2024/12/07 更新

写真a

ブン テイ
文 鄭
所属
理工学術院 国際理工学センター(理工学術院)
職名
准教授(任期付)
学位
博士 ( Waseda University )

学歴

  • 2013年09月
    -
    2019年02月

    早稲田大学  

  • 2005年09月
    -
    2009年06月

    武漢大学  

所属学協会

  •  
     
     

    IEEE

  •  
     
     

    電子情報通信学会

研究分野

  • 情報ネットワーク

研究キーワード

  • データサイエンス

  • IoT

  • ブロックチェーン

  • 人工知能

  • 通信ネットワーク

  • 災害管理

  • コンテンツ指向型ネットワーク

▼全件表示

 

論文

  • Compensation of communication latency in remote monitoring systems by video prediction

    Toshio Sato, Yutaka Katsuyama, Xin Qi, Zheng Wen, Kazuhiko Tamesue, Wataru Kameyama, Yuichi Nakamura, Jiro Katto, Takuro Sato

    IEICE Transactions on Communications     1 - 11  2024年

    DOI

  • Estimating Liquid Water Content Using Dual-Frequency Radar and Bayesian Neural Network

    Zheng Wen, Dingjie Peng, Xun Su, Yousuke Ohya, Kazuhiko Tamesue, Hiroyuki Kasai, Wataru Kameyama, Takuro Sato

    Proceedings of the IEEE Radar Conference    2024年

     概要を見る

    Liquid Water Content (LWC) is a pivotal parameter that describes the mass of the water in a cloud in a specified amount of dry air, crucial for research in cloud physics and meteorology. This study explored a novel approach to estimating the LWC of cloud layers from radar observational data by utilizing dual-frequency radar (35 GHz and 95 GHz) in tandem with Bayesian Neural Network (BNN). The dual-frequency radar utilizes differential attenuation between the two distinct frequencies to directly assess the LWC in clouds, with the variance proportionate to the overall LWC in the surveyed volume. However, due to atmospheric perturbations and other factors, standalone radar observations might not suffice for high-precision LWC evaluations. To address this, we incorporated BNN, capable of handling the inherent uncertainties in radar data and offering a more accurate estimation for LWC. Preliminary results demonstrate that the combination of dual-frequency radar and BNN can effectively assess the LWC of cloud layers, showcasing superior accuracy and resolution compared to conventional methods. This methodology provides a potent tool for a deeper understanding of cloud physical processes and further refinement of climate models.

    DOI

  • GNSS Spoofing Detection Using Multiple Sensing Devices and LSTM Networks

    Xin QI, Toshio SATO, Zheng WEN, Yutaka KATSUYAMA, Kazuhiko TAMESUE, Takuro SATO

    IEICE Transactions on Communications   E106.B ( 12 ) 1372 - 1379  2023年12月

     概要を見る

    The rise of next-generation logistics systems featuring autonomous vehicles and drones has brought to light the severe problem of Global navigation satellite system (GNSS) location data spoofing. While signal-based anti-spoofing techniques have been studied, they can be challenging to apply to current commercial GNSS modules in many cases. In this study, we explore using multiple sensing devices and machine learning techniques such as decision tree classifiers and Long short-term memory (LSTM) networks for detecting GNSS location data spoofing. We acquire sensing data from six trajectories and generate spoofing data based on the Software-defined radio (SDR) behavior for evaluation. We define multiple features using GNSS, beacons, and Inertial measurement unit (IMU) data and develop models to detect spoofing. Our experimental results indicate that LSTM networks using ten-sequential past data exhibit higher performance, with the accuracy F1 scores above 0.92 using appropriate features including beacons and generalization ability for untrained test data. Additionally, our results suggest that distance from beacons is a valuable metric for detecting GNSS spoofing and demonstrate the potential for beacon installation along future drone highways.

    DOI

  • LSTM-Based GNSS Spoofing Detection for Drone Formation Flights

    Zheng Wen, Xin Qi, Toshio Sato, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Jiro Katto, Takuro Sato

    IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society    2023年10月

     概要を見る

    In the rapidly evolving logistics industry, drones are becoming indispensable for automated delivery operations. As drone traffic escalates, formation flying is being explored to enhance operational control and increase drone density, thereby reducing the space they occupy. Drones typically rely on Global Navigation Satellite System (GNSS) positioning information for autonomous flight. However, civilian-grade GNSS devices are susceptible to spoofing via Software Defined Radio (SDR), posing significant challenges. In this study, we introduce a novel approach to detect GNSS spoofing by leveraging the multiple GNSS information available from each drone during formation flight. Our investigations, involving two GNSS receivers spoofed by an SDR, reveal that spoofing results in a calculated distance between two receivers that is smaller than the actual value. Capitalizing on this characteristic, we designed simulations of formation flights involving two and five drones. We also developed a GNSS spoofing detection method using the Long Short-Term Memory (LSTM) network. The performance of our spoof detection method was evaluated using simulation data. The results demonstrate that using multiple GNSS data from drones in formation flight significantly enhances performance, achieving an F1 score of 0.96 or higher. This study underscores the potential of our proposed method in improving the security and reliability of drone operations.

    DOI

  • A Machine Learning-based Non-precipitating Clouds Estimation for THz Dual-Frequency Radar

    Kazuhiko Tamesue, Zheng Wen, Shotaro Yamaguchi, Hiroyuki Kasai, Wataru Kameyama, Toshio Sato, Yutaka Katsuyama, Takuro Sato, Takeshi Maesaka

    IEEE Conference on Antenna Measurements and Applications, CAMA     376 - 380  2023年

     概要を見る

    Accurate measurement of non-precipitable clouds is important for early prediction of heavy rainfall disasters caused by extreme weather events. However, microwave cloud radar cannot observe the early stages of cloud development from non-precipitation clouds (cumulus) to cumulonimbus. In this paper, we propose a terahertz dual-frequency cloud radar using 150 GHz and 95 GHz bands to detect cloud particles in cumulus smaller than 10 μm. Using a dataset generated by the ITU-R radio propagation model, we estimate the liquid water content of non-precipitation clouds and water vapor content in atmospheric gases, respectively, by using a machine learning-based approach. The effectiveness of using the dual wavelength ratio as an explanatory variable is examined.

    DOI

  • A Predictive Approach for Compensating Transmission Latency in Remote Robot Control for Improving Teleoperation Efficiency

    Yutaka Katsuyama, Toshio Sato, Zheng Wen, Xin Qi, Kazuhiko Tamesue, Wataru Kameyama, Yuichi Nakamura, Takuro Sato, Jiro Katto

    Proceedings - IEEE Global Communications Conference, GLOBECOM     6934 - 6939  2023年

     概要を見る

    Transmission latency presents a significant challenge when operating remote equipment, such as a robotic arm. To address this, we developed a platform and conducted experiments to reduce transmission latency to near-zero levels. These experiments employed Long Short-Term Memory (LSTM) to anticipate future motion trends, leveraging both the controller's movement variables and Electromyography (EMG) data from the operator's arm muscles. Our findings indicate the potential to decrease transmission latency by approximately 500ms. Additionally, our research confirms a direct correlation between prediction accuracy and the brevity of prediction time, suggesting that shorter prediction times yield more accurate results when using EMG. In the context of video transmission for a remotely located robotic arm, we applied video prediction techniques using the Predictive Coding Network (PredNet) to counter network latency. Our results suggest that these predictive methods can effectively compensate for a latency period of 300ms, thereby highlighting their potential for reducing transmission latency in remote robotic operations.

    DOI

  • Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

    Peng Zhao, Yongxin Zhang, Qiaozhi Hua, Haipeng Li, Zheng Wen

    CMES - Computer Modeling in Engineering and Sciences   134 ( 2 ) 957 - 979  2023年

     概要を見る

    Bio-inspired computer modelling brings solutions from the living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, high-energy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configuration within the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

    DOI

  • Design and Implementation of Ledger-Based Points Transfer System for IoT Devices in LPWAN

    Xin Qi, Keping Yu, Toshio Sato, Kouichi Shibata, Isao Konno, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    Wireless Communications and Mobile Computing   2022   1 - 13  2022年08月

     概要を見る

    Distributed ledger technology is becoming popular these days because of its high confidentiality, decentralization, and nontampering. It is suitable for replacing centralized security disadvantaged point transfer systems. Low-power wide area network (LPWAN) is capable for long-range communication with low-power consumption. The iconic features like wide area coverage and long battery-powered duration make it best to combine with large-scale IoT application deployment. In both industry and academic field, such combination of LPWAN and point transfer system is highly attended. However, the ledger management system generates too much data that low-bandwidth network such as LPWAN can hardly handle; meanwhile, the processing power’s requirement for small IoT devices is challenging. Towards addressing these issues, we design a packet transmission optimizing mechanism for a ledger-based point transfer system (LPTS) in LPWAN to reduce overall data traffic and build a simulator to evaluate its performance. Moreover, we have implemented the system and evaluated in field experiment.

    DOI

  • Biomedical sensor image segmentation algorithm based on improved fully convolutional network

    Hong'an Li, Jiangwen Fan, Qiaozhi Hua, Xinpeng Li, Zheng Wen, Meng Yang

    Measurement: Journal of the International Measurement Confederation   197  2022年06月

     概要を見る

    Effective use of biomedical sensor image can help locate diseased tissues and tissue structures clearly presented, and clinical diagnosis and treatment can assist doctors in making appropriate treatment plans. In order to efficiently process the images acquired by biomedical sensors, we propose a biomedical sensor image segmentation method with improved fully convolutional network, which firstly extracts the local spatial and frequency domain information of the images acquired by biomedical sensors and enhances the texture information of the images. Secondly, the background interference is suppressed by increasing the target region weights to refine the processing of the image and enhance the features of the image while reducing the information redundancy. It is experimentally proved that the model in this paper can effectively reduce the phenomenon of cell adhesion after image segmentation, has better segmentation effect and segmentation accuracy, and can more effectively utilize the images acquired by biomedical sensors.

    DOI

  • AI-Based W-Band Suspicious Object Detection System for Moving Persons: Two-Stage Walkthrough Configuration and Recognition Optimization

    Zheng Wen, Keping Yu, Xin Qi, Toshio Sato, San Hlaing Myint, Kazuhiko Tamesue, Yutaka Katsuyama, Hironori Dobashi, Yasushi Murakami, Ikuo Koyama, Kiyohito Tokuda, Wataru Kameyama, Takuro Sato

    Wireless Communications and Mobile Computing   2022   1 - 16  2022年06月

     概要を見る

    In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners’ ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.

    DOI

  • A Data Management Model for Intelligent Water Project Construction Based on Blockchain

    Zhoukai Wang, Kening Wang, Yichuan Wang, Zheng Wen

    Wireless Communications and Mobile Computing   2022  2022年

     概要を見る

    The engineering construction-related data is essential for evaluating and tracing project quality in industry 4.0. Specifically, the preservation of the information is of great significance to the safety of intelligent water projects. This paper proposes a blockchain-based data management model for intelligent water projects to achieve standardization management and long-term preservation of archives. Based on studying the concrete production process in water conservancy project construction, we first build a behavioral model and the corresponding role assignment strategy to describe the standardized production process. Then, a distributed blockchain data structure for storing the production-related files is designed according to the model and strategy. In addition, to provide trust repository and transfer on the construction data, an intelligent keyless signature based on edge computing is employed to manage the data's entry, modification, and approval. Finally, standardized and secure information is uploaded onto the blockchain to supervise intelligent water project construction quality and safety effectively. The experiments showed that the proposed model reduced the time and labor cost when generating the production data and ensured the security and traceability of the electronic archiving of the documents. Blockchain and intelligent keyless signatures jointly provide new data sharing and trading methods in intelligent water systems.

    DOI

  • Efficient Multibit Function Encryption for Data Security in Internet of Things

    Qihong Chen, Mingming Jiang, Yuyan Guo, Dongbing Zhang, Weina Jia, Wen Zheng

    Security and Communication Networks   2022  2022年

     概要を見る

    The development of the Internet of Things (IoT) has been facing severe security threats, and the security and fine-grained access control of data in the IoT is one of the security problems that urgently need to deal with. Attribute-based encryption (ABE) schemes over lattice can not only achieve fine-grained access control but also resist quantum attacks. However, most schemes are single-bit encryption, which is inefficient. In this study, a multibit inner product predicate encryption (PE) scheme over lattice is proposed, which effectively expands the plaintext space. The scheme can realize multibit attribute-based encryption with the hidden access structure for data security in the IoT and support And-gate operation in the access structure with multiattribute. The fine-grained access control of ciphertext data can be realized under the condition of ensuring data privacy. The security of the scheme is based on LWE problem, and it can resist quantum attacks, that is, CPA security under the standard model.

    DOI

  • Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems

    Qianqian Liu, Xiaoyan Zhang, Qiaozhi Hua, Zheng Wen, Haipeng Li

    Wireless Communications and Mobile Computing   2022  2022年

     概要を見る

    With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.

