2022/01/28 更新

写真a

ヨ カクヘイ
余 恪平
所属
理工学術院 国際情報通信研究センター
職名
主任研究員(研究院講師)
プロフィール

Keping Yu received the M.E. and Ph.D. degrees from the Graduate School of Global Information and Telecommunication Studies, Waseda University, Tokyo, Japan, in 2012 and 2016, respectively. He was a Research Associate and a Junior Researcher with the Global Information and Telecommunication Institute, Waseda University, from 2015 to 2019 and 2019 to 2020, respectively, where he is currently a Researcher.

 Dr. Yu has hosted and participated in more than ten projects, is involved in many standardization activities organized by ITU-T and ICNRG of IRTF, and has contributed to ITU-T Standards Y.3071 and Supplement 35. He received the Best Paper Award from ITU Kaleidoscope 2020, the Student Presentation Award from JSST 2014. He has authored 100+ publications including papers in prestigious journal/conferences such as the IEEE Wireless Communications, ComMag, NetMag, IoTJ, TFS, TII, T-ITS, TVT, TNSE, TGCN, CEMag, IoTMag, ICC, GLOBECOM etc. He is an Associate Editor of IEEE Open Journal of Vehicular Technology, Journal of Intelligent Manufacturing, Journal of Circuits, Systems and Computers. He has been a Lead Guest Editor for Sensors, Peer-to-Peer Networking and Applications, Energies,  Journal of Internet Technology, Journal of Database Management, Cluster Computing, Journal of Electronic Imaging,  Control Engineering Practice, Sustainable Energy Technologies and Assessments and Guest Editor for IEICE Transactions on Information and Systems, Computer Communications, IET Intelligent Transport Systems, Wireless Communications and Mobile Computing, Soft Computing, IET Systems Biology. He served as general co-chair and publicity co-chair of the IEEE VTC2020-Spring 1st EBTSRA workshop, general co-chair of IEEE ICCC2020 2nd EBTSRA workshop, general co-chair of IEEE TrustCom2021 3nd EBTSRA workshop, session chair of IEEE ICCC2020, TPC co-chair of SCML2020, local chair of MONAMI 2020, Session Co-chair of CcS2020, and session chair of ITU Kaleidoscope 2016. His research interests include smart grids, information-centric networking, the Internet of Things, artificial intelligence, blockchain, and information security.

兼担

  • 理工学術院   国際理工学センター(理工学術院)

  • 政治経済学術院   政治経済学部

学歴

  • 2012年09月
    -
    2016年02月

    早稲田大学   国際情報通信研究科   博士(国際情報通信学)  

  • 2010年09月
    -
    2012年09月

    早稲田大学   国際情報通信研究科   修士(国際情報通信学)  

学位

  • 2016年02月   早稲田大学   博士(国際情報通信学)

経歴

  • 2020年04月
    -
    継続中

    早稲田大学   国際情報通信研究センター   主任研究員

  • 2019年04月
    -
    2020年03月

    早稲田大学   国際情報通信研究センター   次席研究員(研究院講師)

  • 2015年12月
    -
    2019年03月

    早稲田大学   国際情報通信研究センター   研究助手

  • 2011年11月
     
     

    NTTコミュニケーション科学基礎研究所   インターン

  • 2011年09月
    -
    2011年10月

    富士通研究開発中心有限公司(中国)   インターン

所属学協会

  • 2021年07月
    -
    継続中

    ACM

  •  
     
     

    IEICE

  •  
     
     

    IEEE

 

研究分野

  • 情報ネットワーク

研究キーワード

  • artificial intelligence

  • information security

  • blockchain

  • Internet of Things

  • information-centric networking

  • smart grids

▼全件表示

論文

  • Nonlinear MIMO for Industrial Internet of Things in Cyber-Physical Systems

    Yi Gong, Lin Zhang, Renping Liu, Keping Yu, Gautam Srivastava

    IEEE Transactions on Industrial Informatics   17 ( 8 ) 5533 - 5541  2021年08月

    担当区分:責任著者

     概要を見る

    Massive multiple-input multiple-output (MIMO) wireless communication technology with the characteristics of hyperconnectivity is an ideal channel to connect the industrial Internet of Things (IIoT) and the cyber-physical system. It provides stable and reliable connectivity from the data center to distributed user terminals and the IIoT. However, traditional massive MIMO suffers from high power consumption and fabrication cost. The design of energy-efficient massive MIMO technology is essential for larger scale industrial deployments. In this article, we design three types of nonlinear RF chain structures, which not only reduce the power consumption of massive MIMO systems but also save fabrication costs. Information theoretic analysis demonstrates the power efficiency performance of our nonlinear system design. Our nonlinear MIMO system designs can increase the power efficiency by up to 2.3 times compared with the traditional MIMO system. We have demonstrated that our systems can achieve the same uplink rate as traditional MIMO by increasing the number of receiving antennas but with less overall power consumption. We also proposed an algorithm to overcome the problem of low computational efficiency due to high-dimensional integration when calculating the uplink achievable rate of nonlinear MIMO. Moreover, we reveal that when the skew-normal distribution is used as signaling, the nonlinear MIMO systems can achieve better performance than the Gaussian distribution.

    DOI

  • Robust Spammer Detection Using Collaborative Neural Network in Internet-of-Things Applications

    Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Muhammad Imran, Neeraj Kumar, DI Zhang, Keping Yu

    IEEE Internet of Things Journal   8 ( 12 ) 9549 - 9558  2021年06月

    担当区分:最終著者, 責任著者

     概要を見る

    Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.

    DOI

  • Energy-efficient random access for leo satellite-assisted 6G internet of remote things

    Li Zhen, Ali Kashif Bashir, Keping Yu, Yasser D. Al-Otaibi, Chuan Heng Foh, Pei Xiao

    IEEE Internet of Things Journal   8 ( 7 ) 5114 - 5128  2021年04月

    担当区分:責任著者

     概要を見る

    Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

    DOI

  • Efficient and Secure Data Sharing for 5G Flying Drones: A Blockchain-Enabled Approach

    Chaosheng Feng, Keping Yu, Ali Kashif Bashir, Yasser D. Al-Otaibi, Yang Lu, Shengbo Chen, Di Zhang

    IEEE Network   35 ( 1 ) 130 - 137  2021年03月

     概要を見る

    The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs a smart contract for authentication and access control, public key cryptography for providing accounts and ensuring accounts' security, and a distributed ledger for security audit. In addition, to speed up out-sourced computations and reduce electricity consumption, an ABE model with parallel outsourced computation (ABEM-POC) is constructed, and a generic parallel computation method for ABE is proposed. The analysis of the experimental results shows that parallel computation significantly improves the speed of outsourced encryption and decryption compared to serial computation.

    DOI

  • Securing Critical Infrastructures: Deep Learning-based Threat Detection in the IIoT

    Keping Yu, Liang Tan, Shahid Mumtaz, Saba Al-Rubaye, Anwer Al-Dulaimi, Ali Kashif Bashir, Farrukh Aslam Khan

    IEEE Communications Magazine    2021年

  • Graph Embedding-based Intelligent Industrial Decision for Complex Sewage Treatment Processes

    Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Keping Yu, Jerry Chun-wei Lin

    International Journal of Intelligent Systems    2021年

    担当区分:責任著者

  • Energy-Aware Coded Caching Strategy Design with Resource Optimization for Satellite-UAV-Vehicle Integrated Networks

    Shushi Gu, Xinyi Sun, Zhihua Yang, Tao Huang, Wei Xiang, Keping Yu

    IEEE Internet of Things Journal    2021年

     概要を見る

    The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space-air-ground integrated networks (SAGINs), i.e., global seamless access, wide-area coverage and flexible traffic scheduling. However, due to the huge popular traffic volume, the limited cache/power resources and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficient of the IoV in SAGIN. In this paper, to implement the popular content severing multiple vehicle users (VUs), we consider a Cache-enabled Satellite-UAV-Vehicle Integrated Network (CSUVIN), where geosynchronous earth orbit (GEO) satellite is regard as a cloud server, unmanned aerial vehicles (UAVs) are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.

    DOI

  • PMRSS: Privacy-preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare

    Yi Sun, Jie Liu, Keping Yu, Mamoun Alazab, Kaixiang Lin

    IEEE Transactions on Industrial Informatics    2021年

    担当区分:責任著者

     概要を見る

    In medical field, previous patients' cases are extremely private as well as intensely valuable to current disease diagnosis. Therefore, how to make full use of precious cases while not leaking out patients' privacy is a leading and promising work especially in future privacy-preserving intelligent medical period. In this paper, we investigate how to securely invoke patients' records from past case-database while protecting the privacy of both current diagnosed patient and the case-database and construct a privacy-preserving medical record searching scheme based on ElGamal Blind Signature. In our scheme, by blinded the healthy data of the patient and the database of the intelligent doctor respectively, the patient can securely make self-helped medical diagnosis by invoking past case-database and securely comparing the blinded abstracts of current data and previous records. Moreover, the patient can obtain target searching information intelligently at the same time he knows whether the abstracts match or not instead of obtaining it after matching. It greatly increases the timeliness of information acquisition and meets high-speed information sharing requirements especially in 5G era. What's more, our proposed scheme achieves bilateral security, that is, whether the abstracts match or not, both of the privacy of the case-database and the private information of the current patient are well protected. Besides, it resists different levels of violent ergodic attacks by adjusting the number of zeros in a bit string according to different security requirements.

    DOI

  • Secure Artificial Intelligence of Things for Implicit Group Recommendations

    Keping Yu, Zhiwei Guo, Yu Shen, Wei Wang, Jerry Chun Wei Lin, Takuro Sato

    IEEE Internet of Things Journal    2021年

     概要を見る

    The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications such as group recommender systems. As the distances between people have been greatly shortened, there has been more general demand for the provision of personalized services aimed at groups instead of individuals. The existing methods for capturing group-level preference features from individuals have mostly been established via aggregation and face two challenges: secure data management workflows are absent, and implicit preference feedback is ignored. To tackle these current difficulties, this paper proposes secure AIoT for implicit group recommendations (SAIoT-GR). For the hardware module, a secure IoT structure is developed as the bottom support platform. For the software module, a collaborative Bayesian network model and noncooperative game are introduced as algorithms. This secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of SAIoT-GR in terms of efficiency and robustness.

    DOI

  • Deep Learning Empowered Breast Cancer Auxiliary Diagnosis for 5GB Remote E-Health

    Keping Yu, Liang Tan, Long Lin, Xiaofan Chen, Zhang Yi, Takuro Sato

    IEEE Wireless Communications Magazine    2021年

  • A Fuzzy Detection System for Rumors through Explainable Adaptive Learning

    Zhiwei Guo, Keping Yu, Alireza Jolfaei, Ali Kashif Bashir, Alaa Omran Almagrabi, Neeraj Kumar

    IEEE Transactions on Fuzzy Systems     1 - 1  2021年

    担当区分:責任著者

     概要を見る

    Nowadays, rumor spreading has gradually evolved into a kind of organized behaviors, accompanied with strong uncertainty and fuzziness. However, existing fuzzy detection techniques for rumors focused their attention on supervised scenarios which require expert samples with labels for training. Thus they are not able to well handle unsupervised scenarios where labels are unavailable. To bridge such gap, this paper proposes a fuzzy detection system for rumors through explainable adaptive learning. Specifically, its core is a graph embedding-based generative adversarial network (Graph-GAN) model. First of all, it constructs fine-grained feature spaces via graph-level encoding. Furthermore, it introduces continuous adversarial training between a generator and a discriminator for unsupervised decoding. The two-stage scheme not only solves fuzzy rumor detection under unsupervised scenarios, but also improves robustness of the unsupervised training. Empirically, a set of experiments are carried out based on three real-world datasets. Compared with seven benchmark methods in terms of four metrics, the results of Graph-GAN reveal a proper performance which averagely exceeds baselines by 5% to 10%.

