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IEEE International Conference on Communications Workshops, ICC

作者： Zunming Chen Hongyan Cui Ensen Wu Yuanxin Li Yu Xi State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications China Beijing Key Lab. of Advanced Information Networks Beijing University of Posts and Telecommunications China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Fog computing, a novel computing paradigm, migrates cloud computing to the edge of network and consequently decrease the overhead of process and movement of the large-scale big data. Fog computing is promising to lower communication delay and offload network traffic with the scale of big data increasing in large-scale IoT application, which brings micro-data centers to the network edge, enabling the network edge to perceive, process, storage and calculate the massive data, reducing the amount of transferred data. However, numerous fog nodes at the network edge face an external attack resulting in serious security threats arising from the trusting relationship vulnerability because of the lack of the consideration of the properties of the fog computing system. This paper addresses the design for developing secure distributed data management platform for fog computing in large-scale IoT application, along with a blockchain-based data management implementation of the platform, which approaches the major challenges: how to realize the integration of data security and storage management for fog computing in large-scale IoT application and enrich rational interoperability for interconnected things. Experimental results show that the system works well to empower data provenance and transparency and defend against unauthorized access effectively in fog computing, scales well with loss of the performance of communication and computing maintaining in acceptable range, verifying the efficiency and feasibility of our design to provide privacy, integrity and fine-grained data management for fog computing in large-scale IoT application by introducing the data management platform based upon the blockchain network and smart contracts therein.

关键词： Distributed databases Edge computing Authorization Authentication

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A Study on Multidimensional Medical Data Processing Based on Random Forest

A Study on Multidimensional Medical Data Processing Based on...

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International Conference on Universal Village (UV)

作者： Lifeng Zhang Hongyan Cui Roy E. Welsch State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing Lab. of Advanced Information Networks Beijing China Massachusetts Institute of Technology Cambridge USA

Medical detection is one of the important methods to prevent, diagnose and treat diseases. Under normal conditions, there are many indicators as the basis for diagnosis in medical detection usually. However, in some situations, many detection indicators, useful or important, account for a small proportion, which causes a certain cost. On the other hand, so many indicators also give inexperienced researchers difficulty in making precise decisions on the diagnosis of disease status based on more important indicators. We propose a method of multidimensional data processing based on random forest in this paper, aiming to reduce the difficulties in medical multidimensional data. We proposed a method based on Random Forest according to impact score, to classify multi-dimensional attributes as strong impact and weak impact for disease. The experimental dataset is diabetic retinopathy from the UIC. In the experiment, we designed a method based on random forest according to impact score, to classify multi-dimensional attributes as strong impact and weak impact for disease. The experimental result shows that the higher-score group has better performance in diagnosing diabetic retinopathy.

关键词： Retinopathy Data processing Diabetes Random forests Medical diagnostic imaging

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Quasi-optimal Data Placement for Secure Multi-tenant Data Federation on the Cloud

Quasi-optimal Data Placement for Secure Multi-tenant Data Fe...

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IEEE International Conference on Big Data

作者： Qi Kang Ji Liu Sijia Yang Haoyi Xiong Haozhe An Xingjian Li Zhi Feng Licheng Wang Dejing Dou The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Big Data Lab Baidu Inc. Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

As it is difficult to directly share data among different organizations, data federation brings new opportunities to the data-related cooperation among different organizations by providing abstract data interfaces. With the development of Cloud computing, organizations store data on the Cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either communication cost or time cost, and do not consider the features of jobs that process the data. In this paper, we propose an approach to enable secure data processing on the Cloud with the data from different organizations. The approach consists of a data federation platform for secure data processing on the Cloud named FedCube and a greedy data placement algorithm that creates a plan to store data on the Cloud in order to achieve multiple objectives based on a cost model. The cost model is composed of two objectives, i.e., reducing both monetary cost and execution time. We present an experimental evaluation by comparing our data placement algorithm with the existing methods based on the data federation platform. The experiments show that our proposed algorithm significantly reduce the total cost (up to 69.8%).

关键词： Cloud computing Computational modeling Scalability Organizations Big Data Data processing Data models

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Data placement for multi-tenant data federation on the cloud

arXiv

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arXiv 2021年

作者： Liu, Ji Mo, Lei Yang, Sijia Zhou, Jingbo Ji, Shilei Xiong, Haoyi Dou, Dejing Big Data Lab Baidu Inc. Beijing China School of Automation Southeast University Nanjing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Business Intelligence Lab Baidu Inc. Beijing China Security department Baidu Inc. Beijing China

Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. Data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform named FedCube and a Lyapunov-based data placement algorithm. FedCube enables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches. © 2021, CC BY.

关键词： Cloud computing

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Hyperbolic variational graph neural network for modeling dynamic graphs

arXiv

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arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Zhang, Jiawei Wang, Feiyang Peng, Hao Su, Sen Yu, Philip S. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China IFM Lab Department of Computer Science Florida State University FL United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Department of Computer Science University of Illinois ChicagoIL United States

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets. © 2021, CC BY-NC-SA.

关键词： Normal distribution

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Experimental Performance of the Tri-Polarized MIMO Channel in Urban Microcell at 4.9 GHz

Experimental Performance of the Tri-Polarized MIMO Channel i...