    DOI

  • K Nearest Neighbor Similarity Join Algorithm on High-Dimensional Data Using Novel Partitioning Strategy

    Youzhong Ma, Qiaozhi Hua, Zheng Wen, Ruiling Zhang, Yongxin Zhang, Haipeng Li

    Security and Communication Networks   2022  2022年

     概要を見る

    k nearest neighbor similarity join on high-dimensional data has broad applications in many fields; several key challenges still exist for this task such as "curse of dimensionality"and large scale of the dataset. A new dimensionality reduction scheme is proposed by using random projection technique, then we design two novel partition strategies, including equal width partition strategy and distance split tree-based partition strategy, and finally, we propose k nearest neighbor join algorithm on high-dimensional data based on the above partition strategies. We conduct comprehensive experiments to test the performance of the proposed approaches, and the experimental results show that the proposed methods have good effectiveness and performance.

    DOI

  • An image watermark removal method for secure internet of things applications based on federated learning

    Hongan Li, Guanyi Wang, Qiaozhi Hua, Zheng Wen, Zhanli Li, Ting Lei

    Expert Systems    2022年

     概要を見る

    Watermark adding is one of the important means for image security and privacy protection in Internet of things (IOT) applications based on federated learning. It is often inseparable from adversarial training with watermark removal algorithms. The effect of watermark removal algorithms will directly affect the final result of watermark addition. However, the existing watermark removal algorithms have drawbacks such as incomplete image watermark removal, poor image quality after watermark removal, large demand for training data, and incorrect filling, which seriously affects the development of image information security and privacy protection in IOT applications based on federated learning. To solve the above problems, this paper proposes an improved image watermark removal convolutional network model based on deep image prior. First, we improve the U-Net network model, using six downsamping layers and six deconvolution layers combined with deep image prior method to reduce the loss of details and perceive high-level features, thereby improving the ability of the network to extract high-level features of the image. In addition, we design a new type of loss function which is called stair loss, and add L1 loss and perception loss to establish new constraints. In order to verify the effectiveness of our method, a comprehensive experimental comparison was conducted on the public dataset PASCAL VOC 2012 in the same experimental environment with CGAN and the deep prior method. The experimental results show that the improved model combined with the deep image prior method can extract the high-level feature information and can directly remove the watermark from the picture without pretraining the network, the L1 loss and perceptual loss can better retain the image structure information and speed up the watermark removal of the model, the stair loss corrects the final output more accurately by correcting the output of each layer; our method improves the learning ability of the model, and under the condition of the same training time, the image quality after watermark removal is higher, and the final watermark removal result is better, which is more suitable for distributed structure of IoT application based on federated learning.

    DOI

  • Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

    Xiufeng Li, Zheng Wen, Qiaozhi Hua

    Wireless Communications and Mobile Computing   2022  2022年

     概要を見る

    Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.

    DOI

  • Visual Attention and Motion Estimation-Based Video Retargeting for Medical Data Security

    Qingfang Liu, Baosheng Kang, Qiaozhi Hua, Zheng Wen, Haipeng Li

    Security and Communication Networks   2022  2022年

     概要を見る

    Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients' condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user's attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.

    DOI

  • 300GHz Indoor Propagation Measurement, Simulation and Characterization

    Kazuhiko Tamesue, Seiji Nishi, San Hlaing Myint, Zheng Wen, Toshio Sato, Takuro Sato, Tetsuya Kawanishi

    2022 IEEE 9th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2022     380 - 385  2022年

     概要を見る

    This paper presents radio propagation characteristics in a typical indoor space in a 300 GHz Sub-THz broadband channel. We clarified path loss and cross-polarization characteristics in multiple spaces using a linearly polarized horn antenna with 24.6 dBi (E-plane beamwidth 4.5°) and 8.7 dBi (E-plane beamwidth 35°) and a newly developed RHCP circularly polarized patch array antenna which has a gain of 3.4 dBi (E-plane beamwidth 10°) at 300 GHz. We also clarified the effect of shielding by the human body. Furthermore, a comprehensive analysis using ray tracing confirms the accurate agreement of the root mean square delay spread.

    DOI

  • Optimizing Packet Transmission for Ledger-Based Points Transfer System in LPWAN: Solutions, Evaluation and Standardization

    Xin Qi, Keping Yu, Toshio Sato, Kouichi Shibata, Eric Brigham, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    2021 ITU Kaleidoscope: Connecting Physical and Virtual Worlds (ITU K)    2021年12月

     概要を見る

    Low Power Wide Area Network (LPWAN) is a long-range low-power wireless communication network. Its features, such as wide network coverage and low power consumption of terminals, make it suitable for large-scale deployment of IoT applications. The points transfer system, especially points transfer system in LPWAN, as a typical third-party payment application, is being closely attended by both industry and academia. Recent studies have shown that distributed ledger technology, because ofits characteristics such as high confidentiality, non-tampering, and decentralization, is a good solution to problems such as low-security performance due to centralized storage for a points transfer system. However, the distributed ledger will generate a large amount of data traffic in recording the transactions of network participants, which is a challenge for resource-constrained IoT devices. To address these issues, we propose an optimized packet transmission mechanism for a ledger-based points transfer system in LPWAN. Simulation results show that our proposed mechanism can well reduce the packet transmission ofthe whole system and meet the requirements of LPWAN. Moreover, we update the reader with information about distributed ledger and standardization-related activities in this paper.

    DOI

  • Ledger-based Points Transfer System in LPWAN: From Disaster Management Aspect

    Xin Qi, Keping Yu, Toshio Sato, Kouichi Shibata, Eric Brigham, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    2021 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)     150 - 155  2021年12月

     概要を見る

    Low Power Wide Area Network (LPWAN) is an Internet of things (IoT) network layer technology that has emerged in recent years for long-range and low-power communication needs in IoT. Its low-bandwidth, low-power, long-range and mass-connected IoT application features can be well applied to the points transfer system. However, the traditional centralized points transfer system faces many problems such as centralization, high computational requirement of nodes, and low robustness which are difficult to be widely used. In order to solve these problems, we propose a distributed ledger-based points transfer system in LPWAN and analyze the system robustness from the disaster management aspect. The simulation results show that our proposed system can still have strong robustness under extreme disaster situations and ensure the safe and efficient operation of the whole system.

    DOI

  • Deep Learning Based Concealed Object Recognition in Active Millimeter Wave Imaging

    San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Kazuhiko Tamesue, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

    2021 IEEE Asia-Pacific Microwave Conference (APMC)   2021-November   434 - 436  2021年11月

     概要を見る

    In application related to public security check system, passive and active imaging of millimeter wave still faces critical challenges in providing high resolution quality images. Improving the detection, localization, and recognition accuracy of concealed object detection systems is very challenging due to the lack of a dataset of millimeter wave images with good resolution. Although previous studies proposed artificial intelligence-based concealed object recognition systems, improving accuracy remains a critical challenge. Therefore, in this paper, we propose two kinds of training dataset generation methods based on the proposed active millimeter wave imaging (AMWI) approaches presented in our previous work to improve the accuracy of convolutional neural networks (CNN)-based concealed object recognition systems. First, a depth-based training dataset generation method and a distance-based training dataset generation method are proposed for specular images and nonspecular images. Finally, a CNN-based concealed object recognition system is proposed by using generated active millimeter wave images and interferometer active images to improve the recognition accuracy.

    DOI

  • Blockchain–a promising solution to internet of things: A comprehensive analysis, opportunities, challenges and future research issues

    Anup Kumar Paul, Xin Qu, Zheng Wen

    Peer-to-Peer Networking and Applications   14 ( 5 ) 2926 - 2951  2021年09月

     概要を見る

    Blockchain (BC) technology is a promising answer for providing security and ensuring protection in a distributed way. It has transformed the digital currency platform with the revolutionary crypto-currency known as Bitcoin. From a theoretical point of view, a BC is a distributed series of blocks linked with each other where every block is an immutable record of some form of data exchange occurring in a network. Recently, numerous literature reviews and research articles have been published on the combination of BC with the Internet of Things (IoT). However, they are restricted to shallow conversations of specialized possibilities, and not many of them have an exhaustive investigation of the difficulties in creating BC for IoT at implementation levels. Within this frame of reference, the BC is viewed as the important link for creating a genuinely secure, decentralized, and trustless platform designed to be used in IoT and, in this literature review, we mean to figure out an intelligent and thorough image of the present progressive research works toward this path. We begin with the security vulnerabilities presented by the different IoT layers. Then we focused on the central working principle of BC and how BC-based frameworks accomplish the feature of security, decentralization, and auditability. From that point, we focused on the security solutions to several vulnerabilities presented by the IoT with centralized architecture, accompanied by ongoing industrial advancement and academic research to comprehend these difficulties and successfully use BC to ensure a secure platform without any centralized management in IoT.

    DOI

  • Content-oriented Multicamera Trajectory Forecasting Surveillance Network System

    Xin Qi, Toshio Sato, Keping Yu, San Hlaing Myint, Yutaka Katsuyama, Kazuhiko Tamesue, Kiyohito Tokuda, Zheng Wen, Takuro Sato

    2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)   2021-August   17 - 22  2021年08月

     概要を見る

    To reduce safety violations in wide-area ranges, there is a need for highly functional multicamera surveillance systems. We introduce a multicamera trajectory forecasting surveillance network system based on a content-oriented suspicious object network system. This system uses multiple cameras in detection and recognition to track persons among different areas and is capable of retracking people. Each camera node has a processing unit and uses information-centric networking technology to build a content-oriented IoT network. We use field-recorded data to support the simulation, and the evaluation result indicates that our trajectory forecasting method is more efficient than conventional surveillance systems.

    DOI

  • Position Estimation of Pedestrians in Surveillance Video Using Face Detection and Simple Camera Calibration

    Toshio Sato, Xin Qi, Keping Yu, Zheng Wen, Yutaka Katsuyama, Takuro Sato

    2021 17th International Conference on Machine Vision and Applications (MVA)    2021年07月

     概要を見る

    Pedestrian position estimation in videos is an important technique for enhancing surveillance system applications. Although many studies estimate pedestrian positions by using human body detection, its usage is limited when the entire body expands outside of the field of view. Camera calibration is also important for realizing accurate position estimation. Most surveillance cameras are not adjusted, and it is necessary to establish a method for easy camera calibration after installation. In this paper, we propose an estimation method for pedestrian positions using face detection and anthropometric properties such as statistical face lengths. We also investigate a simple method for camera calibration that is suitable for actual uses. We evaluate the position estimation accuracy by using indoor surveillance videos.

    DOI

  • Deep-learning-empowered 3D reconstruction for dehazed images in IoT-Enhanced smart cities

    Jing Zhang, Xin Qi, San Hlaing Myint, Zheng Wen

    Computers, Materials and Continua   68 ( 2 ) 2807 - 2824  2021年04月

     概要を見る

    With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities' information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a fine transmission image deep convolutional regression network (FT-DCRN) dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image. The DCRN is used to obtain the coarse transmission image, which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network. The fine transmission image is obtained by refining the coarse transmission image using a guided filter. The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image. Second, we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction. An advanced relaxed iterative fine matching based on the structure from motion (ARI-SFM) algorithm is proposed. The ARISFM algorithm, which obtains the fine matching corner pairs and reduces the number of iterations, establishes an accurate one-to-one matching corner relationship. The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms. In addition, the ARI-SFM algorithm guarantees the precision and improves the efficiency.

    DOI

  • Congestion-aware suspicious object detection system using information-centric networking

    Xin Qi, Toshio Sato, Keping Yu, Zheng Wen, San Hlaing Myint, Yutaka Katsuyama, Kiyohito Tokuda, Takuro Sato

    2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021    2021年01月

     概要を見る

    Deadly diseases and terrorist attacks are greatly threatening human safety, which challenges global security. To address this issue, urban surveillance systems are being applied at a rapid pace with mature but inefficient solutions in large scale networks. When a surveillance network is managing the data generated from multiple edge nodes, it is easy to create congestions due to concentrated data traffic and inefficient data delivery mechanism. In parallel, 5G technology, cope with explosive mobile data traffic growth and massive device connections, can realize a true 'Internet of Everything' and build the social and economical digital transformation. In this paper, in the context of 5G technology, we propose an Information-Centric Networking (ICN) surveillance system based on our designed Suspicious Object Network System (SONS) over the concept of next-generation networking. In this solution, the edge nodes in the network distribute the computing and data storage requirements. We first describe the current surveillance issues and our proposed system architecture. Then we use simulation to verify and evaluate the system performance between legacy all-to-one centralized surveillance system and ICN based decentralized surveillance system.