    DOI

  • Blockchain-Enhanced Data Sharing with Traceable and Direct Revocation in IIoT

    Keping Yu, Liang Tan, Moayad Aloqaily, Hekun Yang, Yaser Jararweh

    IEEE Transactions on Industrial Informatics     1 - 1  2021年

     概要を見る

    The Industrial Internet of things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semi-credibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users, has become an urgent problem. To solve these problems, a blockchain-enhanced security access control scheme that supports traceability and revocability has been proposed in IIoT for smart factories. The blockchain first performs unified identity authentication, and stores all public keys, user attribute sets, and revocation list. The system administrator then generates system parameters and issues private keys to users. The domain administrator is responsible for formulating domain security and privacy protection policies and performing encryption operations. If the attributes meet the access policies and the user's ID is not in the revocation list, they can obtain the intermediate decryption parameters from the edge/cloud servers. Malicious users can be tracked and revoked during all stages if needed, which ensures the system security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption and can resist multiple attacks. The evaluation has shown that the size of the public/private keys is smaller compared to other schemes, and the overhead time is less for public key generation, data encryption, and data decryption stages.

    DOI

  • Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System

    Keping Yu, Long Lin, Mamoun Alazab, Liang Tan, Bo Gu

    IEEE Transactions on Intelligent Transportation Systems     1 - 11  2020年

     概要を見る

    It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.

    DOI

  • A Blockchain-based Shamir’s Threshold Cryptography for Data Protection in Industrial Internet of Things of Smart City

    L. Tan, K. Yu, C. Yang, A. K. Bashir

    The 27th Annual International Conference On Mobile Computing And Networking (MobiCom 2021)    2021年10月

  • Cross-domain Authentication for 5G-enabled UAVs: A Blockchain Approach

    B. Liu, K. Yu, C. Feng, K. -K. R. Choo

    The 27th Annual International Conference On Mobile Computing And Networking (MobiCom 2021)    2021年10月

  • Energy-efficient user association with load-balancing for cooperative IIoT network within B5G era

    Xin Jian, Langyun Wu, Keping Yu, Moayad Aloqaily, Jalel Ben-Othman

    Journal of Network and Computer Applications   189  2021年09月

    担当区分:責任著者

     概要を見る

    As one of the key technologies of 5G wireless communication technology, cooperative multi-access edge computing allows one device to associate multiple edge nodes simultaneously, namely multi-association, which can provide scalable communication services with characteristics of high reliability, massive connectivity and low latency for promising Industrial Internet of Things (IIoT). Effective association between edge nodes and devices is the prerequisite for providing high quality communication services in dense deployed IIoT networks. Most of state of art researches focus on the user association problem in single-association scenario. There are rarely no solutions presented for the considered user association problem with multi-association. In this paper, user association, power allocation and edge node deployment are jointly considered for load balance and energy efficiency under the multi-association mechanism. The problem is formulated as a nested knapsack optimization problem (NKOP) with energy efficiency and load balancing as objective functions and power and signal quality as constraints. Differential evolution with Monte Carlo and sequential quadratic programming (DMS) algorithm is proposed to solve this problem, which decouples the problem into three parts, user association, power allocation and optimizing the location of edge nodes. Numerical results show that: (1) Compared with the single-association, multi-association with power allocation can provide better signal quality and improve energy efficiency; (2) Proposed DMS algorithm is feasible and stable for optimal deployment of edge nodes. These works together provide good reference for edge node deployment of high-density IIoT application scenarios.

    DOI

  • RON-enhanced blockchain propagation mechanism for edge-enabled smart cities

    Junjie Huang, Liang Tan, Wenjuan Li, Keping Yu

    Journal of Information Security and Applications   61  2021年09月

    担当区分:最終著者

     概要を見る

    Edge computing can serve latency-sensitive data generated by smart IoT devices in smart cities, which cannot be accommodated by cloud services. However, edge-enabled smart cities applications face many security issues such as massive centralization, vulnerability to tampering, and tracing difficulty. Blockchain, as an emerging ledger technology, can be a useful solution to these problems. However, due to network propagation delay, blockchain fork may occur in the network of edge-enabled smart cities at some time. It can lead to double payment attacks and damage data integrity. To address this issue, this paper proposes a blockchain network propagation mechanism based on the resilient overlay network (RON) for edge-enabled smart cities. Firstly, it reconstructs the networking mode between the blockchain nodes in smart cities based on RON so that the blockchain nodes can quickly detect the link state of the Internet in smart cities. The shortest path algorithm and policy routing are then applied to construct the propagation path between the blockchain nodes to optimize the Gossip propagation mechanism and enhance the QoS and QoE. Simulation results show that the RON-based blockchain network propagation mechanism for edge-enabled smart cities is beneficial in terms of average route hop count, transmission success rate, routing overhead, average delay, and reduced fork probability.

    DOI

  • Content-oriented Multicamera Trajectory Forecasting Surveillance Network System

    X. Qi, T. Sato, K. Yu, Z. Wen, S. H. Myint, Y. Katsuyama, K. Tamesue, K. Tokuda, T. Sato

    2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)    2021年08月

  • Towards Secure and Privacy-Preserving Data Sharing for COVID-19 Medical Records: A Blockchain-Empowered Approach

    Liang Tan, Keping Yu, Na Shi, Caixia Yang, Wei Wei, Huimin Lu

    IEEE Transactions on Network Science and Engineering    2021年08月

    担当区分:責任著者

  • 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

    DOI

  • Question-aware memory network for multi-hop question answering in human–robot interaction

    X. Li, M. Alazab, Q. Li, K. Yu, Q. Yin

    Complex & Intelligent Systems    2021年07月

  • Efficient Collision Detection Based on Zadoff-Chu Sequences for Satellite-Enabled M2M Random Access

    L. Zhen, H. Kong, Y. Zhang, W. Wang, K. Yu

    IEEE International Conference on Communications (IEEE ICC 2021)    2021年06月

  • Throughput Maximization for Energy Harvesting based Relay Cooperative Backscattering Transmission

    W. Wang, K. Xu, L. Zhen, K. Yu, A. K. Bashir, S. Garg

    IEEE International Conference on Communications (IEEE ICC 2021)    2021年06月

  • Robust Electronic Nose in Industrial Cyber Physical Systems based on Domain Adaptive Subspace Transfer Model

    T. Guo, K. Yu, X. Cheng, A. K. Bashir

    IEEE International Conference on Communications (IEEE ICC 2021)    2021年06月

  • Latent Discriminative Low-Rank Projection for Visual Dimension Reduction in Green Internet of Things

    Tan Guo, Keping Yu, Gautam Srivastava, Wei Wei, Lei Guo, Neal N. Xiong

    IEEE Transactions on Green Communications and Networking   5 ( 2 ) 737 - 749  2021年06月

    担当区分:責任著者

     概要を見る

    Internet of Things (IoT) terminals have been widely deployed for data sensing and analysis, and efficient data storage and transmission plays an important role in green IoT due to the explosive data growth. To simultaneously reduce the data dimension and preserves the discriminative intrinsic knowledge of data, this paper develops a novel latent discriminative low-rank projection (LDLRP) method for visual dimension reduction. Specifically, a data self-expressiveness model is established by considering the low-rank and discriminative similarity relations of data. Then, the developed model is efficiently optimized and solved via an augmented Lagrange multiplier (ALM) based-iterative algorithm, and a block-diagonal solution can be found for intraclass and interclass graph construction. Afterwards, a discriminative dimension reduced-subspace is derived by concurrently minimizing the intraclass scatter and maximizing the interclass scatter. The experimental results on benchmark datasets show that the proposed method can learn discriminative lower-dimensional expressions of high-dimensional data, and yield promising classification accuracy compared with several state-of-the-art methods. Hence, the effectiveness and efficiency of proposed method in data dimension reduction and knowledge preservation are verified, which will facilitate efficient data storage, transmission and application in green IoT.

    DOI

  • A blockchain-empowered crowdsourcing system for 5G-enabled smart cities

    Liang Tan, Huan Xiao, Keping Yu, Moayad Aloqaily, Yaser Jararweh

    Computer Standards and Interfaces   76   103517 - 103517  2021年06月

    担当区分:責任著者

     概要を見る

    With the development of 5G(5th generation mobile networks) technology, smart cities are an inevitable trend in modern city development. Among them, smart city services are the foundation of 5G-enabled smart cities. As an emerging and informational city service model, crowdsourcing has been widely used in our daily life. However, in the existing crowdsourcing systems, the requesters and the workers are usually required to use the crowdsourcing platform as the trust center, and the payment depends on the third-party central payment institutions, which have a massive security risk. Once these centers are attacked or do evil, it will bring higher losses to the crowdsourcing parties. These problems will negatively affect the further development of 5G-enabled smart cities. To address these issues, we propose a blockchain-empowered and decentralized trusted service mechanism for the crowdsourcing system in 5G-enabled smart cities. In the proposed mechanism, the crowdsourcing service process is divided into nine stages: initialization, task submission, task publication, task reception, scheme submission, scheme arbitration, payment, task rollback, and service compensation. The smart contract controls the execution of each step in each stage, and the payment is completed by blockchain without the involvement of third-party central institutions. Finally, we develop smart contracts to conduct experiments based on Ethereum and compare it with the existing crowdsourcing system. The experimental results show the effectiveness and applicability of the crowdsourcing system service mechanism without the central institutions.

    DOI

  • A cooperative resource allocation model for IoT applications in mobile edge computing

    Xianwei Li, Liang Zhao, Keping Yu, Moayad Aloqaily, Yaser Jararweh

    Computer Communications   173   183 - 191  2021年05月

     概要を見る

    With the advancement in the development of the Internet of Things (IoT) technology, as well as the industrial IoT, various applications and services are benefiting from this emerging technology such as smart healthcare systems, virtual realities applications, connected and autonomous vehicles, to name a few. However, IoT devices are known for being limited computation capacities which is crucial to the device's availability time. Traditional approaches used to offload the applications to the cloud to ease the burden on the end user's devices, however, greater latency and network traffic issues still persist. Mobile Edge Computing (MEC) technology has emerged to address these issues and enhance the survivability of cloud infrastructure. While a lot of attempts have been made to manage an efficient process of applications offload, many of which either focus on the allocation of computational or communication protocols without considering a cooperative solution. In addition, a single-user scenario was considered. Therefore, we study multi-user IoT applications offloading for a MEC system, which cooperatively considers to allocate both the resources of computation and communication. The proposed system focuses on minimizing the weighted overhead of local IoT devices, and minimize the offload measured by the delay and energy consumption. The mathematical formulation is a typical mixed integer nonlinear programming (MINP), and this is an NP-hard problem. We obtain the solution to the objective function by splitting the objective problem into three sub-problems. Extensive set of evaluations have been performed so as to get the evaluation of the proposed model. The collected results indicate that offloading decisions, energy consumption, latency, and the impact of the number of IoT devices have shown superior improvement over traditional models.

    DOI

  • Challenge-based collaborative intrusion detection in software-defined networking: an evaluation

    Wenjuan Li, Yu Wang, Zhiping Jin, Keping Yu, Jin Li, Yang Xiang

    Digital Communications and Networks   7 ( 2 ) 257 - 263  2021年05月

     概要を見る

    Software-Defined Networking (SDN) is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications. It can help manage the whole network environment in a consistent and holistic way, without the need of understanding the underlying network structure. At present, SDN may face many challenges like insider attacks, i.e., the centralized control plane would be attacked by malicious underlying devices and switches. To protect the security of SDN, effective detection approaches are indispensable. In the literature, challenge-based Collaborative Intrusion Detection Networks (CIDNs) are an effective detection framework in identifying malicious nodes. It calculates the nodes’ reputation and detects a malicious node by sending out a special message called a challenge. In this work, we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes. Our results demonstrate that such a mechanism can be effective in SDN environments.

    DOI

  • Agent architecture of an intelligent medical system based on federated learning and blockchain technology

    Dawid Połap, Gautam Srivastava, Keping Yu

    Journal of Information Security and Applications   58   102748 - 102748  2021年05月

    担当区分:最終著者

     概要を見る

    Multi-agent systems enable the division of complicated tasks into individual objects that can cooperate. Such architecture can be useful in building solutions in the Internet of Medical Things (IoMT). In this paper, we propose an architecture of such a system that ensures the security of private data, as well as allows the addition and/or modification of the used classification methods. The main advantages of the proposed system are based on the implementation of blockchain technology elements and threaded federated learning. The individual elements are located on the agents who exchange information. Additionally, we propose building an agent with a consortium mechanism for classification results from many machine learning solutions. This proposal offers a new model of agents that can be implemented as a system for processing medical data in real-time. Our proposition was described and tested to present advantages over other, existing state-of-the-art methods. We show, that this proposition can improve the Internet of Medical Thing solutions by presenting a new idea of a multi-agent system that can separate different tasks like security, or classification and as a result minimize operation time and increase accuracy.