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IEEE International Conference on Communications in China (ICCC)

作者： Zuolong Ying Yuxiang Zhang Pan Tang Zhen Zhang Jianhua Zhang Lei Tian Guangyi Liu Yi Zheng Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

Tri-polarized MIMO system can provide higher capacity, which has been proved theoretically and verified in some real simple indoor channel measurements. In this paper, we did the channel measurements using dipole to form tri-polarized MIMO antennas with 100 MHz bandwidth at 4.9 GHz carrier frequency in Urban Microcell (UMi) scenario. Typical channel propagation characteristics are analysed based on the channel measurement, including cross-polarization discrimination (XPD), correlation coefficient (CC), eigenvalue distributions of channel matrix, and capacity gain (CG). It can be observed that the tri-polarized MIMO channel has three non-zero eigenvalues which support three independent subchannels. It is worthy noted that in UMi scenario, there is a nearly threefold CG in both line-of-sight (LOS) and non-line-of-sight (NLOS) routes by analyzing CC and eigenvalue distributions. This can be well explained by density buildings and low antenna heights in UMi scenario, which leads to rich scattering environment. Therefore, the tri-polarized MIMO is promising to improve the performance in rich-scattering scenarios, e.g., UMi scenario. The results can provide insights for the application of tri-polarized MIMO systems.

关键词： Antenna measurements Scattering Bandwidth Microcell networks Eigenvalues and eigenfunctions Frequency measurement MIMO communication

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Intelligent Reflecting Surfaces Enhanced Federated Learning

Intelligent Reflecting Surfaces Enhanced Federated Learning

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IEEE Globecom Workshops

作者： Wanli Ni Yuanwei Liu Hui Tian State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecommun Beijing China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9781728173078

ISBN: (纸本)9781728173085

This paper investigates the problem of model aggregation for the federated learning aided by multiple intelligent reflecting surfaces (IRSs). Since the local parameters are transmitted over wireless channels, the undesirable propagation error will inevitably deteriorate the performance of global aggregation. The objective of this work is to reduce the signal distortion of over-the-air computation, thus the mean-square-error (MSE) is minimized by optimizing the transmit power at devices, controlling the receive scalar at the base station, and designing the phase shifts at IRSs, subject to the transmit power constraint for devices and the unit-modulus constraint for reflecting elements. To solve this intractable MSE minimization problem (P1), a closed-form expression for transmit power allocation is first derived, and the receive-scaling control subproblem (P2) is addressed by using the semidefinite relaxation. Then, the phase-shifting design subproblem (P3) is reduced to a feasibility-check problem, which is tackled by invoking the penalty method and successive convex approximation. After that, an alternating optimization algorithm is proposed to find a suboptimal solution for the non-linear and non-convex problem. Finally, compare to baselines, simulation results demonstrate that the designed algorithms for multi-IRS enhanced federated learning can converge faster and aggregate model more accurately.

关键词： Wireless communication Simulation Computational modeling Collaborative work Minimization Approximation algorithms Optimization

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Intelligent reflecting surface aided multi-cell NOMA networks

arXiv

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arXiv 2020年

作者： Ni, Wanli Liu, Xiao Liu, Yuanwei Tian, Hui Chen, Yue State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecommun Beijing China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

This paper proposes a novel framework of resource allocation in intelligent reflecting surface (IRS) aided multi-cell non-orthogonal multiple access (NOMA) networks, where a sum-rate maximization problem is formulated. To address this challenging mixed-integer non-linear problem, we decompose it into an optimization problem (P1) with continuous variables and a matching problem (P2) with integer variables. For the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmit power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation and semidefinite relaxation. For the combinational problem (P2), swap matching-based algorithms are proposed to achieve a two-sided exchange-stable state among users, BSs and subchannels. Numerical results are provided for demonstrating that the sum-rate of the NOMA networks is capable of being enhanced with the aid of the IRS. Copyright © 2020, The Authors. All rights reserved.

关键词： Approximation algorithms

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Intelligent Reflecting Surface Aided Multi-Cell NOMA Networks

Intelligent Reflecting Surface Aided Multi-Cell NOMA Network...

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IEEE Globecom Workshops

作者： Wanli Ni Xiao Liu Yuanwei Liu Hui Tian Yue Chen State Key Lab. of Networking and Switching Technology Beijing Univ. of Posts and Telecommun. Beijing China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9781728173078

ISBN: (纸本)9781728173085

This paper proposes a novel framework of resource allocation in intelligent reflecting surface (IRS) aided multi-cell non-orthogonal multiple access (NOMA) networks, where a sum-rate maximization problem is formulated. To address this challenging mixed-integer non-linear problem, we decompose it into an optimization problem (P1) with continuous variables and a matching problem (P2) with integer variables. For the nonconvex optimization problem (P1), iterative algorithms are proposed for allocating transmit power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation and semidefinite relaxation. For the combinational problem (P2), swap matching-based algorithms are proposed to achieve a two-sided exchange-stable state among users, BSs and sub-channels. Numerical results are provided for demonstrating that the sum-rate of the NOMA networks is capable of being enhanced with the aid of the IRS.

关键词： Wireless communication NOMA Upper bound Transforms Throughput Relaxation methods Optimization

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A novel text classification approach based on Word2vec and textrank keyword extraction 4

A novel text classification approach based on Word2vec and t...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Song, Shouyou Wang, Zhongru Xu, Shuai Ni, Shice Xiao, Jia Zhejiang Lab Hangzhou China Institude of Systems Engineering Academy of Military Sciences Beijing China Investigation Technology Center PLCMC Beijing China State Key Laboratory of Networking and Switching Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728145280

In the era of big data, a practical challenge is how to extract the keywords and classify the content of the massive text data efficiently. To address this challenge, through extensive research of existing approaches, we propose a novel text classification approach based on TextRank and Word2vec. In our approach, we integrate the external influence of the Word2vec training model and the location-aware weighting influence of the keywords into the Textrank module. Furthermore, we propose a novel text re-classification algorithm based on the combination of the keywords and KNN. The experiment results show that our approach achieves better accuracy and performance compared to TF-IDF and TextRank algorithm. © 2019 IEEE.

关键词： Extraction

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