    DOI

  • EDKSAP : Efficient Double-Key Stealth Address Protocol in Blockchain

    Cong Feng, Liang Tan, Huan Xiao, Xin Qi, Zheng Wen, Yang Liu

    Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021     1196 - 1201  2021年

     概要を見る

    To achieve user identities protection in transaction, the stealth address is introduced. However, double-key stealth address protocol (DKSAP) requires the receiver to continuously calculate and determine whether it is the real receiver of the transaction until it detected a transaction that matches. In this process, the receiver needs to perform many time-consuming elliptic curve scalar multiplication operations, which limits the application of DKSAP in low-performance devices, such as mobile terminals, IoT devices, etc. This paper proposes an efficient double-key stealth address protocol based on bilinear mapping, EDKSAP. Senders calculate the transaction temporary output address and receivers perform the verification calculation using bilinear mapping, which achieves the purpose of improving computing performance. Finally, we verify the feasibility and applicability of the protocol through experiments. In addition, the experimental results show that the computational performance of EDKSAP is significantly improved compared to DKSAP.

    DOI

  • Ledger-based Points Transfer System in LPWAN: From Disaster Management Aspect.

    Xin Qi 0002, Keping Yu, Toshio Sato, Kouichi Shibata, Eric Brigham, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    ICT-DM     150 - 155  2021年

     概要を見る

    Low Power Wide Area Network (LPWAN) is an Internet of things (IoT) network layer technology that has emerged in recent years for long-range and low-power communication needs in IoT. Its low-bandwidth, low-power, long-range and mass-connected IoT application features can be well applied to the points transfer system. However, the traditional centralized points transfer system faces many problems such as centralization, high computational requirement of nodes, and low robustness which are difficult to be widely used. In order to solve these problems, we propose a distributed ledger-based points transfer system in LPWAN and analyze the system robustness from the disaster management aspect. The simulation results show that our proposed system can still have strong robustness under extreme disaster situations and ensure the safe and efficient operation of the whole system.

    DOI

  • Performance evaluation of online machine learning models based on cyclic dynamic and feature-adaptive time series

    Ahmed Salih Al-Khaleefa, Rosilah Hassan, Mohd Riduan Ahmad, Faizan Qamar, Zheng Wen, Azana Hafizah Mohd Aman, Keping Yu

    IEICE Transactions on Information and Systems   E104D ( 8 ) 1172 - 1184  2021年

     概要を見る

    Machine learning is becoming an attractive topic for researchers and industrial firms in the area of computational intelligence because of its proven effectiveness and performance in resolving real-world problems. However, some challenges such as precise search, intelligent discovery and intelligent learning need to be addressed and solved. One most important challenge is the non-steady performance of various machine learning models during online learning and operation. Online learning is the ability of a machine-learning model to modernize information without retraining the scheme when new information is available. To address this challenge, we evaluate and analyze four widely used online machine learning models: Online Sequential Extreme Learning Machine (OSELM), Feature Adaptive OSELM (FA-OSELM), Knowledge Preserving OSELM (KP-OSELM), and Infinite Term Memory OSELM (ITM-OSELM). Specifically, we provide a testbed for the models by building a framework and configuring various evaluation scenarios given different factors in the topological and mathematical aspects of the models. Furthermore, we generate different characteristics of the time series to be learned. Results prove the real impact of the tested parameters and scenarios on the models. In terms of accuracy, KP-OSELM and ITM-OSELM are superior to OSELM and FA-OSELM. With regard to time efficiency related to the percentage of decreases in active features, ITM-OSELM is superior to KP-OSELM.

    DOI

  • Image super-resolution reconstruction for secure data transmission in Internet of Things environment

    Hongan Li, Qiaoxue Zheng, Wenjing Yan, Ruolin Tao, Xin Qi, Zheng Wen

    Mathematical Biosciences and Engineering   18 ( 5 ) 6652 - 6671  2021年  [国際誌]

     概要を見る

    The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.

    DOI PubMed

  • Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems

    Hong An Li, Qiaoxue Zheng, Xin Qi, Wenjing Yan, Zheng Wen, Na Li, Chu Tang

    Computational Intelligence and Neuroscience   2021   8387382 - 8387382  2021年  [国際誌]

     概要を見る

    Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

    DOI PubMed

  • 3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

    Jing Zhang, Dan Li, Qiaozhi Hua, Xin Qi, Zheng Wen, San Hlaing Myint

    IEEE Access   9   15170 - 15180  2021年

     概要を見る

    Edge computing can provide many key functions without connecting to centralized servers, which enables remote areas to obtain real-time medical diagnoses. The combination of edge computing and Internet of things (IoT) devices can send remote patient data to the hospital, which will help to more effectively address long-term or chronic diseases. CT images are widely used in the diagnosis of clinical diseases, and their characteristics are an important basis for pathological diagnosis. In the CT imaging process, speckle noise is caused by the interference of ultrasound on human tissues, and its component information is complex. To solve these problems, we propose a 3D reconstruction method for noisy CT images in the IoT using edge computing. First, we propose a multi-stage feature extraction generative adversarial network (MF-GAN) denoising algorithm. The generator of MF-GAN adopts the multi-stage feature extraction, which can ensure the reconstruction of the image texture and edges. Second, we apply the denoised images generated from the MF-GAN method to perform the 3D reconstruction. A marching cube (MC) algorithm based on regional growth and trilinear interpolation (RGT-MC) is proposed. With the idea of regional growth, all voxels containing iso-surfaces are selected and calculated, which accelerates the reconstruction efficiency. The intersection point of the voxel and iso-surface is calculated by the trilinear interpolation algorithm, which effectively improves the reconstruction accuracy. The experimental results show that MF-GAN has a better denoising effect than other algorithms. Compared to other representative 3D algorithms, the RGT-MC algorithm greatly improves the efficiency and precision.

    DOI

  • Communication-Based Book Recommendation in Computational Social Systems

    Long Zuo, Shuo Xiong, Xin Qi, Zheng Wen, Yiwen Tang

    Complexity   2021  2021年

     概要を見る

    This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users' neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.

    DOI

  • A Lightweight Ledger-Based Points Transfer System for Application-Oriented LPWAN

    Keping Yu, Kouichi Shibata, Takanori Tokutake, Rikiya Eguchi, Taiki Kondo, Yusuke Maruyama, Xin Qi, Zheng Wen, Toshio Sato, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020     1972 - 1978  2020年12月

     概要を見る

    Along with the rapid development of IoT technology, Low power wide area network (LPWAN) has become the primary technology for IoT access today due to its characteristics of low cost, low power consumption, long-distance, and mass connections. At the same time, the points transfer system, as a typical third-party payment application, is attracting more and more extensive attention from academia and industry. Therefore, the research and development of a points transfer system for LPWAN are of great practical importance. However, the current points transfer systems often face problems such as centralization, high requirements on node computing power, and low robustness, which are difficult to adapt to the development of IoT. To address these problems, we propose a lightweight ledger-based points transfer system for application-oriented LPWAN. The system enables the points transfer between tags in LPWAN, where both tags and nodes are limited. Furthermore, it can be utilized even in disaster situations. Experimental results show that our proposed system is intensely robust and has a lower processing time than Bitcoin-like systems to cope with LPWAN's requirement.

    DOI

  • AI-Based W-Band Suspicious Object Detection System for Moving Persons Using GAN: Solutions, Performance Evaluation and Standardization Activities

    Yutaka Katsuyama, Keping Yu, San Hlaing Myint, Toshio Sato, Zheng Wen, Xin Qi

    2020 ITU Kaleidoscope: Industry-Driven Digital Transformation, ITU K 2020    2020年12月

     概要を見る

    With the intensification of conflicts in different regions, the W-band suspicious object detection system is an essential security means to prevent terrorist attacks and is widely used in many crucial places such as airports. Because artificial intelligence can perform highly reliable and accurate services in the field of image recognition, it is used in suspicious object detection systems to increase the recognition rate for suspicious objects. However, it is challenging to establish a complete suspicious object database, and obtaining sufficient millimeter-wave images of suspicious objects from experiments for AI training is not realistic. To address this issue, this paper verifies the feasibility to generate a large number of millimeter-wave images for AI training by generative adversarial networks. Moreover, we also evaluate the factors that affect the AI recognition rate when the original images used for CNN training are insufficient and how to increase the service quality of AI-based W-band suspicious object detection systems for moving persons. In parallel, all the international standardization organizations have been collectively advancing the novel technologies of AI. We update the reader with information about AI research and standardization related activities in this paper.

    DOI

  • A geometry-based non-stationary wideband mimo channel model and correlation analysis for vehicular communication systems

    Suqin Pang, Fan Bai, Di Zhang, Zheng Wen, Takuro Sato

    Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020     496 - 501  2020年12月

     概要を見る

    In this paper, we propose a novel two-dimensional (2D) non-stationary geometry-based stochastic model (GBSM) for wideband multiple-input multiple-output (MIMO) base station-to-vehicle (B2V) channels. The proposed model combines one-ring and multiple ellipses with time-variant parameters, which can capture the channel non-stationary characteristics more precisely. The corresponding stochastic simulation model is then developed with finite number of effective scatterers. In addition, the birth-death process is applied to determine the number of ellipses in the proposed model at different time instants. Afterwards, the time-variant parameters and time-variant space cross-correlation functions (CCFs) are derived and analyzed. The impact of different parameters on the space CCFs such as vehicle traffic density (VTD) is investigated. Numerical results illustrate that the simulation model has great agreement with the reference model at different time instants, which indicates the correctness of our derivations.

    DOI

  • Knowledge, attitudes, and practices toward COVID-19 among university students in Japan and associated factors: An online cross-sectional survey

    Asuka Hatabu, Xinhua Mao, Yi Zhou, Norihito Kawashita, Zheng Wen, Mikiko Ueda, Tatsuya Takagi, Yu Shi Tian

    PLoS ONE   15 ( 12 December ) e0244350  2020年12月  [国際誌]

     概要を見る

    The coronavirus disease (COVID-19) pandemic has greatly altered peoples’ daily lives, and it continues spreading as a crucial concern globally. Knowledge, attitudes, and practices (KAP) toward COVID-19 are related to individuals’ adherence to government measures. This study evaluated KAP toward COVID-19 among university students in Japan between May 22 and July 16, 2020, via an online questionnaire, and it further investigated the associated determining KAP factors. Among the eligible respondents (n = 362), 52.8% were female, 79.0% were undergraduate students, 32.9% were students whose major university subjects were biology-related, 35.4% were from the capital region, and 83.7% were Japanese. The overall KAP of university students in Japan was high. All respondents (100%) showed they possessed knowledge on avoiding enclosed spaces, crowded areas, and close situations. Most respondents showed a moderate or higher frequency of washing their hands or wearing masks (both at 96.4%). In addition, 68.5% of respondents showed a positive attitude toward early drug administration. In the logistic regressions, gender, major subjects, education level, nationality, residence, and psychological factors (private self-consciousness and extroversion) were associated with knowledge or attitudes toward COVD-19 (p < 0.05). In the logistic and multiple linear regressions, capital regions, high basic knowledge, high information acquisition, correct information explanations contributed positively to preventative action (p < 0.05). Non-capital regions, male gender, non-bio-backgrounds, high public self-consciousness, high advanced knowledge, incorrect information explanations, and high extroversion contributed negatively to self-restraint (p < 0.05). Moreover, self-restraint was decreasing over time. These findings clarify the Japanese university students’ KAP and the related factors in the early period of the COVID-19 pandemic, and they may help university managers, experts, and policymakers control the future spread of COVID-19 and other emerging infections.

    DOI PubMed

  • Pedestrian positioning in surveillance video using anthropometric properties for effective communication

    Toshio Sato, Xin Qi, Keping Yu, Zheng Wen, San Hlaing Myint, Yutaka Katsuyama, Kiyohito Tokuda, Takuro Sato

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2020-October  2020年10月

     概要を見る

    Positioning of pedestrians or persons is an important technique for video-based systems. For network surveillance systems, positioning can be applied to reduce data volume for storage devices and communication traffic. In this paper, we propose a simple positioning method using anthropometric properties such as a face length. A foot point in an image is estimated based on face detection results and anthropometric properties, then perspective transformation converts the foot point into a position on the floor plane. We improve the anthropometric model to reduce estimation errors of positioning. Moreover, as an application of pedestrian positioning, we implement data reduction functions of video data for surveillance systems. Experiments using a 4K video indicate that the average positioning error improves to 0.5 m. In terms of data reduction, we found that combination of tracking, selection of key frames, cropping, resizing, and JPEG compression reduce the 35.6 MB video data to 70 kB. These experiments induce that our approach realize simple and precise positioning and data reduction for effective communication for video surveillance systems.

    DOI

  • Radiometric Passive Imaging for Robust Concealed Object Identification

    San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

    IEEE National Radar Conference - Proceedings   2020-September  2020年09月

     概要を見る

    Artificial Intelligence (AI) based millimeter wave radiometric imaging has become popular in a wide range of public security check systems, such as concealed object detection and identification. However, the low radiometric temperature contrast between small objects and low sensitivity is restricted to some extent. In this paper, an advanced radiometric passive imaging simulation model is proposed to improve the radiometric temperature contrast. This model considers additional noise, such as blur, variation in sensors, noise sources and summation of the number of frames. We establish a comprehensive training dataset that considers the physical characteristics of concealed objects. It can effectively fill the lack of a large database to avoid deteriorating the identification accuracy of AI applications. Moreover, it is also a key solution for improving the robustness of AI based object identification by using a convolutional neural network (CNN). Finally, simulation results are presented and analyzed to validate the proposed comprehensive training dataset and simulation model. Consequently, the proposed simulation model can effectively improve the robustness and accuracy of AI-based concealed object identification.