    DOI

  • Deep Graph neural network-based spammer detection under the perspective of heterogeneous cyberspace

    Zhiwei Guo, Lianggui Tang, Tan Guo, Keping Yu, Mamoun Alazab, Andrii Shalaginov

    Future Generation Computer Systems   117   205 - 218  2021年04月

    担当区分:責任著者

     概要を見る

    Due to the severe threat to cyberspace security, detection of online spammers has been a universal concern of academia. Nowadays, prevailing literature of this field almost leveraged various relations to enhance feature spaces. However, they majorly focused stable or visible relations, yet neglected the existence of those which are generated occasionally. Exactly, some latent feature components can be extracted from the view of heterogeneous information networks. Thus, this paper proposes a Deep Graph neural network-based Spammer detection (DeG-Spam) model under the perspective of heterogeneous cyberspace. Specifically, representations for occasional relations and inherent relations are separately modelled. Based on this, a graph neural network framework is formulated to generate feature expressions for the social graph. With more feature components being mined, acquirement of stronger and more comprehensive feature spaces ensures the accuracy of spammer detection. At last, fruitful experiments are carried out on two benchmark datasets to compare the DeG-Spam with typical spammer detection approaches. Experimental results show that it performs about 5%–10% better than baselines.

    DOI

  • Blockchain-empowered contact tracing for COVID-19 using crypto-spatiotemporal information

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

    2020 IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020    2021年03月

     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

  • Efficient and Privacy-Preserving Medical Research Support Platform against COVID-19: A Blockchain-Based Approach

    Keping Yu, Liang Tan, Xinglin Shang, Junjie Huang, Gautam Srivastava, Pushpita Chatterjee

    IEEE Consumer Electronics Magazine   10 ( 2 ) 111 - 120  2021年03月

     概要を見る

    COVID-19 is a major global public health challenge and difficult to control in a short time completely. To prevent the COVID-19 epidemic from continuing to worsen, global scientific research institutions have actively carried out studies on COVID-19, thereby effectively improving the prevention, monitoring, tracking, control, and treatment of the epidemic. However, the COVID-19 electronic medical records (CEMRs) among hospitals worldwide are managed independently. With privacy consideration, CEMRs cannot be made public or shared, which is not conducive to in-depth and extensive research on COVID-19 by medical research institutions. In addition, even if new research results are developed, the disclosure and sharing process is slow. To address this issue, we propose a blockchain-based medical research support platform, which can provide efficient and privacy-preserving data sharing against COVID-19. First, hospitals and medical research institutions are treated as nodes on the alliance chain, so consensus and data sharing among the nodes is achieved. Then, COVID-19 patients, doctors, and researchers need to be authenticated in various institutes. Moreover, doctors and researchers need to be registered with the Fabric certificate authority. The CEMRs for COVID-19 patients uses the blockchain's pseudonym mechanism to protect privacy. After that, doctors upload CEMRs on the alliance chain, and researchers can obtain CEMRs from the alliance chain for research. Finally, the research results will be published on the blockchain for doctors to use. The experimental results show that the read and write performance and security performance on the alliance chain meet the requirements, which can promote the wide application of scientific research results against COVID-19.

    DOI

  • Probabilistic inference-based modeling for sustainable environmental systems under hybrid cloud infrastructure

    Zhiwei Guo, Yu Shen, Moayad Aloqaily, Yaser Jararweh, Keping Yu

    Simulation Modelling Practice and Theory   107   102215 - 102215  2021年02月

    担当区分:責任著者

     概要を見る

    Data-driven modeling for wastewater treatment process (WWTP) under hybrid cloud environment, has been widely regarded as a promising solution. Existing methods managed to learn a forward mapping for WWTP, and were highly reliable on rich intermediate process parameters (IPP) such as dissolved oxygen amount. However, they cannot well handle scenes where IPP are unavailable. In fact, such situations are quite common because many wastewater treatment plants still lack relevant monitoring systems. To remedy such gap, this research collected real-world data from wastewater treatment plants to build realistic experimental scenarios. On this foundation, a probabilistic model for WWTP, named Pro-WWTP for short, is proposed in this paper. More concretely, generative processes of outlet results are expressed as conditional probability, and IPP are estimated via Gibbs sampling-based Bayesian posterior probabilistic inference. Empirically, we conduct two groups of experiments to evaluate proactivity of the proposed Pro-WWTP. Experimental results reveal that Pro-WWTP possesses proper recovery precision for IPP and is able to promote modeling efficiency. Besides, another group of experiments are further implemented to verify total robustness of Pro-WWTP.

    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

  • 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    2021年

    担当区分:責任著者

  • Novel Anchor-Selection Scheme for Distributed Mobility Management

    Battulga Davaasambuu, Tumnee Telmuun, Dominik Sasko, Yu Keping, Shirmen Sodbileg

    Computer Science   22 ( 1 ) 143 - 164  2021年

     概要を見る

    The number of subscribers in mobile networks is growing rapidly, which chal- lenges network management and data delivery. E cient management and rout- ing are key solutions. One important solution is distributed mobility manage- ment (DMM), which handles the mobility of subscribers at the edges of mo- bile networks and load balancing. Otherwise, mobility anchors are distributed across a network that can manage the handover procedures. In this paper, we propose a novel mobility anchor-selection scheme based on the results of a cost function with three factors to select a suitable cell as well as an anchor for moving subscribers and improving the handover performances of networks. Our results illustrate that the proposed scheme provides signicantly enhanced handover performance.

    DOI

  • Enhancing cancer driver gene prediction by protein-protein interaction network

    Chuang Liu, Yao Dai, Keping Yu, Zi K. Zhang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics    2021年

     概要を見る

    With the advances in gene sequencing technologies, millions of somatic mutations have been reported in the past decades, but mining cancer driver genes with oncogenic mutations from these data remains a critical and challenging area of research. In this study, we proposed a network-based classification method for identifying cancer driver genes with merging the multi-biological information. In this method, we construct a cancer specific genetic network from the human protein-protein interactome to mine the network structure attributes, and combine biological information such as mutation frequency and differential expression of genes to achieve accurate prediction of cancer driver genes. Across seven different cancer types, the proposed algorithm always achieves high prediction accuracy, which is superior to the existing advanced methods. In the analysis of the predicted results, about 40\% of the top 10 candidate genes overlap with the Cancer Gene Census database. Interestingly, the feature comparison indicates that the network based features are still more important than the biological features, including the mutation frequency and genetic differential expression. Further analyses also show that the integration of network structure attributes and biological information is valuable for predicting new cancer driver genes.

    DOI

  • An Efficient Ciphertext-Policy Weighted Attribute-Based Encryption for the Internet of Health Things

    Hang Li, Keping Yu, Bin Liu, Chaosheng Feng, Zhiguang Qin, Gautam Srivastava

    IEEE Journal of Biomedical and Health Informatics    2021年

     概要を見る

    The Internet of Health Things (IoHT) is a concept that describes uniquely identifiable devices connected to the Internet and able to communicate with each other in the medical area. As one of the most important components of smart health monitoring and improvement systems, there are numerous challenges in the IoHT, among which cybersecurity is a major challenge that must be addressed with priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison, remain. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against chosen-plaintext attack.

    DOI PubMed

  • Enhanced Conditional Handover for 5G Heterogeneous Networks

    Tumnee Telmuun, Keping Yu, Davaasambuu Battulga

    Smart Innovation, Systems and Technologies   212   212 - 220  2021年

     概要を見る

    The first wave of 5G networks is already coming and ready to affect our life. One of the main parts of mobile networks is mobility management that provides seamless mobility from the current cell to the neighbor. 3GPP Rel.15 teams developed the conditional handover for a single connection to improve mobility robustness. The prepared multiple target cells in a preparation step are an enhancement of the conditional handover scheme. Also, this enhancement improves performance related to the user’s mobility in the mobile network. In this paper, we present the enhanced conditional handover that can provide a reduction of handover failures. But, the increased signaling overhead and ping-pong handovers are the main problems. From simulation results, our proposal provides the reduced measurement reports and improved handover performance compared with the baseline handovers in the heterogeneous networks.

    DOI

  • Secure and Resilient Artificial Intelligence of Things: a HoneyNet Approach for Threat Detection and Situational Awareness

    Liang Tan, Keping Yu, Fangpeng Ming, Xiaofeng Chen, Gautam Srivastava

    IEEE Consumer Electronics Magazine    2021年

    担当区分:責任著者

     概要を見る

    Artificial Intelligence of Things (AIoT) is emerging as the future of Industry 4.0 and will be widely applied in consumer, commercial, and industrial fields. In AIoT, intelligent objects (smart devices), smart gateways, and edge/cloud nodes are subject to a large number of security threats and attacks. However, the traditional network security approaches are not fully suitable for AIoT. To address this issue, this paper proposes a HoneyNet approach that includes both threat detection and situational awareness to enhance the security and resilience of AIoT. We first design a HoneyNet based on Docker technology that collects data to detect adversaries and monitor their attack behaviors. The collected data are then converted into images and used as samples to train a deep learning model. Finally, the trained model is deployed in AIoT to perform threat detection and provide situational awareness. To validate our scheme, we conduct HoneyNet deployment and model training on the SiteWhere AIoT platform and construct a simulation environment on this platform for threat detection and situational awareness. The experimental results demonstrate the feasibility and effectiveness of our solution.

    DOI

  • Aggregated Decentralized Down-sampling-based ResNet for Smart Healthcare Systems

    Zhiwen Jiang, Ziji Ma, Yaonan Wang, Xun Shao, Keping Yu, Alireza Jolfaei

    Neural Computing and Applications    2021年

    DOI

  • Towards Real-time and Efficient Cardiovascular Monitoring for COVID-19 Patients by 5G-Enabled Wearable Medical Devices: A Deep Learning Approach

    Neural Computing and Applications    2021年

    担当区分:責任著者

    DOI

  • Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing

    Qianmu Li, Shunmei Meng, Xiaonan Sang, Hanrui Zhang, Shoujin Wang, Ali Kashif Bashir, Keping Yu, Usman Tariq

    ACM Transactions on Internet Technology    2021年

    担当区分:責任著者

  • When Internet of Things Meets E-Health: An Indoor Temperature Monitoring and Control Approach

    Shengbo Chen, Jingtian Wang, Lanxue Zhang, Keping Yu, Ali Kashif Bashir, Rupak Kharel, Celimuge Wu

    IEEE Internet of Things Magazine    2021年

    担当区分:責任著者

  • A Blockchain-Empowered Access Control Framework for Smart Devices in Green Internet of Things

    Liang Tan, Na Shi, Keping Yu, Moayad Aloqaily, Yaser Jararweh

    ACM Transactions on Internet Technology    2021年

    担当区分:責任著者

  • Early Collision Detection for Massive Random Access in Satellite-Based Internet of Things

    Li Zhen, Yukun Zhang, Keping Yu, Neeraj Kumar, Ahmed Barnawi, Yong Bin Xie

    IEEE Transactions on Vehicular Technology    2021年

    担当区分:責任著者

     概要を見る

    As a complementary solution for seamless and ubiq- uitous coverage, satellite communications will play crucial roles in future global Internet of Things (IoT). Focusing on enhancing access efficiency and resource utilization of massive machine- type devices (MTDs), we propose an efficient collision detection scheme at the first step of random access (RA) procedure for satellite-based IoT. By leveraging a single root Zadoff-Chu (ZC) sequence with an elaborate set of cyclic shift offsets to generate all the available preamble sequences, the proposed scheme can achieve rapid collision detection and load estimation in one- shot correlation operation, while having the robustness to the non-orthogonal interference. The preamble detection probability, collision detection probability, and load monitoring accuracy, are mathematically analyzed, and an optimal set of preamble selection probabilities is given to maximize the overall load monitoring accuracy. Simulation results validate the remarkable performance improvement of our scheme by compared to the state-of-the-art collision detection schemes.

