in the DOF picture will get more weight. It solves the problem of weight reduction caused by the far-reaching effect of the distant object in the traditional evaluation standard.
{"title":"An Evaluation Standard and Loss Function Applied to the Semantic Segmentation of Large Depth of Field Pictures","authors":"Zhuojin Pan, Xinlei Wei, Xuwen Dai, Zhen Luo","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00062","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00062","url":null,"abstract":"Aiming at the problem that the semantic segmentation algorithm has poor segmentation results in large depth of field (DOF) images, this paper proposed the concept of depth IoU (dIoU) evaluation standard. This concept based on the effective generalized IoU-loss (GIoU-loss) and the instance-level IoU (iIoU) concept proposed by Cityscapes dataset in the field of target detection. This paper introduced the image depth information into the loss function in the semantic segmentation algorithm. By using dIoU as a evaluation standard, the detection effect of the distant object in the DOF picture will get more weight. It solves the problem of weight reduction caused by the far-reaching effect of the distant object in the traditional evaluation standard.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126750462","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00070
H. Zhao, Xin Wang, Zhongze Jiao, W. Zeng, J. Dou, Jianglong Yu
This paper presents a hybrid algorithm based on the invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, computational results, preceded by analysis and selection of IW-PSO parameters, show that IW-PSO can improve the search performance. In the other comparative experiment with fixed iteration, the IW-PSO algorithm is compared with various more up-to-date improved PSO procedures appearing in the literature. Comparative results demonstrate that IW-PSO can generate quite competitive quality solution in stability, accuracy and efficiency. As evidenced by the overall assessment based on two kinds of computational experience, IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum.
{"title":"An Improved Particle Swarm Optimization Algorithm Combined with Invasive Weed Optimization","authors":"H. Zhao, Xin Wang, Zhongze Jiao, W. Zeng, J. Dou, Jianglong Yu","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00070","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00070","url":null,"abstract":"This paper presents a hybrid algorithm based on the invasive weed optimization (IWO) and particle swarm optimization (PSO), named IW-PSO. By incorporating the reproduction and spatial dispersal of IWO into the traditional PSO, exploration and exploitation of the PSO can be enhanced and well balanced to achieve better performance. In a set of 15 test function problem, computational results, preceded by analysis and selection of IW-PSO parameters, show that IW-PSO can improve the search performance. In the other comparative experiment with fixed iteration, the IW-PSO algorithm is compared with various more up-to-date improved PSO procedures appearing in the literature. Comparative results demonstrate that IW-PSO can generate quite competitive quality solution in stability, accuracy and efficiency. As evidenced by the overall assessment based on two kinds of computational experience, IW-PSO can effectively obtain higher quality solutions so as to avoid being trapped in local optimum.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"397 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941000","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00082
Baosheng Yin, Longlong Zhang, D. Pei, Yusheng Yan
Traditional web page sorting algorithms can only find the single web page that is the most relevant to keywords, but can not find the relevant website information source. For tackling the problem, we Propose a website information source evaluation algorithm based on comprehensive feature analysis. This algorithm first obtains multiple web pages corresponding to keywords through Baidu and other search engines, then obtains the contents of corresponding website information sources through crawler program and extracts the features, and finally obtains the sorting results of information sources of relevant websites by calculating relevancy combining BM25 algorithm and cosine distance. At the same time, combined with the implicit feedback behavior of users' browsing time, the sorting results could be dynamically adjusted to make the search results personalized. Experiment results show that this approach could make full use of web features, and improve the quality of web source evaluation algorithm by combining the semantic information of web content.