    DOI

  • PDKSAP : Perfected Double-Key Stealth Address Protocol without Temporary Key Leakage in Blockchain

    Cong Feng, Liang Tan, Huan Xiao, Keping Yu, Xin Qi, Zheng Wen, You Jiang

    2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020     151 - 155  2020年08月

     概要を見る

    The stealth address protocol is to create a one-time temporary output address of a transaction, hide its real output address, destroy the association between the input address and the real output address to achieve privacy protection for user identities in the transaction. However, the widely used double-key stealth address protocol (DKSAP) requires the sender to transmit the temporary public key along with the transaction, which enables attackers to easily identify stealth and non-stealth transactions and can lead to the loss of some private information. We propose a double-key stealth address protocol without temporary key leakage - PDKSAP by which senders and receivers maintain local transaction record databases to record the number of transactions with other users. Senders and receivers generate a temporary key pair for a transaction based on the number of transactions between them, which prevent leaking the transaction temporary key. Finally, we verify the protocol through experiments.

    DOI

  • Empirical Analysis of Bitcoin network (2016-2020)

    Ajay Kumar, Abhishek Kumar, Pranav Nerurkar, Muhammad Rukunuddin Ghalib, Achyut Shankar, Zheng Wen, Xin Qi

    2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020     96 - 101  2020年08月

     概要を見る

    Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence. Bitcoin transaction data from 01 Jan 2016 00:00:00 GMT to 08 May 2020 13:21:33 GMT was processed for this purpose to build a Bitcoin user graph.

    DOI

  • Blockchain-based Content-oriented Surveillance Network

    Xin Qi, Keping Yu, Zheng Wen, San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Kiyohito Tokuda, Takuro Sato

    IEEE Vehicular Technology Conference   2020-May  2020年05月

     概要を見る

    For the reason of public security, there are many surveillance actives taken places in either open areas or small areas. During an outburst of public eventuality, it is necessary to surveil public individuals. Based on the great number of populations nowadays, it is critical to propose efficient and secured data delivery network for surveillance networks. There are many different methods to surveil and secure an area, such as cameras and radio wave scanners. The modern surveillance networks are usually based on video content deliveries which consumes much network bandwidth and data security performance. Because it needs to efficiently deliver and protect the data generated by different devices. We propose a content-oriented IoT surveillance network, currently under development, which aims to identify dangerous individuals with various sensors and track the individuals through areas. This paper describes the concept of simulated passive imaging and identifying for conceal objects, various sensors association for person tracking and its traffic volume reduction. The data security in the network uses trust verification concept from blockchain technology. There are simulation and experiment to prove the work valid.

    DOI

  • Bottleneck Feature Extraction-Based Deep Neural Network Model for Facial Emotion Recognition

    Tian Ma, Kavuma Benon, Bamweyana Arnold, Keping Yu, Yan Yang, Qiaozhi Hua, Zheng Wen, Anup Kumar Paul

    Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST   338 LNICST   30 - 46  2020年

     概要を見る

    Deep learning is one of the most effective and efficient methods for facial emotion recognition, but it still encounters stability and infinite feasibility problems for faces of different races. To address this issue, we proposed a novel bottleneck feature extraction (BFE) method based on the deep neural network (DNN) model for facial emotion recognition. First, we used the Haar cascade classifier with a randomly generated mask to extract the face and remove the background from the image. Second, we removed the last output layer of the VGG16 transfer learning model, which was applied only for bottleneck feature extraction. Third, we designed a DNN model with five dense layers for feature training and used the famous Cohn-Kanade dataset for model training. Finally, we compared the proposed model with the K-nearest neighbor and logistic regression models on the same dataset. The experimental results showed that our model was more stable and could achieve a higher accuracy and F-measure, up to 98.59%, than other methods.

    DOI

  • Simulation and Evaluation of 28GHz SHF Wave Beamforming with 4x4 Element Configuration Using RF Circuit Phase Control

    Kazuhiko Tamesue, Zheng Wen, Takuro Sato

    JOURNAL OF ADVANCED SIMULATION IN SCIENCE AND ENGINEERING   8 ( 1 ) 1 - 11  2020年

     概要を見る

    In the 5th generation mobile communication system (5G), there is a strong demand for higher speed and higher capacity. The beamforming technique using phased-array antennas is effective for long-distance radio propagation and for reducing interference signals in the high SHF (super high frequency) band because the phase shift of the antenna array elements can be controlled to produce a narrow-band beam and to control the directivity. We have developed the 28 GHz array antenna for small cells and evaluated the beamforming characteristics using an RF analog phase shifter control scheme. In this paper, we verify the computer simulation and evaluation results of the beamforming characteristics of a newly developed 4x4 phased array antenna.

    DOI

  • Blockchain-Empowered Contact Tracing for COVID-19 Using Crypto-Spatiotemporal Information.

    Zheng Wen, Keping Yu, Xin Qi 0002, Toshio Sato, Yutaka Katsuyama, Takuro Sato, Wataru Kameyama, Fumiyuki Kato, Yang Cao 0011, Masatoshi Yoshikawa, Min Luo, Jun Hashimoto

    2020 IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020     1 - 6  2020年

     概要を見る

    The pandemic of the COVID-19 [1] has reawakened people that viruses are still the greatest threat to human society. Quarantining the patients and tracking close contacts has been used for hundreds of years in the battle between humans and the plague, which are still useful today. In the information society, we can employ information communications technology (ICT) to suppress the spread of epidemics and lower the epidemic curve. By using spatiotemporal information, we can trace the trajectories of patients and their close contacts. However, spatiotemporal information also involves personal privacy, and it has become a topic of concern about whether people's privacy should be sacrificed for epidemic control. In this paper, we propose a close contact tracing solution based on crypto-spatiotemporal information (CSI). First, the solution encrypts spatiotemporal information to protect personal privacy. Then, it uses a blockchain platform to realize the proof of CSI and uses Intel SGX [2] based trusted execution environment [3] to perform close contact judgment. Finally, it can trace close contacts while protecting personal privacy. The evaluation results indicate that the advantages and efficiency of the proposed scheme are significant.

    DOI

  • Radiometric Passive Imaging for Robust Concealed Object Identification

    San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

    2020 IEEE RADAR CONFERENCE (RADARCONF20)    2020年

     概要を見る

    Artificial Intelligence (AI) based millimeter wave radiometric imaging has become popular in a wide range of public security check systems, such as concealed object detection and identification. However, the low radiometric temperature contrast between small objects and low sensitivity is restricted to some extent. In this paper, an advanced radiometric passive imaging simulation model is proposed to improve the radiometric temperature contrast. This model considers additional noise, such as blur, variation in sensors, noise sources and summation of the number of frames. We establish a comprehensive training dataset that considers the physical characteristics of concealed objects. It can effectively fill the lack of a large database to avoid deteriorating the identification accuracy of AI applications. Moreover, it is also a key solution for improving the robustness of AI based object identification by using a convolutional neural network (CNN). Finally, simulation results are presented and analyzed to validate the proposed comprehensive training dataset and simulation model. Consequently, the proposed simulation model can effectively improve the robustness and accuracy of AI-based concealed object identification.

  • Design and Performance Evaluation of an AI-Based W-Band Suspicious Object Detection System for Moving Persons in the IoT Paradigm

    Keping Yu, Xin Qi, Toshio Sato, San Hlaing Myint, Zheng Wen, Yutaka Katsuyama, Kiyohito Tokuda, Wataru Kameyama, Takuro Sato

    IEEE Access   8   81378 - 81393  2020年

     概要を見る

    The threat of terrorism has spread all over the world, and the situation has become grave. Suspicious object detection in the Internet of Things (IoT) is an effective way to respond to global terrorist attacks. The traditional solution requires performing security checks one by one at the entrance of each gate, resulting in bottlenecks and crowding. In the IoT paradigm, it is necessary to be able to perform suspicious object detection on moving people. Artificial intelligence (AI) and millimeter-wave imaging are advanced technologies in the global security field. However, suspicious object detection for moving persons in the IoT, which requires the integration of many different imaging technologies, is still a challenge in both academia and industry. Furthermore, increasing the recognition rate of suspicious objects and controlling network congestion are two main issues for such a suspicious object detection system. In this paper, an AI-based W-band suspicious object detection system for moving persons in the IoT paradigm is designed and implemented. In this system, we establish a suspicious object database to support AI technology for improving the probability of identifying suspicious objects. Moreover, we propose an efficient transmission mechanism to reduce system network congestion since a massive amount of data will be generated by 4K cameras during real-time monitoring. The evaluation results indicate that the advantages and efficiency of the proposed scheme are significant.

    DOI

  • Medical Image Coloring Based on Gabor Filtering for Internet of Medical Things

    Hong An Li, Jiangwen Fan, Keping Yu, Xin Qi, Zheng Wen, Qiaozhi Hua, Min Zhang, Qiaoxue Zheng

    IEEE Access   8   104016 - 104025  2020年

     概要を見る

    Color medical images better reflect a patient's lesion information and facilitate communication between doctors and patients. The combination of medical image processing and the Internet has been widely used for clinical medicine on Internet of medical things. The classical Welsh method uses matching pixels to achieve color migration of grayscale images, but it exists problems such as unclear boundary and single coloring effect. Therefore, the key information of medical images after coloring can't be reflected efficiently. To address this issue, we propose an image coloring method based on Gabor filtering combined with Welsh coloring and apply it to medical grayscale images. By using Gabor filtering, which is similar to the visual stimulus response of simple cells in the human visual system, filtering in 4 directions and 6 scales is used to stratify the grayscale images and extract local spatial and frequency domain information. In addition, the Welsh coloring method is used to render the image with obvious textural features in the layered image. Our experiments show that the color transboundary problem can be solved effectively after the layered processing. Compared to images without stratification, the coloring results of the processed images are closer to the real image.

    DOI

  • Energy and materials-saving management via deep learning for wastewater treatment plants

    Jianhui Wang, Keyi Wan, Xu Gao, Xuhong Cheng, Yu Shen, Zheng Wen, Usman Tariq, Jalil Piran

    IEEE Access   8   191694 - 191705  2020年

     概要を見る

    With the increasing public attention on sustainability, conservation of energy and materials has been a general demand for wastewater treatment plants (WWTPs). To meet the demand, efficient optimal management and decision mechanism are expected to reasonably configure resource of energy and materials.In recent years, advanced computational techniques such as neural networks and genetic algorithm provided data-driven solutions to overcome some industrial problems. They work from the perspective of statistical learning, mining invisible latent rules from massive data. This paper proposes energy and materials-saving management via deep learning for WWTPs, using real-world business data of a wastewater treatment plant located in Chongqing, China. Treatment processes are modeled through neural networks, and materials cost that satisfies single indexes can be estimated on this basis. Then, genetic algorithm is selected as the decision scheme to compute overall cost that is able to simultaneously satisfy all the indexes. Empirically, experimental results evaluate that with the proposed management method, total energy and materials cost can be reduced by 10%-15%.

    DOI

  • 3D Reconstruction for Super-Resolution CT Images in the Internet of Health Things Using Deep Learning

    Jing Zhang, Ling Rui Gong, Keping Yu, Xin Qi, Zheng Wen, Qiaozhi Hua, San Hlaing Myint

    IEEE Access   8   121513 - 121525  2020年

     概要を見る

    The Internet of Health Things (IoHT) enables health devices to connect to the Internet and communicate with each other, which provides the high-accuracy and high-security diagnosis result in the medical area. As essential parts of the IoHT, computed tomography (CT) images help doctors diagnose disease. In the traditional disease diagnosing process, low-resolution medical CT images produce low-accuracy diagnosis results for microlesions. Moreover, CT images can only provide 2D information about organs, and doctors should estimate the 3D shape of a lesion based on experience. To solve these problems, we propose a 3D reconstruction method for secure super-resolution computed tomography (SRCT) images in the IoHT using deep learning. First, we use deep learning to obtain secure SRCT images from low-resolution images in the IoHT. To this end, we adopt a conditional generative adversarial network (CGAN) based on the edge detection loss function (EDLF) in the deep learning process, namely EDLF-CGAN algorithm. In this algorithm, the CGAN is employed to generate SRCT images with luminance and contrast as the input auxiliary conditions, which can improve the accuracy of super-resolution (SR) images. An EDLF is proposed to consider the edge features in the generated SRCT images, which reduces the deformation of generated image. Second, we apply the secure SR images generated from the deep learning method to perform 3D reconstruction. An advanced ray casting 3D reconstruction algorithm that can reduce the number of rays by selecting the appropriate bounding box is proposed. Compared with the traditional algorithm, the proposed ray casting 3D reconstruction algorithm can reduce the time and memory cost. The experimental results show that our EDLF-CGAN has a better SR reconstruction effect than other algorithms via the indicators of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In addition, our advanced ray casting 3D reconstruction algorithm greatly improves the efficiency compared with the traditional ray casting algorithm.