    DOI

  • A Displacement Estimated Method for Real Time Tissue Ultrasound Elastography

    Hong an Li, Min Zhang, Keping Yu, Xin Qi, Jianfeng Tong

    Mobile Networks and Applications    2021年

    担当区分:責任著者

     概要を見る

    As an important means of medical imaging, elastic imaging is an indispensable part of mobile telemedicine. Ultrasound elastography has become a research hotspot because it can accurately measure soft tissue lesions. Displacement estimation is the most important step in ultrasound elastography. At present, the phase zero search method is an accurate and fast displacement estimation method. However, when the displacement exceeds 1/4 wavelength, it is invalid. The accuracy of block matching method is not high, but it is suitable for large displacement, so it can overcome this shortcoming. It is worth noting that the quality-guided block matching method has good robustness under complex mutation conditions. It can provide prior knowledge to increase the robustness of the phase-zero search under large displacement conditions. So we propose a novel displacement estimation method for real time tissue ultrasound elastography, which combines the quality-guided block matching method and the phase-zero search method. The experimental results show that this method is more accurate, faster and robust than other displacement estimation methods.

    DOI

  • Data-driven management for fuzzy sewage treatment processes using hybrid neural computing

    Wenru Zeng, Zhiwei Guo, Yu Shen, Ali Kashif Bashir, Keping Yu, Yasser D. Al-Otaibi, Xu Gao

    Neural Computing and Applications    2021年

    担当区分:責任著者

     概要を見る

    With the growing public attention on sustainable development and green ecosystems, the efficient management of fuzzy sewage treatment processes (FSTPs) has been a major concern in academia. Characterized by strong abstraction and analysis abilities, data mining technologies provide a novel perspective to solve this problem. In recent years, data-driven management for FSTP has been widely investigated, resulting in a number of typical approaches. However, almost all existing technical approaches consider FSTP a unidirectional, sequential process, ignoring the bidirectional temporality caused by backflow operations. Therefore, we propose a data-driven management mechanism for FSTP based on hybrid neural computing (IM-HNC for short). This mechanism attempts to capture the bidirectional time-series features of FSTP with the aid of a bidirectional long short-term memory model, and further introduces a convolutional neural network to construct feature spaces with a stronger expression capability. Empirically, we implement a series of experiments on three datasets under different parameter settings to test the efficiency and robustness of the proposed IM-HNC. The experimental results manifest that the IM-HNC has an average performance improvement of approximately 5% compared to the baselines.

    DOI

  • Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations

    Zhiwei Guo, Keping Yu, Yu Li, Gautam Srivastava, Jerry Chun Wei Lin

    IEEE Transactions on Network Science and Engineering    2021年

    担当区分:責任著者

     概要を見る

    With the increasing demand of users for personalized social services, social recommendation (SR) has been an important concern in academia. However, current research on SR universally faces two main challenges. On the one hand, SR lacks the considerable ability of robust online data management. On the other hand, SR fails to take the ambiguity of preference feedback into consideration. To bridge these gaps, a deep learning-embedded social Internet of Things (IoT) is proposed for ambiguity-aware SR (SIoT-SR). Specifically, a social IoT architecture is developed for social computing scenarios to guarantee reliable data management. A deep learning-based graph neural network model that can be embedded into the model is proposed as the core algorithm to perform ambiguity-aware SR. This design not only provides proper online data sensing and management but also overcomes the preference ambiguity problem in SR. To evaluate the performance of the proposed SIoT-SR, two real-world datasets are selected to establish experimental scenarios. The method is assessed using three different metrics, selecting five typical methods as benchmarks. The experimental results show that the proposed SIoT-SR performs better than the benchmark methods by at least 10% and has good robustness.

    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

  • Implicit Feedback-based Group Recommender System for Internet of Things Applications

    Zhiwei Guo, Keping Yu, Tan Guo, Ali Kashif Bashir, Muhammad Imran, Mohsen Guizani

    2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings   2020-January  2020年12月

     概要を見る

    With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods.

    DOI

  • An Intelligent Management Mechanism for Residential Power under Software Defined Network

    Wenru Zeng, Boxin Du, Zhiwei Guo, Keping Yu, Xu Gao, Yu Shen

    2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings    2020年12月

     概要を見る

    The residential power is the lifeblood of the national economy, and its efficient management is of great significance. Precise prediction of the residential power demand has always been a most important concern of management mechanism which can be carried by a software defined network (SDN) platform. However, existing methods are heavily reliable on data with multiple features and high dimensions, failing to discovering sequential characteristics from simple and sparse data. In this paper, we develop a residual correction-based grey prediction model for residential power management under SDN. In detail, the residual function is used to correct the prediction value of the traditional gray model, so that prediction accuracy can be improved. Besides, a set of computational experiments are carried out on real-world business data to assess precision accuracy of the proposed model. It is concluded through experiments that the proposed model can better predict residential power demand.

    DOI

  • Dynamic Polygon Generation for Flexible Pattern Formation in Large-Scale UAV Swarm Networks

    Gunasekaran Raja, Kottilingam Kottursamy, Ajay Theetharappan, Korhan Cengiz, Aishwarya Ganapathisubramaniyan, Rupak Kharel, Keping Yu

    2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings    2020年12月

     概要を見る

    A UAV swarm network is a network formed by aggregating a large number of UAVs and coordinate them to execute a specific mission, especially in areas where human intervention is not physically possible or economically viable. The process of coordinating and maintaining a UAV swarm network has various phases. The pattern formation phase is one of the important phases and is highly significant in missions where geography is an important aspect of the mission. For the purpose of automating the pattern generation process, this paper proposes the dynamic polygon generation (DPGen) algorithm that can generate convex polygonal pattern with any number of vertices in linear time. The DPGen algorithm generates a pattern dynamically for any number of drones which increases the scalability of the UAV swarm networks, increasing the magnitude of use-cases of the swarm. The DPGen algorithm contains a mechanism to use this algorithm in a decentralized manner while balancing the load on all the UAVs in the network. The usage of DPGen algorithm reduces the network traffic in the UAV swarm network by 78.26% and decreases the power requirement of the leader drone by 74.39%.

    DOI

  • The effect of eye movements and cultural factors on product color selection

    Bo Wu, Yishui Zhu, Keping Yu, Shoji Nishimura, Qun Jin

    Human-centric Computing and Information Sciences   10 ( 1 )  2020年12月

     概要を見る

    A color is a powerful tool used to attract people’s attention and to entice them to purchase a product. However, the way in which a specific color influences people’s color selection and the role of their eye movements and cultural factors in this process remain unknown. In this study, to delve into this problem, we designed an experiment to determine the influence of specific colors on people’s product preferences by using an eye-tracking device, intending to identify the role of their eye movements and cultural factors. Based on the experimental data, a detailed influence path model was built to describe the effect of specific colors on product evaluations by an integrated moderation and mediation analysis. Our findings show that in the influence process, the effects of specific colors on product evaluations are mediated by eye movements. Additionally, cultural factors partly moderate the process as an influencing factor. The research findings from this study have important implications for user-centered product design and visual marketing management.

    DOI

  • Attribute-Based Encryption with Parallel Outsourced Decryption for Edge Intelligent IoV

    Chaosheng Feng, Keping Yu, Moayad Aloqaily, Mamoun Alazab, Zhihan Lv, Shahid Mumtaz

    IEEE Transactions on Vehicular Technology   69 ( 11 ) 13784 - 13795  2020年11月

    担当区分:責任著者

     概要を見る

    Edge intelligence is an emerging concept referring to processes in which data are collected and analyzed and insights are delivered close to where the data are captured in a network using a selection of advanced intelligent technologies. As a promising solution to solve the problems of insufficient computing capacity and transmission latency, the edge intelligence-empowered Internet of Vehicles (IoV) is being widely investigated in both academia and industry. However, data sharing security in edge intelligent IoV is a challenge that should be solved with priority. Although attribute-based encryption (ABE) is capable of addressing this challenge, many time-consuming modular exponential operations and bilinear pair operations as well as serial computing cause ABE to have a slow decryption speed. Consequently, it cannot address the response time requirement of edge intelligent IoV. Given this problem, an ABE model with parallel outsourced decryption for edge intelligent IoV, called ABEM-POD, is proposed. It includes a generic parallel outsourced decryption method for ABE based on Spark and MapReduce. This method is applicable to all ABE schemes with a tree access structure and can be applied to edge intelligent IoV. Any ABE scheme based on the proposed model not only supports parallel outsourced decryption but also has the same security as the original scheme. In this paper, ABEM-POD has been applied to three representative ABE schemes, and the experiments show that the proposed ABEM-POD is efficient and easy to use. This approach can significantly improve the speed of outsourced decryption to address the response time requirement for edge intelligent IoV.

    DOI

  • 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

  • Indoor temperature monitoring using wireless sensor networks: A SMAC application in smart cities

    Shengbo Chen, Lanxue Zhang, Yuanmin Tang, Cong Shen, Roshan Kumar, Keping Yu, Usman Tariq, Ali Kashif Bashir

    Sustainable Cities and Society   61   102333 - 102333  2020年10月

    担当区分:責任著者

     概要を見る

    Social, Mobile, Analytics and Cloud (SMAC) technologies aim to bridge the cyber, physical and social spaces. The use of wireless sensor networks to monitor indoor temperature is a typical application in smart cities. Rather than splitting the measured temperature and the design of a sensor network, a cyber-physical design approach is proposed by this paper for indoor temperature monitoring using wireless sensor network. The source sensors adopt sleep/wake scheduling, that is, source nodes wake up and sense the temperature periodically. The temperature data is sent to the cloud server via multi-hop relaying sensor nodes in an anycast way. Each sensor decides how to route packets based on its local information and dynamically adjust the sleep/wake duty cycle according to the sensed temperature: if the measured temperature is within normal range, the sensor wakes up infrequently to achieve higher energy efficiency; and vice versa. We first propose an optimal delay algorithm for anycast protocol. The simulation results show that our approach outperforms other heuristic schemes. Furthermore, we implement the proposed algorithm using TelosB sensors with TinyOS. Experiments demonstrate that the designed system can report a temperature anomaly within a small delay and achieve good long-term energy efficiency at the same time.

    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

  • Deep spectral‐spatial features of near infrared hyperspectral images for pixel‐wise classification of food products

    Hongyan Zhu, Aoife Gowen, Hailin Feng, Keping Yu, Jun Li Xu

    Sensors (Switzerland)   20 ( 18 ) 1 - 20  2020年09月

     概要を見る

    Hyperspectral imaging (HSI) emerges as a non‐destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel‐wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three‐dimensional convolution neural network (3‐D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2‐D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral‐spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre‐processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

    DOI PubMed

  • Timing Advance Estimation with Robustness to Frequency Offset in Satellite Mobile Communications

    Yukun Zhang, Li Zhen, Guangyue Lu, Keping Yu

    2020 IEEE/CIC International Conference on Communications in China, ICCC 2020     917 - 922  2020年08月

     概要を見る

    Timing advance (TA) estimation based on Zadoff-Chu (ZC) sequences is susceptible to carrier frequency offset (CFO), especially in high-dynamic satellite mobile communication scenarios with large Doppler shift. To solve this problem, a novel random access (RA) preamble sequence is first constructed by combining the real and imaginary part of a root ZC sequence, which entirely inherits the excellent correlation properties of the ZC sequence. With the aim of mitigating the adverse impact of large CFO, we further present a multi-peak joint detection algorithm that can obtain accurate TA value in once correlation operation without additional resource consumption and computational complexities. Numerical results consist with the mathematical analysis, and exhibit the robustness of the proposed method to large CFO in terms of error detection probability (EDP) and timing mean square error (MSE).