{"title":"A Website Source Evaluation Algorithm Based on Comprehensive Feature Analysis","authors":"Baosheng Yin, Longlong Zhang, D. Pei, Yusheng Yan","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00082","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00082","url":null,"abstract":"Traditional web page sorting algorithms can only find the single web page that is the most relevant to keywords, but can not find the relevant website information source. For tackling the problem, we Propose a website information source evaluation algorithm based on comprehensive feature analysis. This algorithm first obtains multiple web pages corresponding to keywords through Baidu and other search engines, then obtains the contents of corresponding website information sources through crawler program and extracts the features, and finally obtains the sorting results of information sources of relevant websites by calculating relevancy combining BM25 algorithm and cosine distance. At the same time, combined with the implicit feedback behavior of users' browsing time, the sorting results could be dynamically adjusted to make the search results personalized. Experiment results show that this approach could make full use of web features, and improve the quality of web source evaluation algorithm by combining the semantic information of web content.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125638735","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}
This paper focuses on the typical mobile data assets of telecom operators and explores their internal relations. Furthermore, we propose a novel solution model of data fusion for telecom operator's diverse data and a deeper discussion on the scene-driven applications. For the scenario-driven demand, we construct varied data views for scenario-based applications, make an introduction for constructing knowledge graph as well. Eventually, we propose some typical application patterns for the data assets.
{"title":"Sorting and Utilizing of Telecom Operators Data Assets Based on Big Data","authors":"Yongsheng Chi, Xinzhou Cheng, Chuntao Song, Rui Xia, Lexi Xu, Zhi Li","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00130","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00130","url":null,"abstract":"This paper focuses on the typical mobile data assets of telecom operators and explores their internal relations. Furthermore, we propose a novel solution model of data fusion for telecom operator's diverse data and a deeper discussion on the scene-driven applications. For the scenario-driven demand, we construct varied data views for scenario-based applications, make an introduction for constructing knowledge graph as well. Eventually, we propose some typical application patterns for the data assets.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658399","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00034
Han Qi, Zelin Li, Jian Qi, Xinyao Wang, A. Gani, Md. Whaiduzzaman
Edge computing (EC) aims to place partial processing resources at the edge datacenters (EDCs) for terminal devices to improve the delivery of content and applications to end users. Compared with traditional centralized cloud datacenters (CDC), the EDCs are distributed on the edge of the network that closer to terminal devices in geographical location for reducing the delay of data transmission between cloud and terminals, and enhancing the quality of services for the time sensitive applications. Currently, the edge datacenter networks (EDCNs) use the tree-hierarchical architecture which inherits the problems of limited bandwidth capacity and lower server utilization. This requires a new design of scalable and inexpensive EDCN infrastructure which enables high-speed interconnection for exponentially increasing number of terminal devices and provides fault-tolerant and high network capacity. In this paper, we propose a novel architecture call Sierpinski Triangle Based (STB) for EDCN which uses Sierpinski fractal to mitigate throughput bottleneck in aggregate layers as accumulated in tree hierarchical architecture. The results of the experiment show that the STB architecture has higher throughput than both traditional tree-hierarchical and DCell architectures from the scale of 12 to 363 servers without link failure happens.
{"title":"An Improved Sierpinski Fractal Based Network Architecture for Edge Computing Datacenters","authors":"Han Qi, Zelin Li, Jian Qi, Xinyao Wang, A. Gani, Md. Whaiduzzaman","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00034","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00034","url":null,"abstract":"Edge computing (EC) aims to place partial processing resources at the edge datacenters (EDCs) for terminal devices to improve the delivery of content and applications to end users. Compared with traditional centralized cloud datacenters (CDC), the EDCs are distributed on the edge of the network that closer to terminal devices in geographical location for reducing the delay of data transmission between cloud and terminals, and enhancing the quality of services for the time sensitive applications. Currently, the edge datacenter networks (EDCNs) use the tree-hierarchical architecture which inherits the problems of limited bandwidth capacity and lower server utilization. This requires a new design of scalable and inexpensive EDCN infrastructure which enables high-speed interconnection for exponentially increasing number of terminal devices and provides fault-tolerant and high network capacity. In this paper, we propose a novel architecture call Sierpinski Triangle Based (STB) for EDCN which uses Sierpinski fractal to mitigate throughput bottleneck in aggregate layers as accumulated in tree hierarchical architecture. The results of the experiment show that the STB architecture has higher throughput than both traditional tree-hierarchical and DCell architectures from the scale of 12 to 363 servers without link failure happens.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131155382","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}
2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)
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