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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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QoE-Driven Big Data Management in Pervasive Edge Computing Environment

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Big Data Mining and Analytics 2018年第3期1卷 222-233页

作者： Qianyu Meng Kun Wang Xiaoming He Minyi Guo Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai Jiao Tong University

In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience(QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two ***, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural network(TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.

关键词： Quality-of-Experience(QoE) high-dimensional big data management deep learning pervasive edge computing

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Deep reinforcement learning for smart home energy management

arXiv

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arXiv 2019年

作者： Yu, Liang Xie, Weiwei Xie, Di Zou, Yulong Zhang, Dengying Sun, Zhixin Zhang, Linghua Jiang, Tao Key Laboratory of Broadband Wireless Communication Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 Wuhan National Laboratory for Optoelectronics School of Electronics Information and Communications Huazhong University of Science and Technology Wuhan430074

In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy management

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Heralded amplification of single-photon entanglement with polarization feature

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Frontiers of physics 2018年第5期13卷 241-249页

作者： Yu-Yu Jin Sheng-Xian Qin Hao Zu Lan Zhou Wei Zhong Yu-Bo Sheng School of Science Nanjing University of Posts and Telecommunications Nanjing 210003 China New Energy Technology Engineering Laboratory of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing 210003 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing 210003 China

Heralded noiseless amplification is beneficial in overcoming transmission photon loss in a noisy quantum channel. We propose a single-photon-assisted heralded noiseless amplification protocol of the single- photon entanglement （SPE）, where the single-photon qubit has an arbitrary unknown polarization feature. We focus on both the complete and partial photon loss during the transmission process. After the amplification, the parties can recover the pure less-entangled SPE into a maximally entangled SPE and increase its fidelity. Moreover, the polarization feature of the single-photon qubit will be well preserved and not be leaked. Our protocol can be realized under our current experimental condition. Based on the features above, our protocol may be useful in the quantum secure communication schemes that encode information in the polarization degree of freedom of photons.

关键词： quantum communication single-photon entanglement quantum state amplification polarization feature

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Correction to: Underwater image enhancement using an edge-preserving filtering Retinex algorithm

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Multimedia Tools and Applications 2020年第25期79卷 17279-17279页

作者： Peixian Zhuang Xinghao Ding Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Technology and Engineering Center of Meteorological Sensor Network School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen China

In the original publication, Equations were incorrectly presented. The original article has been corrected.

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Robust and Passive Motion Detection with COTS WiFi Devices

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Tsinghua Science and technology 2017年第4期22卷 345-359页

作者： Hai Zhu Fu Xiao Lijuan Sun Xiaohui Xie Panlong Yang Ruchuan Wang the College of Computer Nanjing University of Posts and Telecommunications the Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education and College of Computer Nanjing University of Posts and Telecommunications the College of Computer Science and Technology University of Science and Technology of China

Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

关键词： device-free passive detection Received Signal Strength（RSS） channel state information MIMO

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Load Feedback-Based Resource Scheduling and Dynamic Migration-Based Data Locality for Virtual Hadoop Clusters in OpenStack-Based Clouds

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Tsinghua Science and technology 2017年第2期22卷 149-159页

作者： Dan Tao Zhaowen Lin Bingxu Wang School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003China Network and Information Center Institute of Network TechnologyScience and Technology on Information Transmission and Dissemination in Communication Networks LaboratoryNational Engineering Laboratory for Mobile Network SecurityBeijing University of Posts and TelecommunicationsBeijing 100876China

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service（Iaa S） cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on Iaa S（DHCI） architecture, which includes four key modules： monitoring,scheduling, Virtual Machine（VM） management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host（s） or rack（s） where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on Open *** experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.