    DOI

  • Deep Learning-based Management for Wastewater Treatment Plants under Blockchain Environment

    Keyi Wan, Zhiwei Guo, Jianhui Wang, Wenru Zeng, Xu Gao, Yu Shen, Keping Yu

    2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020     106 - 110  2020年08月

     概要を見る

    Smart management for sewage treatment plants has always been a hot issue. It is generally implemented on the basis of a data scheduling platform, in which intelligent algorithms can be embedded. The most essential problem for such management is to predict daily business volumes, including amount and quality of wastewater. To achieve a comprehensive perspective, the generation of wastewater is viewed as collaborative effect of multiple factors in social system. This paper proposes a deep learning-based management for sewage treatment plants. Specially, it combines two classical neural network models to construct a hybrid model for precise prediction of business volumes. At last, a set of experiments are carried out to assess the proposed management mechanism. Results reveal that it performs better than general baselines.

    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

  • 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

  • Random Access Preamble Design and Detection for 5G Remote Health via Satellite Communications

    Teng Sun, Li Zhen, Guangyue Lu, Keping Yu

    2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings    2020年04月

     概要を見る

    This paper deals with the crucial issue of random access (RA) preamble design and detection for 5G remote health systems based on satellite communication. In consideration of the characteristics of satellite environment and system compatibility, a long preamble sequence is first constructed by cascading multiple different root Zadoff-Chu (ZC) sequences with large subcarrier interval in time domain. Then, we further present a multiple sequence joint correlation (MSJC) based timing detection scheme to estimate the value of timing advance (TA) in one step. By flexibly adjusting the number of ZC sequences involved in correlation operation, the proposed method not only is capable of effective mitigation of noise, but also can achieve the robustness to large carrier frequency offsets (CFOs). Simulation results consist with mathematical analysis, and demonstrate that the proposed method significantly improves performance in terms of timing mean square error (MSE), compared with the previous methods.

    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.

  • A blockchain-empowered AAA scheme in the large-scale HetNet

    Na Shi, Liang Tan, Wenjuan Li, Xin Qi, Keping Yu

    Digital Communications and Networks    2020年

    担当区分:責任著者

     概要を見る

    A Large-Scale Heterogeneous Network (LS-HetNet) integrates different networks into one uniform network system to provide seamless one-world network coverage. In LS-HetNet, various devices use different technologies to access heterogeneous networks and generate a large amount of data. For dealing with a large number of access requirements, these data are usually stored in the HetNet Domain Management Server (HDMS) of the current domain, and HDMS uses a centralized Authentication/Authorization/Auditing (AAA) scheme to protect the data. However, this centralized method easily causes the data to be modified or disclosed. To address this issue, we propose a blockchain-empowered AAA scheme for accessing data of LS-HetNet. Firstly, the account address of blockchain is used as the identity authentication, and the access control permission of data is redesigned and stored on the blockchain, then processes of AAA are redefined. Finally, the experimental model on Ethereum private chain is built, and the results show that the scheme is not only secure but also decentral, without tampering and trustworthiness.

    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

    担当区分:責任著者

     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

  • 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

  • Integrated deep neural networks-based complex system for urban water management

    Xu Gao, Wenru Zeng, Yu Shen, Zhiwei Guo, Jinhui Yang, Xuhong Cheng, Qiaozhi Hua, Keping Yu

    Complexity   2020   1 - 12  2020年

     概要を見る

    Although the management and planning of water resources are extremely significant to human development, the complexity of implementation is unimaginable. To achieve this, the high-precision water consumption prediction is actually the key component of urban water optimization management system. Water consumption is usually affected by many factors, such as weather, economy, and water prices. If these impact factors are directly combined to predict water consumption, the weight of each perspective on the water consumption will be ignored, which will be greatly detrimental to the prediction accuracy. Therefore, this paper proposes a deep neural network-based complex system for urban water management. The essence of it is to formulate a water consumption prediction model with the aid of principal component analysis (PCA) and the integrated deep neural network, which is abbreviated as UWM-Id. The PCA classifies the factors affecting water consumption in the original data into three categories according to their correlation and inputs them into the neural network model. The results in the previous step are assigned weights and integrated into the form of fully connected layer. Finally, analyzing the sensitivity of the proposed UWM-Id and comparing its performance with a series of commonly used baseline methods for data mining, a large number of experiments have proved that UWM-Id has good performance and can be used for urban water management system.

    DOI

  • Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems

    Qingfang Liu, Baosheng Kang, Keping Yu, Xin Qi, Jing Li, Shoujin Wang, Hong An Li

    IEEE Access   8   12644 - 12654  2020年

     概要を見る

    Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.

    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

  • Modular-based secret image sharing in Internet of Things: A global progressive-enabled approach

    Lina Zhang, Xiangqin Zheng, Keping Yu, Wenjuan Li, Tao Wang, Xuan Dang, Bo Yang

    Concurrency Computation    2020年

     概要を見る

    Due to the continuous development and progress of information technology, the Internet has also entered the era of big data based on the Internet of Things (IoT). How to protect the security of data stored and transmitted in the IoT is one of the urgent problems to be solved. This article focuses on the security issues of storage and transmission of image data in the IoT. Secret image sharing (SIS) is a kind of image protection mechanism by dividing an image into n shares, and different shares are given to different participants separately for preservation. Only when the number of shares reaches the threshold can the original image be recovered. From the perspective of image reconstruction mode, there are two types of SIS schemes: one is the traditional (k, n) threshold scheme, which provides an all-or-nothing reconstruction mode, the other is the progressive scheme, which can gradually restore the original image. In this article, a novel (k, k2) progressive secret image sharing based on modular operations is proposed, this method can divide the important images stored in the IoT into many parts and then transmit them to people in different places. It takes the whole as a unit in terms of the progressive recovery form. When the share reaches the threshold, certain blocks of the original image can be seen. As the share increases, the image will be clearer. When all shares participate in the reconstruction together, the original image can be restored without loss. Compared with other schemes, our scheme has the same smoothness, shadow size and satisfies the security, and is fine-grained progressive.

    DOI

  • RVFL-LQP: RVFL-based link quality prediction of wireless sensor networks in smart grid

    Xue Xue, Wei Sun, Jianping Wang, Qiyue Li, Guojun Luo, Keping Yu

    IEEE Access   8   7829 - 7841  2020年

     概要を見る

    In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the time-varying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively.

    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

  • AuthPrivacyChain: A Blockchain-Based Access Control Framework with Privacy Protection in Cloud

    Caixia Yang, Liang Tan, Na Shi, Bolei Xu, Yang Cao, Keping Yu

    IEEE Access   8   70604 - 70615  2020年

     概要を見る

    Cloud is a computing model that provides sharing and supports ubiquitous on-demand access computing, providing new data processing and services for many industries, significantly reducing user computing and storage costs, and improving ease of use. With the development of cloud-scale and intensification, cloud security has become an essential issue in the field of cloud computing. Access control is one of the critical security technologies for protecting sensitive data stored in the cloud by enterprises and individuals. Since the centralized access control mechanism is adopted in the cloud, the sensitive data in the cloud are easy to be tampered with or leaked by hackers or cloud internal managers. To address this issue, we propose a blockchain-based access control framework with privacy protection called AuthPrivacyChain. Firstly, we use the account address of the node in blockchain as the identity, and at the same time, redefine the access control permission of data for the cloud, which is encrypted and stored in blockchain. After that, we design processes of access control, authorization, and authorization revocation in AuthPrivacyChain. Finally, we implement AuthPrivacyChain based on enterprise operation system (EOS), and the results show that AuthPrivacyChain can not only prevent hackers and administrators from illegally accessing resources, but also protect authorized privacy.

    DOI

  • R3MR: Region Growing Based 3D Mesh Reconstruction for Big Data Platform

    Hong An Li, Min Zhang, Keping Yu, Xin Qi, Qiaozhi Hua, Yu Zhu

    IEEE Access   8   91740 - 91750  2020年

     概要を見る

    Visualization is one of the most intuitive and perceptible ways for information representation in the big data era. As an essential part of the visualization, 3D mesh reconstruction is facing great challenges due to its characteristics of quantity, non-structure, and low-accuracy. The traditional 3D mesh reconstruction method has strict theoretical proof and can be used to reconstruct the surface of the complex topological structure for computer rendering and display. However, it is not suitable to handle a large number of point cloud and noise point cloud in a big data platform because the process is inefficient, low-automation and requires massive calculations. To address this issue, we propose a region growing based 3D mesh reconstruction (R3MR) in the big data platform. Firstly, we divide the large data points into three categories: flat point set, high curvature point set, and boundary point set. The errors of topological structure for 3D meshes usually occur in the place with large curvatures and noise points, so the division of high curvature point set is beneficial to solve the low-accuracy problem in 3D mesh reconstruction. Moreover, the flat points can be treated as one kind of point to avoid repetitive calculations because their features are basically the same. Hence, the division of the flat point set is beneficial to solve the problem of quantity and massive calculations. Secondly, our proposal is to start the mesh reconstruction from the flat point set progressively, because it can obtain the outline of the 3D model. In many scenarios, such as autonomous driving, only the overall outline of the model is required. Finally, during the 3D mesh reconstruction, the inner edge adjacency list and optimal selection principle are set to improve the robustness of the whole system. Simulation experiments show that the proposed 3D mesh reconstruction can naturally reflect the detailed features of objects in the big data platform, especially effective for the scattered point cloud.

    DOI

  • Preamble Design and Detection for 5G Enabled Satellite Random Access

    Li Zhen, Teng Sun, Guangyue Lu, Keping Yu, Rui Ding

    IEEE Access   8   49873 - 49884  2020年

     概要を見る

    This paper deals with the crucial issue of random access (RA) preamble design and detection for fifth generation new radio (5G NR) enabled satellite communication systems. In consideration of the characteristics of satellite environment and system compatibility, a long preamble sequence is first constructed by cascading multiple different root Zadoff-Chu (ZC) sequences with large sub-carrier interval in time domain. Then, we further present a multiple sequence joint correlation (MSJC) based one-step timing detection scheme to effectively estimate the value of timing advance (TA), which is capable of flexibly adjusting the number of available ZC sequences involved in correlation operation. The superiority of the proposed method is mathematically validated in terms of robustness to carrier frequency offset (CFO), mitigation of noise, as well as computational complexity. Numerical results in a typical low-earth-orbit (LEO) based non-terrestrial network (NTN) scenario demonstrate that the proposed method, without the pre-compensation of timing and frequency offset, can achieve a remarkable timing performance improvement in comparison with the existing methods.

    DOI

  • A Heterogeneous Image Fusion Method Based on DCT and Anisotropic Diffusion for UAVs in Future 5G IoT Scenarios

    Shuai Hao, Beiyi An, Hu Wen, Xu Ma, Keping Yu

    Wireless Communications and Mobile Computing   2020   1 - 11  2020年

     概要を見る

    Unmanned aerial vehicles, with their inherent fine attributes, such as flexibility, mobility, and autonomy, play an increasingly important role in the Internet of Things (IoT). Airborne infrared and visible image fusion, which constitutes an important data basis for the perception layer of IoT, has been widely used in various fields such as electric power inspection, military reconnaissance, emergency rescue, and traffic management. However, traditional infrared and visible image fusion methods suffer from weak detail resolution. In order to better preserve useful information from source images and produce a more informative image for human observation or unmanned aerial vehicle vision tasks, a novel fusion method based on discrete cosine transform (DCT) and anisotropic diffusion is proposed. First, the infrared and visible images are denoised by using DCT. Second, anisotropic diffusion is applied to the denoised infrared and visible images to obtain the detail and base layers. Third, the base layers are fused by using weighted averaging, and the detail layers are fused by using the Karhunen-Loeve transform, respectively. Finally, the fused image is reconstructed through the linear superposition of the base layer and detail layer. Compared with six other typical fusion methods, the proposed approach shows better fusion performance in both objective and subjective evaluations.

    DOI

  • Deep-Learning-Empowered Digital Forensics for Edge Consumer Electronics in 5G HetNets

    Feng Ding, Guopu Zhu, Mamoun Alazab, Xiangjun Li, Keping Yu

    IEEE Consumer Electronics Magazine     1 - 1  2020年

    担当区分:責任著者

     概要を見る

    The upcoming 5G heterogeneous networks (HetNets) have attracted much attention worldwide. Large amounts of high velocity data can be transported by using the bandwidth spectrum of HetNets, yielding both great benefits and several concerning issues. In particular, great harm to our community could occur if the main visual information channels, such as images and videos, are maliciously attacked and uploaded to the internet, where they can be spread quickly. Therefore, we propose a novel framework as a digital forensics tool to protect end users. It is built based on deep learning and can realize the detection of attacks via classification. Compared with the conventional methods and justified by our experiments, the data collection efficiency, robustness, and detection performance of the proposed model are all refined. In addition, assisted by 5G HetNets, our proposed framework makes it possible to provide high-quality real-time forensics services on edge consumer devices (ECE) such as cell phones and laptops, which brings colossal practical value. Some discussions are also carried out to outline potential future threats.