    DOI

  • Block Chain Based Internet of Medical Things for Uninterrupted, Ubiquitous, User-Friendly, Unflappable, Unblemished, Unlimited Health Care Services (BC IoMT U6HCS)

    J. Indumathi, Achyut Shankar, Muhammad Rukunuddin Ghalib, J. Gitanjali, Qiaozhi Hua, Zheng Wen, Xin Qi

    IEEE Access   8   216856 - 216872  2020年

     概要を見る

    The most burning topic of today, calls for a holistic solution that is reliable, secure, privacy preserved, cost effective Cloud storage that can tide over the turbulent conditions of the rapidly budding digital storage technologies. This send an outcry for a devoted solution, in the form of an individualized, patient-centric care - IoMT that augments precise disease identifications, decrease in errors, reduction in costs of care through the support of technology, allows patients to direct health information data to doctors,manage drugs, keep Personal Health Records, caters to remote medical supports Care, provides proactive approach to preserving Good Health, improves and Accelerates Clinician Workflows, empowers extreme connectivity due to better automation and perceptions in the DNA of IoMT functions. But IoMT adoption is like a rose with thorns like constraints of increased administrative costs, deficiency of universal data access, present-day electronic medical records. The BCT is used in the framework to overcome the security issues of IoMT through the use of latest encryptions. Furthermore, this framework harnesses the benefits of Block Chain like reduced cost, speed, automation, immutability, near-impossible loss of data, permanence, removal of intermediaries, decentralization of consensus, legitimate access to health data, data safekeeping, accrual-based imbursement mechanisms, and medical supply chain efficacy. The outcomes in this paper are (i)A systematic investigation of the current IoMT, Block Chain and Cloud Storage in Health Care;(ii) Explore the challenges and necessities for the confluence of Block Chain (BC), Internet of Medical Things (IoMT), Cloud Computing (CC);(iii)Formulate the requirements necessary for the real-time remote Health Care of one-to-one care structure, which, supports the vital functions that are critical to the Patient Centric Health Care;(iv) Design and develop a novel BC IoMT U6 HCS (Block Chain based Internet of Medical Things for Uninterrupted, Ubiquitous, User-friendly, Unflappable, Unblemished, Unlimited Health Care Services) Layered Architecture, to support the vital functions critical for Patient Centric Health Care and (v) Implement and test with the previous established and proven techniques. The integrity of the Layered Architecture is validated with the already existing ones in terms of audit performances. The results from the Layered Architecture are validated and are proven to be competent in achieving safe auditing and surpass the former ones. The technology is in the sprouting phases, it is perilous that affiliates of the Health Care community realize the rudimentary ideas behind Block Chain, and detect its feasible impact on the future of patient centric medical care. Finally, and most importantly, this paper also gives a panoramic view on the current research status, and imminent directions of Secure Internet of Medical Things Using Block Chain.

    DOI

  • On Scheduling Policies with Heavy-Tailed Dynamics in Wireless Queueing Systems

    Shengbo Chen, Lanxue Zhang, Cong Shen, Keping Yu, San Hlaing Myint, Zheng Wen

    IEEE Access   8   32137 - 32149  2020年

     概要を見る

    This paper takes a system view and studies a wireless queueing system where heavy-tailness may occur both at the traffic arrival and in the form of the multi-user interference. With the rapid development of AI technologies, this heavy-tailed traffic model has become more prevalent in the current network system, such as the file or data size used in the deep learning algorithm. We first re-visit the standard asymmetric queueing system with a mix of heavy-tailed and light-tailed traffic, but under a new variable-rate service model that not only better models the dynamics of the wireless medium but also includes the previous models as special cases. We then focus on the scheduling problem when heavy-tailed interference disrupts the serving link. The performance of queueing policies is investigated during an ON/OFF renewal channel process with heavy-tailed OFF periods, and the expected queue length and the throughput characteristic is studied under the priority as well as max-weight scheduling policies. The results show that the expected queue length of the heavy queue cannot be maintained as finite even under the most favorable priority policy. On the other hand, a priority policy can guarantee the finiteness of an expected queue length for the light queue, but the system is not throughput optimal any longer. It is further shown that no benefit can be provided by the max-weight scheduling policy to the light queue for the queue length behavior in a steady-state, though the system is always throughput optimal.

    DOI

  • 3D Reconstruction for motion blurred images using deep learning-based intelligent systems

    Jing Zhang, Keping Yu, Zheng Wen, Xin Qi, Anup Kumar Paul

    Computers, Materials and Continua   66 ( 2 ) 2087 - 2104  2020年

     概要を見る

    The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual's height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image. Then, the blurred image and the corresponding sharp image are input into the WGAN. This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions. Next, we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction. We propose a threshold optimization random sample consensus (TO-RANSAC) algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately. Compared with the traditional RANSAC algorithm, the TO-RANSAC algorithm can adjust the threshold adaptively, which improves the accuracy of the 3D reconstruction results. The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms. In addition, the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

    DOI

  • 5G-Enabled Health Systems: Solutions, Challenges and Future Research Trends

    Di Zhang, Teng Zhang, Yunkai Zhai, Joel J.P.C. Rodrigues, Dalong Zhang, Zheng Wen, Keping Yu, Takuro Sato

    11th Academic Conference ITU Kaleidoscope: ICT for Health: Networks, Standards and Innovation, ITU K 2019    2019年12月

     概要を見る

    In the literature, Information communication technology (ICT)-assisted health systems have been intensively discussed. However, it has seldom become a reality. This is mainly due to the current wireless technologies' limited transmission rate, few connected devices and high latency. On the contrary, the fifth generation (5G) wireless communications can connect more devices, provide faster transmission rates and a lower latency. In this article, we first introduce the 5G-enabled health systems and our specific implementation in the first affiliated hospital of Zhengzhou University (FAHZZU). Afterwards, the potential challenges and future research trends on demonstrating the 5G-enabled health systems are discussed.

    DOI

  • Content-Oriented Common IoT Platform for Emergency Management Scenarios

    Zheng Wen, Xin Qi, Keping Yu, Jairo Eduardo Lopez, Takuro Sato

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2019-November  2019年11月

     概要を見る

    With the explosive growth of Internet of Things (IoT) devices, the demand for network systems is increasing rapidly. In terms of bandwidth, security, management, and more, there are many bottlenecks in current network systems. To address the above issues, future network technology, such as information-centric networking, is proposed to provide a good solution for IoT applications. However, new communications technologies need to be implemented with new communications applications. The traditional client/server-based network applications will become one of the bottlenecks in information-centric networking (ICN). In this paper, the work of the Ministry of Internal Affairs and Communications project at Waseda University over the past three years is summarized, and a highly efficient content-oriented common IoT platform for emergency management scenarios is proposed.

    DOI

  • Energy-efficient game-theoretical random access for M2M communications in overlapped cellular networks

    Zhenyu Zhou, Junhao Feng, Yunjian Jia, Shahid Mumtaz, Kazi Mohammed Saidul Huq, Jonathan Rodriguez, Di Zhang

    COMPUTER NETWORKS   129   493 - 501  2017年12月  [査読有り]

     概要を見る

    The unprecedented growth of machine-to-machine (M2M) devices has brought a heavy burden to traditional cellular networks. In this paper, we focus on the overload problem caused by massive connections of M2M devices in overlapped cellular networks. We formulate the joint base station (BS) selection and power allocation optimization problem for each M2M device as a noncooperative access game. The utility function of each M2M device is described as the success probability of random access weighted by the energy efficiency (EE). We propose an iterative energy-efficient game-theoretical random access algorithm, in which each M2M device searches its optimal strategies in turn until no M2M device is able to improve its individual utility with a unilateral deviation. Numerical results demonstrate that significant performance enhancements on both the delay and energy consumption can be achieved simultaneously. (C) 2017 Elsevier B.V. All rights reserved.

    DOI

  • Performance Analysis of Non-Regenerative Massive-MIMO-NOMA Relay Systems for 5G

    Di Zhang, Yuanwei Liu, Zhiguo Ding, Zhenyu Zhou, Arumugam Nallanathan, Takuro Sato

    IEEE TRANSACTIONS ON COMMUNICATIONS   65 ( 11 ) 4777 - 4790  2017年11月  [査読有り]

     概要を見る

    The non-regenerative massive multi-input-multi-output (MIMO) non-orthogonal multiple access (NOMA) relay systems are introduced in this paper. The NOMA is invoked with a superposition coding technique at the transmitter and successive interference cancellation (SIC) technique at the receiver. In addition, a maximum mean square error-SIC receiver design is adopted. With the aid of deterministic equivalent and matrix analysis tools, a closed-form expression of the signal to interference plus noise ratio (SINR) is derived. To characterize the performance of the considered systems, closed-form expressions of the capacity and sum rate are further obtained based on the derived SINR expression. Insights from the derived analytical results demonstrate that the ratio between the transmitter antenna number and the relay number is a dominate factor of the system performance. Afterward, the correctness of the derived expressions are verified by the Monte Carlo simulations with numerical results. Simulation results also illustrate that: 1) the transmitter antenna, averaged power value, and user number display the positive correlations on the capacity and sum rate performances, whereas the relay number displays a negative correlation on the performance and 2) the combined massive-MIMO-NOMA scheme is capable of achieving higher capacity performance compared with the conventional MIMO-NOMA, relay-assisted NOMA, and massive-MIMO orthogonal multiple access (OMA) scheme.

    DOI

  • Capacity Analysis of NOMA With mmWave Massive MIMO Systems

    Di Zhang, Zhenyu Zhou, Chen Xu, Yan Zhang, Jonathan Rodriguez, Takuro Sato

    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS   35 ( 7 ) 1606 - 1618  2017年07月  [査読有り]

     概要を見る

    Non-orthogonal multiple access (NOMA), millimeter wave (mmWave), and massive multiple-input-multiple-output (MIMO) have been emerging as key technologies for fifth generation mobile communications. However, less studies have been done on combining the three technologies into the converged systems. In addition, how many capacity improvements can be achieved via this combination remains unclear. In this paper, we provide an in-depth capacity analysis for the integrated NOMA-mmWave-massive-MIMO systems. First, a simplified mmWave channel model is introduced by extending the uniform random single-path model with angle of arrival. Afterward, we divide the capacity analysis into the low signal to noise ratio (SNR) and high-SNR regimes based on the dominant factors of signal to interference plus noise ratio. In the noise-dominated low-SNR regime, the capacity analysis is derived by the deterministic equivalent method with the Stieltjes-Shannon transform. In contrast, the statistic and eigenvalue distribution tools are invoked for the capacity analysis in the interference-dominated high-SNR regime. The exact capacity expression and the low-complexity asymptotic capacity expression are derived based on the probability distribution function of the channel eigenvalue. Finally, simulation results validate the theoretical analysis and demonstrate that significant capacity improvements can be achieved by the integrated NOMA-mmWave-massive-MIMO systems.

    DOI

  • Node name routing in information-centric Ad-Hoc network

    Zheng Wen, Di Zhang, Keping Yu, Takuro Sato

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E100A ( 2 ) 680 - 687  2017年02月  [査読有り]

     概要を見る

    We propose the node name routing (NNR) strategy for information-centric ad-hoc networks based on the named-node networking (3N). This strategy is especially valuable for use in disaster areas because, when the Internet is out of service during a disaster, our strategy can be used to set up a self-organizing network via cell phones or other terminal devices that have a sharing ability, and it does not rely on a base station (BS) or similar providers. Our proposed strategy can solve the multiple-name problem that has arisen in prior 3N proposals, as well as the dead loop problems in both 3N ad-hoc networks and TCP/IP ad-hoc networks. To evaluate the NNR strategy, it is compared with the optimized link state routing protocol (OLSR) and the dynamic source routing (DSR) strategy. Computer-based comprehensive simulations showed that our NNR proposal exhibits a better performance in this environment when all of the users are moving randomly. We further observed that with a growing number of users, our NNR protocol performs better in terms of packet delivery, routing cost, etc.

    DOI

  • One Integrated Energy Efficiency Proposal for 5G IoT Communications

    Di Zhang, Zhenyu Zhou, Shahid Mumtaz, Jonathan Rodriguez, Takuro Sato

    IEEE INTERNET OF THINGS JOURNAL   3 ( 6 ) 1346 - 1354  2016年12月  [査読有り]

     概要を見る

    To further enhance the energy efficiency (EE) performance of fifth generation (5G) Internet of Things systems, an integrated structure is proposed in this paper. That is, other than prior studies that separately study the wireless and wired parts, the wireless and wired parts are holistically combined together to comprehensively optimize the EE of the whole system. The integrated system structure is introduced beforehand with the proposed unified control center components for better deployment of the select-and-sleep mechanism. In addition, in the wireless part, one cellular partition zooming (CPZ) mechanism is proposed. In contrast, in the wired part, a precaching mechanism is introduced. With these proposals, the proposed system EE performance is investigated. Comprehensive computer-based simulation results demonstrate that the proposed schemes display better EE performance. This is due to the fact that system power consumption is further reduced with these schemes as compared to the prior work.