关键词： Hadoop resource scheduling data locality Infrastructure as a Service（Iaas） OpenStack

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Channel measurements and models for high-speed train wireless communication systems in tunnel scenarios: a survey

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Science China(Information Sciences) 2017年第10期60卷 107-123页

作者： Yu LIU Ammar GHAZAL Cheng-Xiang WANG Xiaohu GE Yang YANG Yapei ZHANG Shandong Provincial Key Laboratory of Wireless Communication Technologies Shandong University School of Engineering and Sustainable Development De Montfort University Institute of Sensors Signals and Systems School of Engineering & Physical Sciences Heriot-Watt University Department of Electronics and Information Engineering Huazhong University of Science and Technology Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences

The rapid developments of high-speed trains(HSTs) introduce new challenges to HST wireless communication systems. Realistic HST channel models play a critical role in designing and evaluating HST communication systems. Due to the length limitation, bounding of tunnel itself, and waveguide effect, channel characteristics in tunnel scenarios are very different from those in other HST scenarios. Therefore, accurate tunnel channel models considering both large-scale and small-scale fading characteristics are essential for HST communication systems. Moreover, certain characteristics of tunnel channels have not been investigated sufficiently. This article provides a comprehensive review of the measurement campaigns in tunnels and presents some tunnel channel models using various modeling methods. Finally, future directions in HST tunnel channel measurements and modeling are discussed.

关键词： 5G high-speed train(HST) tunnel scenario tunnel channel measurement tunnel channel model non-stationary statistical property

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An Overview of General Theory of Security

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China Communications 2017年第7期14卷 1-10页

作者： Min Lei Yixian Yang Xinxin Niu Yu Yang Jie Hao Information Security Center Beijing University of Posts and Telecommunications Guizhou University Guizhou Provincial Key Laboratory of Public Big Data Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology

Cyber security lacks comprehensive theoretical guidance. General security theory, as a set of basic security theory concepts, is intended to guide cyber security and all the other security work. The general theory of security aims to unify the main branches of cyber security and establish a unified basic theory. This paper proposal an overview on the general theory of security, which is devoted to constructing a comprehensive model of network security. The hierarchical structure of the meridian-collateral tree is described. Shannon information theory is employed to build a cyberspace security model. Some central concepts of security, i.e., the attack and defense, are discussed and several general theorems on security are presented.

关键词： cyber security network attack and defense meridian-collateral theory

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Computing Simulation of Interactions Between α + β Protein and Janus Nanoparticle 18th

Computing Simulation of Interactions Between α + β Protein...

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18th International Conference on Computational Science, ICCS 2018

作者： Guo, Xinlu Zhao, Xiaofeng Fang, Shuguang Bian, Yunqiang Kang, Wenbin Jiangsu Research and Development Center of Application Engineering Technology of Wireless Sensor System and School of Internet of Things Wuxi Vocational Institute of Commerce Wuxi214153 China Taihu University of Wuxi Wuxi214000 China Shandong Provincial Key Laboratory of Biophysics Institute of Biophysics Dezhou University Dezhou253023 China Bio-X Research Center and Department of Mathmatics and Physics Hubei University of Medicine Shiyan442000 China

ISBN: (纸本)9783319937120

Janus nanoparticles have surfaces with two or more distinct physical properties, allowing different types of chemical properties to occur on the same particle and thus making possible many unique applications. It is necessary to investigate the interaction between proteins and Janus nanoparticles (NPs), which are two typical building blocks for making bio-nano-objects. Here we computed the phase diagrams for an α + β protein(GB1) and Janus NP using coarse-grained model and molecular dynamics simulations, and studied how the secondary structures of proteins, the binding interface and kinetics are affected by the nearby NP. Two phases were identified for the system. In the folded phase, the formation of β-sheets are always enhanced by the presence of NPs, while the formation of α-helices are not sensitive to NPs. The underlying mechanism of the phenomenon was attributed to the geometry and flexibility of the β-sheets. The knowledge gained in this study is useful for understanding the interactions between proteins and Janus NP which may facilitate designing new bio-nanomaterials or devices. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Nanoparticles

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