    DOI

  • Exploring Uplink Achievable Rate for HPO MIMO through Quasi-Monte Carlo and Variance Reduction Techniques

    Yi Gong, Lin Zhang, Keping Yu, Renping Liu

    IEEE Access   8   75874 - 75883  2020年

     概要を見る

    The power consumption at the receiver side will be dramatically increased in the millimetre-wave and massive multiple-input-multiple-output (MIMO) communication systems due to the wide bandwidth and a large number of antennas adopted. A half phase-only MIMO (HPO MIMO) scheme, in which the base station (BS) acquires \pi -periodic phase measurements of the complex envelop signals was proposed very recently to overcome the above problem. Due to the non-linear nature of HPO MIMO, the valuation of the achievable rate is very challenging. The purpose of the paper is to provide an efficient method for calculating the achievable rate of the HPO MIMO system. By the mutual information theory, we transform the achievable rate into a sum of two high-dimensional integrations. However, calculating those integrations suffers from the enormous computational burden when using the traditional Monte-Carlo method. In order to increase efficiency, a new method by combining quasi-Monte Carlo with a variance reduction technique is proposed. Besides, we derive the probability density function (PDF) of the HPO MIMO system and analyze the uplink achievable rate of the HPO MIMO scheme. Numerical results show that our proposed method is efficient for calculating the achievable rate of the HPO MIMO system. With the proposed method we confirm that HPO MIMO is a promising technology in future low-power communication scenarios.

    DOI

  • A Blockchain-Based Access Control Framework for Cyber-Physical-Social System Big Data

    Liang Tan, Na Shi, Caixia Yang, Keping Yu

    IEEE Access   8   77215 - 77226  2020年

     概要を見る

    Cyber-Physical-Social System (CPSS) big data is specified as the global historical data which is usually stored in cloud, the local real-time data which is usually stored in the fog-edge server (FeS) of the mobile terminal devices or sensors, and the social data which is usually stored in the social data server (SdS), moreover adopts a centralized access control mechanism to offer users' access strategy which can easily cause CPSS big data to be tampered with and to be leaked. Therefore, a blockchain-based access control scheme called BacCPSS for CPSS big data is proposed. In BacCPSS, account address of the node in blockchain is used as the identity to access CPSS big data, the access control permission for CPSS big data is redefined and stored in blockchain, and processes of authorization, authorization revocation, access control and audit in BacCPSS are designed, and then a lightweight symmetric encryption algorithm is used to achieve privacy-preserving. Finally, a credible experimental model on EOS and Aliyun cloud is built. Results show that BacCPSS is feasible and effective, and can achieve secure access in CPSS while protecting privacy.

    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

  • Detour Path Angular Information based Range Free Localization with Last Hop RSSI Measurement based Distance Calculation

    Anup Kumar Paul, Mohammad Arifuzzaman, Keping Yu, Takuro Sato

    2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019    2019年11月

     概要を見る

    The location estimation accuracy of range-free localization (RFL) is a crucial issue in Wireless Sensor Networks (WSNs). The accuracy has significant impact on localization dependent routing protocols and applications. The assumption that the sensor nodes are deployed in regular areas without any obstacles do not match the practical deployment scenarios, especially for scenarios like outdoor deployment of WSNs. In this paper, we propose a hybrid solution by combining a RFL method and range-based localization (RBL) method namely Received Signal Strength Indication (RSSI) to tackle the detoured path between sensors in anisotropic network and to combat the last hop distance calculation problem respectively. As a result, our hybrid approach significantly improves the localization accuracy in anisotropic network as compared to range free method only. We calculate the average hop distance (AHD) of detoured path by estimating the angle of the middle of the transmission path between every two anchor pairs one by one. The AHD is finally adjusted by estimating the RSSI based last hop distance measurement. Based on the simulation results, it is observed that our hybrid approach with few anchor nodes outperforms other RFL algorithms in anisotropic network and indicates an improvement in the localization accuracy.

    DOI

  • Vibration and Noise Emitted by Dry-type Air-core Reactors Under Sine-wave Current Excitation

    Jingsong Li, Baojun Qu, Keping Yu

    2019 22nd International Conference on Electrical Machines and Systems, ICEMS 2019    2019年08月

     概要を見る

    According to magneto-mechanical strong coupled effect, structure mode is discussed by engaging finite element (FE) method and both natural frequency and modal shape for a dry-type air-core reactor (DAR) are obtained in this paper. On the basis of harmonic response analysis, electromagnetic forces under sine-wave current excitation are mapped with the structure mesh, the vibration spectrums are gained and the consequences represent that the whole structure vibration predominates in the axial direction, with less radial vibration. Referring to the test standard of reactor noise, the rules of emitted noise of the DAR are measured and comparatively analyzed at power frequency and chosen other frequencies match the sample resonant frequency and the methods of active vibration and noise reduction are put forward. Finally, the low acoustic noise emission of a prototype DAR is verified by simulation and measurement.

    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  [査読有り]

     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  [査読有り]

     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

  • 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  [査読有り]

     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

  • Combined forecasting model of cloud computing resource load for energy-efficient IoT System

    Hong An Li, Min Zhang, Keping Yu, Jing Zhang, Qiaozhi Hua, Bo Wu, Zhenhua Yu

    IEEE Access   7   149542 - 149553  2019年  [査読有り]

     概要を見る

    Cloud computing is generally considered as a special energy-efficient form for the Internet of Things (IoT) resource usage. Dedicated server systems for cloud services, better capacity utilization and economies of scale because of the use of larger and more energy-efficient data centers are the reasons why cloud solutions typically use less energy than traditional on-premise systems. To scientifically and rationally configure the hardware and software resources of the cloud computing, the research on forecasting a cloud computing resource load becomes a research focus. However, the widely-used single forecasting model cannot contain all the characteristics of the cloud computing resource load sequence, resulting in inaccurate forecasting results. In this paper, a combined forecasting approach of cloud computing resource load based on wavelet decomposition is proposed, which combined the grey model and cubic exponential smoothing model. It can well preserve details and reduce noise. Firstly, the cloud computing resource load sequence is decomposed into several frequencies by the wavelet decomposition method. The decomposed load sequences with different characteristics are divided into different resolution scale subspaces in deferent frequencies. The noise of the load sequences is reduced by the wavelet threshold denoising method. And then, the load sequences are reconstructed according to the wavelet coefficients. The reconstructed load sequence not only contains less noise but also reserves detailed information. Consequently, it is closer to the real data and more regular. Experimental results show that our proposed combined forecasting model with wavelet decomposition can provide more accurate forecasting results than each single forecasting model or the combined forecasting model without using the wavelet decomposition method. Thus, our proposal is demonstrated to be efficient for forecasting the cloud computing resource load and helping to reduce energy consumption.

    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  [査読有り]

     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

  • Modeling and Analysis of Error Process in 5G Wireless Communication Using Two-State Markov Chain

    San Hlaing Myint, Keping Yu, Takuro Sato

    IEEE Access   7   26391 - 26401  2019年  [査読有り]

     概要を見る

    In fifth-generation wireless communications, data transmission is challenging due to the occurrence of burst errors and packet losses that are caused by multipath fading in multipath transmissions. To acquire more efficient and reliable data transmissions and to mitigate the transmission medium degradation in the 5G networks, it is important to study the error patterns or burst the error sequences that can provide insights into the behavior of 5G wireless data transmissions. In this paper, a two-state Markov-based 5G error model is investigated and developed to model the statistical characteristics of the underlying error process in the 5G network. The underlying 5G error process was obtained from our 5G wireless simulation, which was implemented based on three different kinds of modulation methods, including QPSK, 16QAM, and 64QAM, and was employed using the LDPC and TURBO coding methods. By comparing the burst or gap error statistics of the reference error sequences from the 5G wireless simulations and those of the generated error sequences from the two-state Markov error model, we show that the error behaviors of the coded OFDM 5G simulations can be adequately modeled by using the two-state Markov error model. Our proposed two-state Markov-based wireless error model can help to provide a more thorough understanding of the error process in 5G wireless communications and to evaluate the error control strategies with less computational complexity and shorter simulation times.

    DOI

  • Information-Centric Networking: Research and Standardization Status

    Keping Yu, Suyong Eum, Toshihiko Kurita, Qiaozhi Hua, Takuro Sato, Hidenori Nakazato, Tohru Asami, Ved P. Kafle

    IEEE Access   7 ( 8821346 ) 126164 - 126176  2019年  [査読有り]

     概要を見る

    Information-centric networking (ICN) is a new approach to networking contents rather than devices that hold the contents. It has recently attracted much attention of network research and standardization communities. National and multi-national funded research projects have progressed worldwide. International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) started ICN standardization activities in 2012. In parallel, the standards-oriented research cooperation is progressing in the Information-Centric Networking Research Group (ICNRG) of the Internet Research Task Force (IRTF). All these global efforts have been collectively advancing the novel network architecture of ICN. However, there are very few surveys and discussions on the detailed ICN standardization status. To update the reader with information about the ICN research and standardization related activities, this paper starts with the history of global activities on ICN from 2010, giving references to various projects. It then describes the recent progress in the standardization of ICN component technologies in ITU-T and various documents produced by ICNRG. Lastly, it discusses the future directions for progressing ICN.

    DOI

  • An intelligent content prefix classification approach for quality of service optimization in information-centric networking

    Cutifa Safitri, Yoshihide Yamada, Sabariah Baharun, Shidrokh Goudarzi, Quang Ngoc Nguyen, Keping Yu, Takuro Sato

    Future Internet   10 ( 4 )  2018年04月

     概要を見る

    This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

    DOI

  • A Context-Aware Green Information-Centric Networking Model for Future Wireless Communications

    Quang Ngoc Nguyen, Mohammad Arifuzzaman, Keping Yu, Takuro Sato

    IEEE Access   6   22804 - 22816  2018年04月  [査読有り]

     概要を見る

    This research proposes a novel wireless information-centric networking (ICN) architecture, namely, Context-Aware Green ICN Model (CAGIM), which can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The power adaption in ICN nodes is conducted through dynamically adjusting the link-rate corresponding to content popularity and traffic load to reduce wasteful energy consumption. Moreover, we propose a smart popularity-based caching strategy, called distinguished caching scheme (DCS), with the introduction of hot and cold-caching partitions of ICN node's cache storage for popular and non-popular content objects, respectively. DCS improves the content diversity of the cache storage by adjusting, for each content, the number of chunks to be cached at ICN nodes based on its type and popularity level. DCS thus can further decrease the network system power consumption, thanks to its improved cache hit that reduces network traffic load. Toward the goal of realizing a context-aware green wireless network system with efficient content delivery, we also design a Wi-Fi Direct based scheme as an alternative approach to minimize power consumption and latency by sharing essential/important content objects via direct communications with power-saving mechanisms in the case that wireless local area network connections are not available. The evaluation results show that CAGIM can improve network efficiency by reducing both hop-count and power consumption considerably compared with existing wireless network systems with different well-known caching schemes. This proposal enables a flexible and efficient content delivery mechanism for future networks with various real-life scenarios, like Green building, Green company, and Green campus content accesses.