    DOI

  • Reverse Combinatorial Auction based Resource Allocation in Heterogeneous Software Defined Network with Infrastructure Sharing

    Di Zhang, Zheng Chang, Timo Hamalainen

    2016 IEEE 83RD VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING)    2016年  [査読有り]

     概要を見る

    In this paper, resource allocation (RA) problem in heterogeneous Software Defined Network (SDN) with infrastructure sharing platform among multiple network service providers (NSPs) is studied. The considered problem is modeled as a reverse combinatorial auction (R-CA) game, which takes competitiveness and fairness of different NSPs into account. The heterogeneous RA associated with personal QoS requirement problem is optimized by maximizing the social welfare, which is demonstrated to be total system throughput. By exploiting the properties of iterative programming, the resulting non-convex Winner Determination Problem (WDP) is transformed into an equivalent convex optimization problem. The proposed R-CA game is strategy-proof and proved to be with low computational complexity. Simulation results illustrate that with SDN controller sharing environment, the proposed iterative ascending price Vickrey (IA-PV) algorithm converges fast and can obtain nearly optimal system throughput. It is also demonstrated to be robust and can ensure higher fairness and individual profit among different NSPs. With the fairness guaranteed, this infrastructure sharing SDN platform can attract more NSPs to participate, in order to achieve more profit and cost reduction.

  • Integrating Energy Efficiency Mechanism with Components Selection for Massive MIMO Based C-RAN

    Di Zhang, Shahid Mumtaz, Zhenyu Zhou, Takuro Sato

    2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC)     74 - 79  2016年  [査読有り]

     概要を見る

    Based on the Cloud-Radio Access Network (C-RAN) structure and massive Multi-Input Multi-Output (MIMO), we propose one comprehensive system model and give the Energy Efficiency (EE) analysis of this system. The C-RAN structure is amended for better antenna control in massive MIMO based C-RAN system model. Furthermore, system EE optimization problem is modeled from a comprehensive way, and its solution is addressed with a convex optimization method plus an offline decision. In EE analysis, apart from the prior literature, Carrier Capacity (CC), antenna, Radio-Frequency (RF) chain, machine room and also the electron circuits are integrally taken into consideration. A positive correlation is observed between CC and the EE performance prior to the optimal value, after this optimal value, further increase CC has little help to EE enhancement.

  • A Key Management Scheme for Secure Communications of Information Centric Advanced Metering Infrastructure in Smart Grid

    Keping Yu, Mohammad Arifuzzaman, Zheng Wen, Di Zhang, Takuro Sato

    IEEE Transactions on Instrumentation and Measurement   64 ( 8 ) 2072 - 2085  2015年08月  [査読有り]

     概要を見る

    Advanced metering infrastructure (AMI), as the totality of systems and networks to measure, collect, store, analyze, and use energy usage data, is supposed to be the core component in smart grid. In AMI, there are numerous challenges among which cyber security is a major one that needs to be addressed with priority. The information centric networking (ICN) is a promising architecture for the future Internet that disseminates content based on named data instead of named hosts. The congestion control and self-security can enable more scalable, secure, collaborative, and pervasive networking, these make the ICN a potential network architecture for smart grid. This paper aims to apply the ICN approach on AMI system, which we termed as information centric AMI (ICN-AMI). To the best of our knowledge, this is the first attempt to distribute contents (or requests for contents) based on ICN in AMI system. Moreover, a simulation-based performance evaluation is employed to evaluate the effectiveness of the proposed ICN-AMI approach in traffic control for developing AMI system in smart grid. In addition, we proposed a novel key management scheme (KMS) for a large number of smart meters in this system to ensure confidentiality, integrality, and authentication. To validate the scheme, the security analysis, comparisons are done to demonstrate that the proposed information centric KMS (ICN-KMS) is possible and a promising solution for ICN-AMI system.

    DOI

  • Information centric networking for disaster information sharing services

    Zheng Wen, Di Zhang, Keping Yu, Takuro Sato

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E98A ( 8 ) 1610 - 1617  2015年08月  [査読有り]

     概要を見る

    Information Centric Networking (ICN) had merits in terms of mobility, security, power consumption and network traffic. When a large-scale disaster occurred, the current communication system might be fragile and the server based network service might be unavailable due to the damages, network congestions, and power failure, etc. In this paper, we proposed an ICN based Disaster Information Sharing Service (DISS) [1], [2] system. DISS could provide robust information sharing service. Users could publish disaster information as a content message with the help of our DISS. In addition, by utilizing DISSïs message naming strategy, users could retrieve disaster information even without a server connection. The ICN based DISS could reduce the probability of network congestion when a large number of simultaneous connections occurring. It could provide server-less service in poor network condition. DISS allows users retrieve disaster information from terminals or ICN nodes. During disasters, sharing information timely and effective could protect people from disaster, ensure peopleïs safety.

    DOI

  • Energy Efficiency Scheme with Cellular Partition Zooming for Massive MIMO Systems

    Di Zhang, Keping Yu, Zhenyu Zhou, Takuro Sato

    2015 IEEE 12TH INTERNATIONAL SYMPOSIUM ON AUTONOMOUS DECENTRALIZED SYSTEMS ISADS 2015     266 - 271  2015年  [査読有り]

     概要を見る

    Massive Multiple-Input Multiple-Output (Massive MIMO) has been realized as a promising technology element for 5G wireless mobile communications, in which Spectral Efficiency (SE) and Energy Efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy consumption of cellular system comes from the operator, mostly used to feed the base station (BS). Yet previously, the User Equipment (UE) is focused on while studying the EE issue instead of BS. In this case, in this paper, an EE scheme that focuses on the optimization of BS energy consumption is proposed. Apart from the previous studies, which divides the coverage area by circuit section, the coverage area is divided by fan section with the help of Propagation theory for zoom in or zoom out. In the proposal, transmission model and parameters related to EE is deduced first. Afterwards, the Cellular Partition Zooming (CPZ) scheme is proposed where the BS can zoom in to maintain the coverage area or zoom out to save the energy. Comprehensive simulation results demonstrate that CPZ presents better EE performance with negligible impact on the transmission rate.

    DOI

  • A novel synchronization algorithm for IEEE802.11 TDMA ad hoc network

    Zheng Wen, Ung Heo, Jaeho Choi

    2007 IEEE INTERNATIONAL CONFERENCE ON MOBILE AD-HOC AND SENSOR SYSTEMS, VOLS 1-3     865 - 870  2007年  [査読有り]

     概要を見る

    Time synchronization problem has been thoroughly studied for years, as it is a critical service for many applications. A novel self-synchronization algorithm is proposed for IEEE802.11 interface in TDMA-based ad hoc network, in which the advantage of RBS is exploited for the leading node election. Here, it overcomes shortcomings of previous methods by adopting a novel and efficient clock updating algorithm. The proposed scheme can be successfully applied to support real time applications such as voice calls over the multi-hop ad hoc network. The preliminary analytical results yields that the maximum clock offset is within 10 mu s.

▼全件表示

共同研究・競争的資金等の研究課題

  • Blockchain-empowered Contact Tracing for COVID-19 Using Crypto-spatiotemporal Information

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists

    研究期間:

    2021年04月
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    2026年03月
     

  • テラヘルツ波を利用した雲・水蒸気分布観測二周波レーダーシステムの研究開発

    国立研究開発法人情報通信研究機構 革新的情報通信技術(Beyond 5G(6G))基金事業 

    研究期間:

    2022年04月
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  • 低遅延でインタラクティブなゼロレイテンシー映像・Somatic統合ネットワーク

    国立研究開発法人情報通信研究機構 Beyond 5G研究開発促進事業Beyond 5Gシーズ創出型プログラム 

    研究期間:

    2021年11月
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    2024年03月
     

  • 超多数・多種移動体による人流・物流のためのダイナミックセキュアネットワークの研究

    国立研究開発法人情報通信研究機構 Beyond 5G研究開発促進事業Beyond 5Gシーズ創出型プログラム 

    研究期間:

    2021年11月
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    2023年03月
     

  • LPWAに対応した軽量な分散台帳技術を用いた認証システムの研究開発

    総務省 令和2年度電波有効利用促進型研究開発(先進的電波有効利用型フェーズⅡ(社会展開促進) 

    研究期間:

    2020年05月
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  • セキュリティ強化に向けた移動物体高度認識レーダー基盤技術の研究開発

    総務省: 電波資源拡大のための研究開発 

    研究期間:

    2019年07月
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  • サービスに応じたスライス動的生成・管理機能の実証と標準化を目的とする日欧連携5G移動通信基盤テストベッドの研究開発

    総務省  総務省SCOPE サービスに応じたスライス動的生成・管理機能の実証と標準化を目的とする日欧連携5G移動通信基盤テストベッドの研究開発

    研究期間:

    2016年04月
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  • IoT共通基盤技術の確立実証高効率かつセキュアなIoTデータ収集・配信ネットワーク制御技術の確立

    総務省  総務省 IoT共通基盤技術の確立実証高効率かつセキュアなIoTデータ収集・配信ネットワーク制御技術の確立

    研究期間:

    2017年04月
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  • 止まらない通信網”を活用した命をつなぐ減災推進事業

    総務省  総務省 SCOPE 止まらない通信網”を活用した命をつなぐ減災推進事業

    研究期間:

    2016年04月
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    2018年03月
     

▼全件表示

Misc

  • 低遅延でインタラクティブなゼロレイテンシー映像・Somatic統合ネットワーク(4)遠隔作業における映像予測による遅延補償

    佐藤俊雄, 勝山裕, 中村裕一, QI Xin, WEN Zheng, 爲末和彦, 亀山渉, 甲藤二郎, 佐藤拓朗

    電子情報通信学会大会講演論文集(CD-ROM)   2023  2023年

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    勝山裕, 佐藤俊雄, WEN Zheng, 爲末和彦, 中村裕一, 佐藤拓朗, 亀山渉, 甲藤二郎

    電子情報通信学会大会講演論文集(CD-ROM)   2023  2023年

    J-GLOBAL

  • 低遅延でインタラクティブなゼロレイテンシー映像・Somatic統合ネットワーク(1)全体概要

    甲藤二郎, 勝山裕, 佐藤俊雄, QI Xin, WEN Zheng, 金井謙治, SUN Heming, 亀山渉, 佐藤拓朗, 津田俊隆, 中村裕一, 近藤一晃, 下西慶, 小野浩司, 根波健一, 小林康雄, 森一倫, 永松衛二

    電子情報通信学会大会講演論文集(CD-ROM)   2023  2023年

    J-GLOBAL

  • GNSS Spoofing Detection using Multiple Sensing Devices and Decision Tree Classifier

    Xin Qi, Toshio Sato, Zheng Wen, Masaru Takeuchi, Yutaka Katsuyama, Kazuhiko Tamesue, Kazue Sako, Jiro Katto, Takuro Sato

    IEICE Proceeding Series   72   O3-5  2022年11月

     概要を見る

    For next-generation logistics systems using autonomous vehicles and drones, spoofing of the GNSS location data induces serious problems. Although signal-based anti-spoofing has been studied, it is difficult to apply to current commercial GNSS modules in many cases. We investigate possibilities to detect spoofing of GNSS location data using multiple sensing devices and a decision tree classifier. Multiple features using the GNSS, beacons, and the IMU are defined and create a model to detect spoofing. Experimental results using learning-based classifier indicates the higher performances and generalization capability. The results also show that distance from beacons is useful to detect GNSS spoofing and indicate prospects of installation for the future drone highways.

    DOI

  • Using linguistic properties of place specification for network naming to improve mobility performance

    Jairo López, Quang Ngoc Nguyen, Zheng Wen, Keping Yu, Takuro Sato

    Sensors (Switzerland)   19 ( 13 )  2019年07月   [ 国際誌 ]

     概要を見る

    By considering the definitions and properties from the field of linguistics regarding place specification, a questionnaire that can be used to improve naming in networks is obtained. The questionnaire helps introduce the idea of place specification from linguistics and the concept of metric spaces into network naming schemes. The questionnaire results are used to improve the basic Information-Centric Networking (ICN) architecture’s notoriously lax network naming structure. The improvements are realized by leveraging components from the Named-Node Network Architecture, a minor ICN design, to supply the resulting network architecture with the properties the questionnaire highlights. Evaluation results from experiments demonstrate that modifying the network architecture so that the proposed questionnaire is satisfied results in achieving high mobility performance. Specifically, the proposed system can obtain mean application goodput at above 88% of the ideal result, with a delay below 0.104 s and with the network time-out Interest ratio below 0.082 for the proposed single mobile push producer, single mobile consumer scenario, even when the nodes reach the maximum tested speed of 14 m/s.