    DOI

  • Toward standardization activities for future networks in ITU-T: A viewpoint from Y. Suppl.35: ITU-T Y.3033 data-aware networking-scenarios and use cases

    Keping Yu, Zhenyu Zhou, Mohammad Arifuzzaman, Anup Kumar Paul, Davaasambuu Battulga, Quang N. Nguyen, Takuro Sato

    2017 3rd IEEE International Conference on Computer and Communications, ICCC 2017   2018-January   412 - 417  2018年03月

     概要を見る

    Information-centric networking (ICN) approach, based on named contents instead of Internet Protocol (IP) addresses, has been emerging as a promising technology for the future Internet. It brings in innovative naming mechanisms, novel routing strategies, security schemes, and augmented with caching at intermediate nodes to improve network efficiency and enhance security. Therefore, it has attracted much attention to research communities, and its related standardization activities have been progressed. In this paper, we summarize the standardization activities on data aware networking (DAN) which corresponds to ICN in ITU-T. Thereinto, our contributions in recommendation Y. Suppl. 35 (ITU-T Y.3033-Data aware networking: Scenarios and use cases) has been highlighted.

    DOI

  • Utilization efficient game-theoretical handover scheme for Macro-Femtocell networks

    Qiaozhi Hua, Yuwei Su, Takuro Sato, Keping Yu

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2017-December   57 - 62  2018年02月

     概要を見る

    The Macro-Femtocell system is widely used in current society because the cooperation between Macrocell base stations and Femtocell base stations can improve the communication capacity of certain areas and adapt to the large multi-user demand. To increase the system's communication quality, the handover scheme should be utilized when the user is moving within the limited coverage of Femtocell base stations. Generally, the Markov decision strategy is used for mobile users in the process of handover. However, the Markov decision process can't ensure the maximum utilization balance of Macrocell base stations and Femtocell base stations. In this case, users often choose the base station offering the highest signal strength, which will cause a Macrocell base station's load to be too large and Femtocell base stations to be idle, so the system's utilization is very low. To address this problem, we propose the utilization efficient game-theoretical handover scheme based on Starckberg competition theory for appropriately increasing a Femtocell base station's transmitting power. This can ensure the communication quality of the system and guarantees the system's load balance to induce users to take the initiative in processing the handover scheme. Finally, the simulation results show that our strategy is effective in choosing the best base station to receive the highest signal strength by predicting the user's movement. It could also realize the load balance of the antenna in the system. The overall transmission quality of the user group is optimized.

    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 x(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.

    DOI

  • A game-Theoretical green networking approach for information-centric networks

    Quang N. Nguyen, Keping Yu, Takuro Sato, Mohammad Arifuzzaman

    2017 IEEE Conference on Standards for Communications and Networking, CSCN 2017     132 - 137  2017年10月  [査読有り]

     概要を見る

    In order to address the energy issue and enhance the feasibility of Information-Centric Networking (ICN) in the case of access networks, this research proposes a novel Green ICN design which can adapt power consumption of network devices to their optimized real-Time link-utilizations based on content popularity levels. We utilize dynamic ALR (Adaptive Link Rate) based scheme for content nodes to provide efficient content delivery as a realistic approach for the economically viable green network We also develop a game-Theoretical model to study the interaction between an ISP and a network equipment company in the context of green networking. Specifically, we present the system concept and some demonstration results of game-based Green ICN model to analyze the economic incentives of players. Moreover, we discuss ICN deployment and standardization challenges, then show that the proposal is robust, easy to deploy and practically relevant for the network players.

    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

    © 2017 The Institute of Electronics, Information and Communication Engineers. 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

  • 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

  • Standardization Activities for Future Networks in ITU-T: A Case Study from Y.3071: Data Aware Networking (Information Centric Networking) - Requirements and Capabilities

    PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC)     418 - 423  2017年  [査読有り]

  • 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  [査読有り]

     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

  • Cost-efficient residential energy management scheme for information-centric networking based home network in smart grid

    Keping Yu, Battulga Davaasambuu, Nam Hoai Nguyenand, Quang Nguyen, Arifuzzaman Mohammad, Takuro Sato

    International Journal of Computer Networks and Communications   8 ( 2 ) 25 - 42  2016年

     概要を見る

    Home network (HOMENET) performs multiple important functions such as energy management, multimedia sharing, lighting and climate control in smart grid (SG). In HOMENET there are numerous challenges among which mobility and security are the basic requirements that need to be addressed with priority. The information-centric networking (ICN) is regarded as the future Internet that subscribes data in a content-centric manner irrespective of its location. Furthermore, it has pecial merit in mobility and security since ICN supports in-network caching and self-contained security, these make ICN a potential solution for home communication fabric. This paper aims to apply the ICN approach on HOMENET system, which we called ICN-HOMENET. Then, a proof-of-concept evaluation is employed to evaluate the effectiveness of the proposed ICN-HOMENET approach in data security, device mobility and efficient content distribution for developing HOMENET system in SG. In addition, we proposed a cost-efficient residential energy management (REM) scheme called ICN-REM scheme for ICN-HOMENET system which encourages consumers to shift the start time of appliances from peak hours to off-peak hours to reduce the energy bills. To the best of our knowledge, this is the first attempt to propose an ICN-based REM scheme for HOMENET system. In this proposal, we not only consider the conflicting requests from appliances and domestic power generation, but also think the energy management unit (EMU) should cooperate with measurement sensors to control some specific appliances in some specific conditions. Moreover, the corresponding performance evaluation validates its correctness and effectiveness.

    DOI

  • Locating the content in the locality: ICN caching and routing strategy revisited

    M. Arifuzzaman, Yu Keping, Quang N. Nguyen, Sato Takuro

    2015 European Conference on Networks and Communications, EuCNC 2015     423 - 428  2015年08月

     概要を見る

    In Information Centric Networking (ICN), besides Off-path caching, On-path caching is an integrated caching solution with-in an ISP's local network. However, in On-path caching, content is cached en-route in the backward path towards the Interest generator and local RENE1/NRS2 (in routing through name resolution) or FIB3 (in name-based routing) is not aware of the cached data. Hence, the most widely used intra-domain routing protocol and their forwarding strategy cannot address all available temporary cached copies of content. Thus, on-path caching strategy usually suffers from either of the two drawbacks- 1) too many replications within AS (Autonomous system) which do not add substantial value in terms of cache resource utilization. 2) An attempt to minimize redundancy ends up with the cost of fetching more copy of the same content from the repository (i.e., the closest cache copy is unknown/ not en-route). We propose an integrated routing and caching solution which reduces the caching redundancy and maximize the probability of finding nearby cached content, resulting in efficient utilization of available cache resources of ISP's as a whole. We introduce novel concepts of naming the content router and caching the content's cache-route to address the issue. We leverage NDN node architecture and the forwarding plane/strategy (of routing) as well to achieve our goal.

    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

     概要を見る

    Copyright © 2015 The Institute of Electronics, Information and Communication Engineers. 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

  • 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

    Proceedings - 2015 IEEE 12th International Symposium on Autonomous Decentralized Systems, ISADS 2015     266 - 271  2015年04月

     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

  • Self-optimization of handover parameters for long-term evolution with dual wireless mobile relay nodes

    Battulga Davaasambuu, Keping Yu, Takuro Sato

    Future Internet   7 ( 2 ) 196 - 213  2015年

     概要を見る

    In recent years, train passengers have been transferring increasing amounts of data using mobile devices. Wireless networks with mobile relay nodes support broadband wireless communications for passengers of such vehicles using backhaul links. However, the mobility management entity reuses the handover of existing user equipment, resulting in the handover of the Long-Term Evolution network being unsuitable for user equipment within the cabins of vehicles traveling at high speed. In this paper, we propose a self-optimizing handover hysteresis scheme with dual mobile relay nodes for wireless networks in high-speed mobile environments. The proposed mechanism tunes the hysteresis and cell individual offset handover parameters based on the velocity of the vehicle and the handover performance indicator, which affects the handover triggering decision and performance. The results of simulations conducted in which the performance of the proposed scheme was compared to that of an existing scheme show that the proposed scheme can reduce the number of radio link failures and service interruptions during handover procedures.

    DOI

  • 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

    POWERCON 2014 - 2014 International Conference on Power System Technology: Towards Green, Efficient and Smart Power System, Proceedings     2019 - 2024  2014年12月

     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

  • 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

  • Content distribution in information centric network: Economic incentive analysis in game theoretic approach

    Mohammad Arifuzzaman, Keping Yu, Takuro Sato

    Proceedings of the 2014 ITU Kaleidoscope Academic Conference: Living in a Converged World - Impossible Without Standards?, K 2014     215 - 220  2014年

     概要を見る

    The contributions of this paper are twofold. Firstly, we analyze the decision making problem of caching contents by the network players of Information Centric Networking (ICN) from a game theoretic perspective. We also mention a possible content distribution model for ICN. By our proposed game theory, different network players can find the optimality taking into consideration which benefits them with optimum revenue. Secondly, we present a solution for Live Streaming Media broadcast in ICN and analyze the economic part with a decision tree. We believe, our paper clearly shows the economic incentives for major network players which can stand as a motivation to achieve the faster accommodation of ICN architecture. Besides, we identify some standardization issue in ICN architecture and we emphasize on the need for a common standard for content routers (CR) so that as a node in the ICN, CR ensure scalable content delivery as well as its functionalities match with the Internet open standard philosophy. © 2014 ITU.

    DOI

▼全件表示

書籍等出版物

  • Handbook of Microplastics in the Environment

    Jun-Li Xu, Martin Hassellöv, Keping Yu, Aoife A. Gowen( 担当: 共著,  担当範囲: Microplastic Characterization by Infrared Spectroscopy)

    Springer  2020年07月

  • Intelligent Cyber-Physical Systems for Autonomous Transportation

    Z. Guo, K. Yu

    Springer 

Misc

  • Report on 5G hardware trial equipment and evaluation of 5G propagation characteristics

    HUA Qiaozhi, TAZAWA Ryoichiro, SAN Hlaing Myint, YU Keping, WEN Zheng, YAN Chengkai, QUANG Ngoc Nguyen, TOKUDA Kiyohito, SATO Takuro

    電子情報通信学会大会講演論文集(CD-ROM)   2019   ROMBUNNO.B‐5‐70  2019年03月

    J-GLOBAL

  • 5Gハードウェア試験装置の報告と評価5G伝搬特性

    HUA Qiaozhi, TAZAWA Ryoichiro, SAN Hlaing Myint, ZHENG Wen, NGUYEN Quang Ngoc, TOKUDA Kiyohito, YU Keping, SATO Takuro

    電子情報通信学会技術研究報告   118 ( 474(RCS2018 282-338)(Web) ) 129‐132 (WEB ONLY)  2019年02月

    J-GLOBAL

  • Standardization activities for future networks in ITU-T: A case study from Y.3071 : Data aware networking (Information Centric Networking) - Requirements and Capabilities

    Keping Yu, Qiaozhi Hua, Quang N. Nguyen, Rungrot Sukjaimuk, Cutifa Safitri, Takuro Sato

    2017 3rd IEEE International Conference on Computer and Communications, ICCC 2017   2018-January   418 - 423  2018年03月

     概要を見る

    Information-centric networking (ICN) is a promising architecture for the future Internet that disseminates content based on named data instead of named hosts. It brings in novel naming mechanisms, innovative security schemes, and routing strategies as well as in-network caching at intermediate nodes, has led researchers into considering a radical change to the Internet architecture and its related standardization activities have been progressed. There into, International Telecommunication Union Telecommunication Standardization Sector, ITU-T, has begun the ICN standardization from 2012 to promote the development of future networks. In this paper, we summarize the standardization activities on Data-Aware Networking (DAN) which corresponds to ICN in ITU-T. Besides, we provide some guidelines for a case study on ITU-T recommendation Y.3071: Data-Aware Networking (Information Centric Networking) - Requirements and Capabilities, by highlighting our contributions during all the process for this standard.