    DOI PubMed

  • Content-oriented disaster network utilizing named node routing and field experiment evaluation

    Xin Qi, Zheng Wen, Keping Yu, Kazunori Murata, Kouichi Shibata, Takuro Sato

    IEICE Transactions on Information and Systems   E102D ( 5 ) 988 - 997  2019年05月

     概要を見る

    Low Power Wide Area Network (LPWAN) is designed for low-bandwidth, low-power, long-distance, large-scale connected IoT applications and realistic for networking in an emergency or restricted situation, so it has been proposed as an attractive communication technology to handle unexpected situations that occur during and/or after a disaster. However, the traditional LPWAN with its default protocol will reduce the communication efficiency in disaster situation because a large number of users will send and receive emergency information result in communication jams and soaring error rates. In this paper, we proposed a LPWAN based decentralized network structure as an extension of our previous Disaster Information Sharing System (DISS). Our network structure is powered by Named Node Networking (3N) which is based on the Information-Centric Networking (ICN). This network structure optimizes the excessive useless packet forwarding and path optimization problems with node name routing (NNR). To verify our proposal, we conduct a field experiment to evaluate the efficiency of packet path forwarding between 3N+LPWA structure and ICN+LPWA structure. Experimental results confirm that the load of the entire data transmission network is significantly reduced after NNR optimized the transmission path.

    DOI CiNii

  • A novel base-station selection strategy for cellular vehicle-to-everything (C-V2X) communications

    Qiaozhi Hua, Keping Yu, Zheng Wen, Takuro Sato

    Applied Sciences (Switzerland)   9 ( 3 )  2019年02月

     概要を見る

    Cellular vehicle-to-everything (C-V2X) communication facilitates the improved safety, comfort, and efficiency of vehicles and mobility by exchanging information between vehicles and other entities. In general, only the macrocell or only the femtocell is the communication infrastructure for C-V2X. Currently, a macro-femtocell network is used as the new C-V2X networking architecture. However, there are two unresolved problems for C-V2X in macro-femtocell networks. Firstly, vehicle mobility requires the frequent switching of connections between different base stations; invalid switching results in worse communication quality. Secondly, unintelligent base station selections cause network congestion and network-load imbalance. To address the above challenges, this paper proposes a base station selection strategy based on a Markov decision policy for a vehicle in a macro-femtocell system. Firstly, we present a mechanism to predict received signal strength (RSS) for base station selection. Secondly, a comparing Markov decision policy algorithm is presented in C-V2X. To the best of our knowledge, this is the first attempt to achieve predicted RSS based on a Markov decision policy in C-V2X technology. To validate the proposed mechanism, we simulated the traditional base station selection and our proposal when the vehicle moved at different speeds. This demonstrates that the effectiveness of a traditional base station selection policy is obvious only at high speeds, and this weakness can be resolved by our proposal. Then, we compare our solution with the traditional base station selection policy. The simulation results show that our solution is effective at switching connections between base stations, and it can effectively prevent the overloading of network resources.

    DOI

  • Design and performance evaluation of content-oriented communication system for iot network: A case study of named node networking for real-Time video streaming system

    Xin Qi, Yuwei Su, Keping Yu, Jingsong Li, Qiaozhi Hua, Zheng Wen, Jairo Lopez, Takuro Sato

    IEEE Access   7   88138 - 88149  2019年01月

     概要を見る

    Information-Centric Networking (ICN) was born in an era that more and more users are shifting their interests to the content itself rather than the location where contents are stored. This paradigm shift in the usage patterns of the Internet, along with the urgent needs for content naming, pervasive caching, better security, and mobility support, has prompted researchers to consider a radical change to the Internet architecture. However, ICN is still in its infancy and development stage, and many issues still exist and need to be addressed. The common ICN architecture is lacking the host-centric communication ability and difficult to provide seamless mobility in current solutions. To solve this problem, our team had proposed the Named-Node Networking (3N) concept, which not only naming the content but also naming the node and it proved to have better performance of providing seamless mobility in the simulation. However, the previous contributions were limited in the 3N namespace for seamless mobility support in both producer and consumer. In this paper, we have proposed a 3N system which includes 3N naming, data delivery, mobility support, and data security. Moreover, we have created a 3N-based real-Time video streaming system to evaluate data delivery performance and mobility handoff performance. The evaluation result proofs that our system performs better than TCP video streaming in a multi-client situation and a WiFi-based handoff was demonstrated.

    DOI

  • Content-Oriented Surveillance System Based on ICN in Disaster Scenarios

    Koki Okamoto, Toru Mochida, Daichi Nozaki, Zheng Wen, Xin Qi, Takuro Sato

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2018-November   484 - 489  2018年07月

     概要を見る

    This paper deals with an efficient image object detection method for use in a disaster prevention network that uses information-centric networking (ICN). In ICN for disaster prevention, a large number of surveillance cameras are arranged, and disaster image contents corresponding to the user's requests are distributed directly from the node attached to the surveillance camera. At this time, the name of the content requested by the user does not necessarily match the name of the image acquired by the surveillance camera. In this paper, the content requested by the user is processed and named using natural language processing (NLP). In addition, the image content from the surveillance camera is named using artificial intelligence technology. In this way, a method for improving the hit ratio between users and cameras was established. Furthermore, the volume of the interest packets decreases based on the information which area often occurs disaster. As a result, the network efficiency of ICN can be improved.

    DOI

  • AI Management System to Prevent Accidents in Construction Zones Using 4K Cameras Based on 5G Network

    Daichi Nozaki, Koki Okamoto, Toru Mochida, Xin Qi, Zheng Wen, San Hlaing Myint, Kiyohito Tokuda, Takuro Sato, Kazuhiko Tamesue

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2018-November   462 - 466  2018年07月

     概要を見る

    Accident prevention for trucks, cranes and work vehicles at construction sites is important. Here, we use high-precision surveillance cameras with 4K cameras as IoT terminals to implement safe workplace environments. In this research, the system transmits photographic images from trucks, cranes, and other construction equipment fitted with 4K cameras at the work site to a database via 5G wireless networks, and uses AI to assess the interactions and movements of workers in the database. We introduce a system that makes it possible to avoid crashes by informing truck and crane drivers, and notifying automatic driving trucks and crane cars of that information. It is conceivable that uplink traffic could become congested due to many vehicles equipped with 4K cameras simultaneously transmitting images over the 5G uplink. Here, as a basic 5G characteristic evaluation, 4K images from trucks or cranes moving at a low speed are transmitted to the database according to moving speed and distance to the 5G terminal connecting the base station and IoT device. Based on the error environment, we report the relation with the error of the system that judges and identifies the surrounding environment using AI when video quality is deteriorated.

    DOI

  • Naming scheme using NLP machine learning method for network weather monitoring system based on ICN

    Toru Mochida, Daichi Nozaki, Koki Okamoto, Xin Qi, Zheng Wen, Takuro Sato, Keping Yu

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2017-December   428 - 434  2018年02月

     概要を見る

    The market for IoT devices has been expanding rapidly for several years, and many fields are anticipating further demand in the future. However, together with this expansion, increased communication failures are expected, and solutions to prevent them are needed. Here, we aim to solve communication problems using a new network, called the CCN (Contents Centric Networking), which is one of the leading ICN (Information Centric Networking) solutions. Our system was constructed by assuming the case of a weather monitoring IoT application in which large data communications were very likely to occur. In this paper, we propose a technique for distributing meteorological data gathered with a weather observation device and 4K camera by ICN, and caching content with a new Naming scheme using machine learning. x

    DOI

  • An artificial neural network-based distributed information-centric network service

    Zheng Wen, Takuro Sato

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2017-December   453 - 458  2018年02月

     概要を見る

    Artificial neural networks (ANN) have been widely used in various areas. As a bottleneck, hardware specification affects the efficiency of an ANN. With the development of distributed computing, distributed ANNs show advantages in dealing with huge data. The network bandwidth is a new bottleneck restricting the performance of distributed ANNs. Information-Centric Networking (ICN) [1], as the Next Generation Network (NGN) solution, has shown merits regarding mobility, security, power consumption and network traffic. In this paper, we remodel the architecture of network service using ANNs. We proposed an ANN-Based Distributed Information-Centric Network Service (ANN based DICNS). The distributed nodes are connected like a neural network. When a client utilizes the DICNS, the data flow from the source to the consumer node like the signal traveling from an input layer to an output layer in a neural network. By using an ICN, our proposal can significantly reduce network consumption, and the named data can help the DICNS effectively manage and classify the data.

    DOI

  • Node name routing in information-centric Ad-Hoc network

    Zheng Wen, Di Zhang, Keping Yu, Takuro Sato

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E100A ( 2 ) 680 - 687  2017年02月

     概要を見る

    We propose the node name routing (NNR) strategy for information-centric ad-hoc networks based on the named-node networking (3N). This strategy is especially valuable for use in disaster areas because, when the Internet is out of service during a disaster, our strategy can be used to set up a self-organizing network via cell phones or other terminal devices that have a sharing ability, and it does not rely on a base station (BS) or similar providers. Our proposed strategy can solve the multiple-name problem that has arisen in prior 3N proposals, as well as the dead loop problems in both 3N ad-hoc networks and TCP/IP ad-hoc networks. To evaluate the NNR strategy, it is compared with the optimized link state routing protocol (OLSR) and the dynamic source routing (DSR) strategy. Computer-based comprehensive simulations showed that our NNR proposal exhibits a better performance in this environment when all of the users are moving randomly. We further observed that with a growing number of users, our NNR protocol performs better in terms of packet delivery, routing cost, etc.

    DOI CiNii

  • A Novel ICN & Drone Based Emergency Information System for Disaster Area (回路とシステム)

    Wen Zheng, Zhang Di, Qi Xin, Yu Keping, Sato Takuro

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 421 ) 47 - 52  2017年01月

    CiNii

  • A Novel ICN & Drone Based Emergency Information System for Disaster Area (安全・安心な生活とICT)

    Wen Zheng, Zhang Di, Qi Xin, Yu Keping, Sato Takuro

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 422 ) 47 - 52  2017年01月

    CiNii

  • Outage probability analysis of NOMA within massive MIMO systems

    Di Zhang, Keping Yu, Zheng Wen, Takuro Sato

    IEEE Vehicular Technology Conference   2016-July  2016年07月

     概要を見る

    A Pseudo Double Scattering Channel (PDSC) Matrix assumption is proposed here for the downlink Non- Orthogonal Multiple Access (NOMA) within the massive Multi-Input Multi-Output (MIMO) systems. Afterwards, the outage probability analysis of such a system is investigated. That is, with the aid of random matrix and statistics theories, the Cumulative Probability Distribution (CDF) and also the outage probability performance are addressed. After that, the mathematics derivations obtained here are verified through numerical simulation results, wherein we further find out that with antenna number increasing, the system outage probability performance is reduced.

    DOI

  • B-6-121 3N-SIMULATOR PACKAGE TRANSMISSION ON PHYSICAL NETWORK

    Qi Xin, Zhang Lu, Wen Zheng, Sato Takuro

    電子情報通信学会総合大会講演論文集   2016 ( 2 ) 121 - 121  2016年03月

    CiNii

  • Seamless mobility in data aware networking

    Jairo E. López, M. Arifuzzaman, Li Zhu, Zheng Wen, Sato Takuro

    Proceedings of the 2015 ITU Kaleidoscope: Trust in the Information Society, K-2015 - Academic Conference     123 - 129  2016年01月

     概要を見る

    The underlying networks (of the Internet) have been reworked to make way for new technologies, some serious inefficiencies and security problems have arisen. As a result, over the past years, fundamentally new network designs have taken shape and are being tested. In ITU Recommendation Y.3001 [1], four objectives are identified in line with the requirements for Future Network; one of them is data awareness. In ITU Recommandation Y.3033 [2], the 'Mobility' is addressed as one of the key problem spaces of data aware networking (DAN). This paper proposes Named-Node-Networking (3N), a novel architecture for DAN. We design a simulator (nnnSIM) [3] for evaluating our proposed 3N architecture which is the second major contribution of this paper. The nnnSIM simulator is written in C++ under the ns-3 framework [4] and has been made available as open-source software for the scientific community. Considering the importance of a unique DAN architecture, we propose a study for standardization work in the ITU as an initiative which can lead to its rapid adaptation.

    DOI

  • Content oriented surveillance system based on information-centric network

    Xin Qi, Zheng Wen, Toshitaka Tsuda, Wataru Kameyama, Jiro Katto, Takuro Sato, Kouichi Shibata

    2016 IEEE Globecom Workshops, GC Wkshps 2016 - Proceedings    2016年

     概要を見る

    Urban surveillance systems are being applied in a rapid pace with mature but inefficient solutions. The inefficiency is revealed with two aspects, too concentrated bandwidth and processing requirement. To solve this problem, we proposed a content oriented surveillance system based on Information-Centric Network. However, we can't simply replace TCP/IP streaming structure with named contents streaming structure because it can't improve the surveillance system's efficiency enough. In this paper, we took the ICN network's profits even further with the named contents. Instead of streaming live video to the central data center and processing multiple data stream in the same time, we have designed the nodes to process the captured raw data and produce objective contents for the central data center. With the extremely size difference in raw data and actual valued contents from it, we could apply the method in investigating area people traffic conditions and even in disaster and anti-terrorism scenarios. There was a field experiment performed to evaluate tourists' densities and dressing habits during winter season of March. The experiment expressed the benefits of our system and compared our method with traditional surveillance systems in saving network bandwidth and functionalities.

    DOI

  • スマートグリッドネットワークの効率的運用に関する研究

    周 巍, 文 鄭, 佐藤 拓朗

    日本シミュレーション学会論文誌   8 ( 1 ) 25 - 31  2016年

     概要を見る

    &amp;nbsp;&amp;nbsp;Recently, miniaturization, low cost and standardization activates of the wireless sensor network technologies has been remarkably advanced, because of advancement of the wireless technologies. Especially smart grid is important application of the wireless sensor network technologies. In the smart grid network, the network congestion occurs caused by huge traffic in real time transmission of energy usage data or environment information data in the house or building. It is required to effectively collect the data and improve the network efficiency. We describes the scheme and the results in this paper since we clarified the efficient data transmission based on data fusion method for the energy usage data using Back Propagation (BP) algorithm with Artificial Neural Network.&lt;br&gt;

    DOI CiNii

  • Information Centric Networking for Disaster Information Sharing Services

    WEN Zheng, ZHANG Di, YU Keping, SATO Takuro

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   98 ( 8 ) 1610 - 1617  2015年08月

     概要を見る

    Information Centric Networking (ICN) had merits in terms of mobility, security, power consumption and network traffic. When a large-scale disaster occurred, the current communication system might be fragile and the server based network service might be unavailable due to the damages, network congestions, and power failure, etc. In this paper, we proposed an ICN based Disaster Information Sharing Service (DISS) [1], [2] system. DISS could provide robust information sharing service. Users could publish disaster information as a content message with the help of our DISS. In addition, by utilizing DISS&#039;s message naming strategy, users could retrieve disaster information even without a server connection. The ICN based DISS could reduce the probability of network congestion when a large number of simultaneous connections occurring. It could provide server-less service in poor network condition. DISS allows users retrieve disaster information from terminals or ICN nodes. During disasters, sharing information timely and effective could protect people from disaster, ensure people&#039;s safety.

    DOI CiNii

  • B-6-108 ICNベース災害情報共有システム(B-6.ネットワークシステム,一般セッション)

    張 成成, 文 鄭, 佐藤 拓朗

    電子情報通信学会総合大会講演論文集   2015 ( 2 ) 108 - 108  2015年02月

    CiNii

  • BS-3-46 Towards SE and EE in 5G with NOMA and Massive MIMO Technologies(BS-3. Advanced Technologies in the Design, Management and Control for Future Innovative Communication Network)

    Zhang Di, Yu Keping, Wen Zheng, Su Yuwei, Sato Takuro

    電子情報通信学会総合大会講演論文集   2015 ( 2 ) "S - 95"-"S-96"  2015年02月

     概要を見る

    In 5G research the spectrum efficiency (SE) and energy efficiency (EE) are two crucial issue. Some scholars propose the Massive Multiple-Input Multiple-Output (Massive MIMO) technology as a essential element to achieve the targets. In addition, the non-orthogonal multiple access (NOMA) and mm Wave technologies are proposed towards SE in 5G. In this paper, apart from the previous studies, we study the EE issue while combining the massive MIMO and NOMA together. Which can take advantage of the NOMA for better SE and antenna selection for better EE performance within the background of massive MIMO in 5G.

    CiNii

  • CCN-AMI: Performance evaluation of content-centric networking approach for advanced metering infrastructure in smart grid

    Keping Yu, Li Zhu, Zheng Wen, Arifuzzaman Mohammad, Zhenyu Zhou, Takuro Sato

    2014 IEEE International Workshop on Applied Measurements for Power Systems, AMPS 2014 - Proceedings     121 - 126  2014年11月

     概要を見る

    Advanced Metering Infrastructure (AMI), as the totality of systems and networks to measure, collect, store, analyze, and use energy usage data, is supposed to the key component that should be preferentially constructed for smart grid. In the meanwhile, content-centric networking (CCN) is perceived as a promising approach where the content itself becomes the core of communication instead of the address or location in the future internet research. Based on its caching advantage, it is widely believed that CCN can effectively reduce the AMI network bandwidth. This paper aims to apply the CCN approach on AMI system, which is called CCN-AMI. To the best of our knowledge, this is the first attempt to distribute contents or requests based on CCN in AMI system. Moreover, a simulation-based performance evaluation is employed to evaluate the effectiveness of the proposed CCN-AMI approach in traffic control for developing AMI system in smart grid.

    DOI

  • B-6-83 CCN BASED DISASTER INFORMATION SERVICE

    Wen Zheng, Zhu Li, Zhao Zhengge, Sato Takuro

    電子情報通信学会総合大会講演論文集   2014 ( 2 ) 83 - 83  2014年03月

    CiNii

  • B-6-84 PCC: Against Cache Pollution in CCN

    Wu Chao, Zhao Zhengge, Wen Zheng, Zhu Li, Shen Yang, Na Yu, Sato Takuro

    電子情報通信学会総合大会講演論文集   2014 ( 2 ) 84 - 84  2014年03月

     概要を見る

    The content-centric network (CCN) provides a new solution to what future network would be like. By caching named data on routers, CCN intends to realize higher speed and energy efficiency in Internet. In this paper, we propose an against-attack caching strategy-popularity comparing caching (PCC). The basic design concept is based on analysis of contents&#039; popularity. Simulation shows that PCC achieves higher hit ratio than LCE when there is no attack, and outperforms LCD and LCE under attack.

    CiNii

  • B-6-85 CCNにおける効率的VODサービスの検討(B-6.ネットワークシステム,一般セッション)

    趙 鄭戈, 文 鄭, 朱 力, 呉 超, 沈 揚, 佐藤 拓朗

    電子情報通信学会総合大会講演論文集   2014 ( 2 ) 85 - 85  2014年03月

    CiNii

  • B-6-91 Contents hit rate evaluation of proactive cashing utilizing transport systems based on CCN

    Zhu Li, Wen Zheng, Wu Chao, Zhao Zhengge, Shen Yang, Yu Na, Sato Takuro

    電子情報通信学会総合大会講演論文集   2014 ( 2 ) 91 - 91  2014年03月

     概要を見る

    Content Centric Network(CCN) as one of the next generation networks has the characteristic of cache storage in CCN routers which reduces congestion and latency in backbone network. This characteristic can turn into great advantage in railway scene for the large throughput such as at the train station will cause great pressure on backbone network. In this paper, we propose an content pre-fetching scheme by using CCN for railway information distribution. Simulation show that content pre-fetching scheme has better performance in the aspect of bit ratio by using same cache strategy than original CCN.

    CiNii

  • B-6-92 CCNにおける電気自動車を活用した最適移動情報通信(B-6.ネットワークシステム,一般セッション)

    沈 揚, 文 鄭, 朱 力, 呉 超, 趙 鄭戈, 余 娜, 佐藤 拓朗

    電子情報通信学会総合大会講演論文集   2014 ( 2 ) 92 - 92  2014年03月

    CiNii

  • CCN Based Pre-fetching Scheme for Disaster Scenario (無線通信システム)

    朱 力, 文 鄭, 呉 超, 趙 鄭戈, 沈 揚, 余 娜, 佐藤 拓朗

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   113 ( 456 ) 511 - 514  2014年03月

     概要を見る

    本論文では、情報通信技術(ICT)のアプローチの近代的な概念を使用して、災害シナリオのコンテンツ中心ネットワーク(CCN)スキームを提示する。コンテンツ中心ネットワークは必要がある場所に関連するコンテンツをプッシュすることを特徴として新しいネットワークである。そのキャッシュとリポジトリ機能を使用して、バックボーンネットワークの輻輳や遅延を低減することができる。災害シナリオでは、完備していないネットワーク環境のため、通信障害が発生しやすい。また、被災地の内と外両側から通常より災害に関する情報配信の高い需要、がバックボーンネットワークに大きな圧力をもたらす。事前キャッシュされた内容を詰め込みするリポジトリ機能を持つコンテンツ中心ネットワークは、キャッシュヒットとホップ距離で、堅牢性と高い性能を表す。バックボーンネットワークにおける輻輳やスループットの圧力を解放に活用できる。

    CiNii

  • Evaluation of PCC in CCN MANET (無線通信システム)

    Chao Wu, Li Zhu, Zhengge Zhao, Wen Zheng, Sato Takuro

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   113 ( 456 ) 515 - 519  2014年03月

     概要を見る

    Content Centric Network (CCN) intends to meet the rapid growth of data delivery demand. The mobile application of CCN poses a hot topic, and Mobile Ad-hoc Network (MANET), which plays a crucial role in information propagation in post-disaster rescue, combat missions and infrastructure-less applied with CCN is suggested to be more efficient. However, situation in the mobile case is different as that of wired. In this paper, we focus on the cache characteristics of mobile CCN device. We propose a novel caching decision policy -- Popularity Compare caching (PCC). Simulation show on limited memory mobile devices, PCC outperforms LCE, and achieves higher hit ratio outperforms LCD and LCE under cache attack.

    CiNii

  • CCN Based Proactive Caching Scheme for VoD Service (無線通信システム)

    趙 鄭戈, 文 鄭, 朱 力, 呉 超, 沈 揚, 佐藤 拓朗

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   113 ( 456 ) 523 - 526  2014年03月

     概要を見る

    コンテンツセントリックネットワーク(Contents Centric Network; CCN)は、従来、ノードを基準として行われていた通信形態をコンテンツを主体として行う形態へと変革させる新しい通信パラダイムである.本文はその特徴を利用して、CCNにおけるVoDサービスについて研究している.そして、新たな効率的キャッシュアルゴリズムの検討を行った。

    CiNii

  • CCN Based Mobile Disaster Information Sharing System : General Talk(Theory)

    Wen Zheng, Zhu Li, Zhao Zhengge, Wu Chao, Shen Yang, Yu Na, Sato Takuro

    電子情報通信学会技術研究報告. RCS, 無線通信システム   113 ( 456 ) 521 - 522  2014年02月

     概要を見る

    The content-centric networking (CCN) has changed the method of network service design. As the next generation network, the CCN provides significant advantages over previous networking. Nowadays, most webservers still work in terms of client to server (C/S) architecture. When a large scale disaster happens, the C/S architecture will become unstable. In this paper, we proposed a disaster information sharing method based on CCN, Content-oriented network service. The terminals could share and gain information from the CCN routers without webservers. The exchange of information is between the routers and mobile terminals. Even if the connection between CCN routers and Webservers is disconnected, our system may still provide disaster information service for terminals.

    CiNii

  • A Key Management Scheme for Secure Communications of Information Centric Advanced Metering Infrastructure in Smart Grid

    Keping Yu, Di Zhang, Arifuzzaman Mohammad, Nam Hoai Nguyen, Takuro Sato

    2014 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON)   64   2072 - 2085  2014年

     概要を見る

    The Advanced Metering Infrastructure (AMI) is the core component in Smart Grid. The cyber security is one of the major concerns and challenges should be prior considered. The Information Centric Networking (ICN) is a promising architecture for the future Internet that disseminates content based on named data instead of named hosts. It will be involved into Smart Grid because its excellent congestion control and self-security can enable more scalable, secure, collaborative and pervasive networking. This paper aims at proposing an Information Centric AMI (ICN-AMI) structure and a novel key management scheme (KMS) for a large number of smart meters (SMs) in this system to ensure confidentiality, integrality and authentication. To validate the scheme, the security analysis, comparisons and NDNsim simulation are done to demonstrate that the proposed ICN-KMS is possible solution for ICN-AMI system.

    DOI

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産業財産権

  • FIB検索器、FIB検索方法、及びプログラム

    南 勇貴, 佐藤 拓朗, 文 鄭, 斉 欣

    特許権

    J-GLOBAL

 

現在担当している科目

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他学部・他研究科等兼任情報

  • 政治経済学術院   政治経済学部

学内研究所・附属機関兼任歴

  • 2024年
     
     

    理工学術院総合研究所   兼任研究員

  • 2022年
    -
    2024年

    国際情報通信研究センター   兼任研究員

特定課題制度(学内資金)

  • Content‐oriented Emergency Information Sharing System

    2020年  

     概要を見る

    In this research, the proposed&nbsp;Content‐oriented Emergency Information Sharing System was realized and evaluated. The user can publish/retrieve the emergency information by using the ICN-based via information-centric network without any server application. And also the related works, such as ICN-based surveillance technologies, are also developed in this research.The result was published at&nbsp;2021 IEEE 18th Annual Consumer Communications &amp; Networking Conference (CCNC).