    DOI

  • A Markov‐Based Handover Scheme for Macro‐Femtocell Networks

    HUA Qiaozhi, SU Yuwei, YU Keping, SATO Takuro

    International Conference on Simulation Technology (CD-ROM)   2018(Web)   197‐202 (WEB ONLY)  2018年

    J-GLOBAL

  • The design and implementation of Named Node Network

    QI Xin, WEN Zheng, LOPEZ Jairo, DU Yingshuang, NOZAKI Daichi, OKAMOTO Koki, MOCHIDA Toru, YU Keping, SATO Takuro

    電子情報通信学会大会講演論文集(CD-ROM)   2017   ROMBUNNO.BT‐1‐4  2017年08月

    J-GLOBAL

  • Introduction of Standards Activities for Information‐Centric Networking

    SATO Takuro, YU Keping

    電子情報通信学会大会講演論文集(CD-ROM)   2017   ROMBUNNO.BT‐2‐3  2017年03月

    J-GLOBAL

  • A Novel ICN & Drone Based Emergency Information System for Disaster Area

    WEN Zheng, ZHANG Di, QI Xin, YU Keping, SATO Takuro

    電子情報通信学会技術研究報告   116 ( 421(CAS2016 77-114) ) 47‐52  2017年01月

    J-GLOBAL

  • Energy Efficient Policy for Cloud Radio Access Network

    Di Zhang, Zhenyu Zhou, Keping Yu, Takuro Sato

       2015年04月

    機関テクニカルレポート,技術報告書,プレプリント等  

     概要を見る

    Energy Efficiency (EE) is a big issue in 5th Generation Wireless<br />
    Communications (5G) on condition that the number of access User Equipments<br />
    (UEs) are exploding and more antennas should be equipped in one Base Station<br />
    (BS). In EE studies, prior literatures focus on the energy consumption of<br />
    single separated BS coverage area or through scheduling mechanism or network<br />
    coding method. But some other elements are ignored in those literatures, such<br />
    as the energy consumption of machine room, circuit, etc. In this paper, to be<br />
    more closer to the reality, based on the Cloud Radio Access Network (C-RAN), we<br />
    modify its traditional structure for easier layout of sleeping mechanism in the<br />
    real world, study the EE issue within a comprehensive view while taking more<br />
    elements into consideration. We modified the traditional C-RAN structure with<br />
    the purpose of much easily adopting the sleeping mechanism with on-off<br />
    selection method. Afterwards, the EE issue is modeled into a mathematical<br />
    optimizing problem and its solution is given by a tractable method. The<br />
    analysis of sum capacity in one cluster of this modified structure is addressed<br />
    first. Then based on the analysis, the EE issue is studied with a comprehensive<br />
    view while taking more elements into consideration. In the next step, we<br />
    convert it into an optimization problem and give its solution with the sleeping<br />
    techniques. Comparing with prior works, this proposal is of better performance<br />
    for the merit of comprehensive vision and easier layout characteristic.

  • Towards SE and EE in 5G with NOMA and Massive MIMO Technologies

    ZHANG Di, YU Keping, WEN Zheng, SU Yuwei, SATO Takuro

    電子情報通信学会大会講演論文集(CD-ROM)   2015   ROMBUNNO.BS-3-46  2015年02月

    J-GLOBAL

  • Performance of ICN for large‐scale data in Smart Grid

    KOVI Aduayom Ahego, YU Keping, ZHANG Di, SATO Takuro

    電子情報通信学会大会講演論文集(CD-ROM)   2015   ROMBUNNO.B-6-119  2015年02月

    J-GLOBAL

  • 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

  • B-6-119 Performance of ICN for large-scale data in Smart Grid

    Kovi Aduayom Ahego, Yu Keping, Zhang Di, Sato Takuro

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

     概要を見る

    The information exchange in the power grid has been undergoing through crucial changes. With the application of advanced Information and Communication Technologies (ICT) to the existing power network; the future grid sees incredible enhancements. However the huge amount of data to transfer over the network becomes challenging. Many literatures show the benefits of applying Information-Centric Network to the power system. In this paper we propose an application of 1CN to Smart Grid system. We evaluate the robustness of the ICN over IP.

    CiNii

  • 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

    機関テクニカルレポート,技術報告書,プレプリント等  

     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

  • Economic Incentives for major network players in Information Centric Networking: A Game Theoretic Analysis

    ARIFUZZAMAN M, KEPING Yu, SATO Takuro

    電子情報通信学会技術研究報告   113 ( 456(RCS2013 306-396) ) 533 - 537  2014年02月

    J-GLOBAL

  • CCN‐SG: Performance Evaluation of Content‐Centric Networking Approach for Smart Grid

    YU Keping, ZHU Li, WEN Zheng, MOHAMMAD Arifuzzaman, ZHOU Zhenyu, SATO Takuro

    International Conference on Simulation Technology (CD-ROM)   2014   52 - 53  2014年

    J-GLOBAL

  • 災害システムのCCN応用に関する研究 (ネットワークシステム)

    余 娜, Mohammad Arifuzzaman, 余 恪平, 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 ( 360 ) 1 - 6  2013年12月

     概要を見る

    災害時には災害情報の発信と受信のために,膨大なアクセスが短時間にネットワークに対して行われる.それら通信サービスに対応するための通信処理能力が大幅に不足することから,通信途絶等の状態が生じたことを踏まえ,通信ネットワークの耐災害性強化に向けるネットワークを統合的に構成することが必要である.本研究では,ネットワーク全体のトラヒックを削減するには,コンテンツ中心ネットワークを用い,ルータ上でキャッシュを行う方法を検討する.さらに,キャッシュヒット率の向上を図るために,パスキャパシティとコンテンツ人気度を考慮するキャッシュを適切に多重化するキャッシュデシジョンポリシの提案とその評価を行う.

    CiNii

  • 災害システムのCCN応用に関する研究

    YU Na, MOHAMMAD Arifuzzaman, YU Keping, 佐藤拓朗

    電子情報通信学会技術研究報告   113 ( 360(NS2013 134-162) ) 1 - 6  2013年12月

    J-GLOBAL

  • Cloud‐based Modified Residential Energy Management Algorithm in Smart Grid Network

    YU Keping, ZHOU Zhenyu, SATO Takuro

    International Conference on Simulation Technology (CD-ROM)   2013   ROMBUNNO.OS4,PAPER16  2013年

    J-GLOBAL

  • Performance Evaluation of Residential Energy Management algorithm in Smart Grid network

    YU Keping, ZHOU Zhengyu, PAUL Anup Kumar, SATO Takuro

    電子情報通信学会大会講演論文集   2011   S.110-S.111  2011年08月

    J-GLOBAL

  • BS-6-41 Performance Evaluation of Residential Energy Management algorithm in Smart Grid network(BS-6. Planning, Control and Management on Networks and Services)

    Yu Keping, Zhou Zhengyu, Paul Anup Kumar, SATO Takuro

    電子情報通信学会ソサイエティ大会講演論文集   2011 ( 2 ) "S - 110"-"S-111"  2011年08月

    CiNii

▼全件表示

その他

  • 国際標準の勧告化

    2017年02月
     
     

     概要を見る

    ITU-T Standard, Series Y, “ITU-T Y.3071: Data Aware Networking (Information Centric Networking) – Requirements and Capabilities”, February 2017

  • 国際標準の勧告化

    2016年05月
     
     

     概要を見る

    ITU-T Standard, Series Y, Supplement 35, “Y.3033-Data aware networking-Scenarios and use cases”, May 2016.

受賞

  • Best Paper Award

    2020年12月   ITU KALEIDOSCOPE 2020  

  • Young Author Recognition

    2020年   ITU Kaleidoscope  

  • Appreciation Award

    2016年11月   International Telecommunication Union  

    受賞者: 余 恪平

  • Scholarship for Young Doctoral Students

    2015年01月   早稲田大学  

    受賞者: 余 恪平

  • Student Presentation Award

    2014年12月   Japan Society foor Simulation Technology  

    受賞者: 余 恪平

  • Isao Okawa Scholarship for Information Technology Science

    2014年08月   早稲田大学  

    受賞者: 余 恪平

  • Canon Electronics Scholarship

    2013年11月   早稲田大学  

    受賞者: 余 恪平

  • JASSO Honors Scholarship

    2013年06月   早稲田大学  

    受賞者: 余 恪平

  • Canon Electronics Scholarship

    2012年10月   早稲田大学  

    受賞者: 余 恪平

  • Excellent Intern

    2011年11月   Fujitsu R&D Center (China)  

    受賞者: 余 恪平

  • Waseda University Partial Tuition-Waiver Scholarship

    2011年06月   早稲田大学  

    受賞者: 余 恪平

  • Canon Electronics Scholarship

    2011年06月   早稲田大学  

    受賞者: 余 恪平

  • Mitsubishi UFJ Trust Foundation Scholarship

    2011年06月   早稲田大学  

    受賞者: 余 恪平

  • Waseda University Partial Tuition-Waiver Scholarship

    2010年12月   早稲田大学  

    受賞者: 余 恪平

  • Privately Financed International Students Scholarship

    2010年10月   早稲田大学  

    受賞者: 余 恪平

  • Outstanding Graduate Student in Sichuan Province

    2010年05月   Government of Sichuan Province, China  

    受賞者: 余 恪平

▼全件表示

共同研究・競争的資金等の研究課題

  • Lightweight Distributed Ledger for Internet of Medical Things

    日本学術振興会 科学研究費助成事業 若手研究 

    研究期間:

    2021年04月
    -
    2023年03月
     

    余恪平

  • 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 開発及び異シ ステム間の周 波数共用技術 の高度化に関 する研究開発

    研究期間:

    2019年09月
    -
    2022年09月
     

    佐藤拓朗

  • 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  戦略的情報通信研究開発推進事業

    研究期間:

    2019年05月
    -
    2022年03月
     

    佐藤拓朗

  • Applying a Unified Access Control Enforcement Mechanism for Information-centric Internet of Things

    日本学術振興会 科学研究費助成事業  若手研究

    研究期間:

    2018年04月
    -
    2020年03月
     

    余恪平

  • 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  情報通信技術の研究開発「共通基盤技術の確立・実証」

    研究期間:

    2016年04月
    -
    2019年03月
     

    佐藤拓朗

  • IoTプラットフォームにおける情報通信機器・デバイス開発のための国際標準化実現可能性調査

    経済産業省  戦略的国際標準化加速事業(政府戦略分野に係る国際標準開発活動)

    佐藤拓朗

▼全件表示

講演・口頭発表等

  • Information Centric Advanced Metering Infrastructure in Smart Grid and its Standardization

    余 恪平

    2018 Hanyang-Waseda IT Workshop  

    発表年月: 2018年12月

  • Enabling Advanced Metering Infrastructure with Information-Centric Networking in Smart Grid

    余 恪平

    Japan society for simulation technology  

    発表年月: 2017年03月

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

    余 恪平

    2015 Hanyang-Waseda IT Workshop  

    発表年月: 2015年11月

  • A Security mechanism for Content-Centric Networking based Vehicle-to-Vehicle Data Delivery: A Case Study in Vehicular Ad Hoc Networks of Smart Grid

    余 恪平

    3rd International Symposium on Energy Challenges & Mechanics-towards a big picture  

    発表年月: 2015年07月

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

    余 恪平

    2014 Waseda-Thammasat Computer Science Research and Collaboration Workshop  

    発表年月: 2014年11月

  • Study on Cloud-based Modified Energy Management Scheme in Home Energy Management Systems for Smart Grid Network

    余 恪平

    2013 Waseda-Toshiba Workshop  

    発表年月: 2013年12月

  • Study on a New Algorithm in Home Energy Management System for Smart Grid

    余 恪平

    2013 Hanyang-Waseda IT Workshop  

    発表年月: 2013年11月

  • Study on Cloud-based Modified Energy Management Scheme in Home Energy Management Systems for Smart Grid Network

    余 恪平

    2013 Waseda-KDDI Workshop  

    発表年月: 2013年11月

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現在担当している科目

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委員歴

  • 2021年
    -
    継続中

    IEEE TrustCom 2021 Workshop on Emerging Blockchain Technology Solutions for Real-world Applications (EBTSRA), Shenyang, China, August 2021.  Workshop Chair

  • 2021年
    -
    継続中

    2021 International Conference on Wireless Communications and Signal Processing, Changsha, China, October 2021.  Trackchair

  • 2021年
    -
    継続中

    IEEE Transactions on Computational Social Systems, Special Issue on “Advanced Cognitive Computing for Data-Driven Computational Social Systems”  Guest Editor

  • 2021年
    -
    継続中

    IET Systems Biology, Special Issue on “AI Emerged Deep Learning Methods for Genomics: A New Biological Era”  Guest Editor

  • 2021年
    -
    継続中

    Soft Computing, Special Issue on “Advances in Pattern Recognition and Computer Vision, Applications and Systems”  Guest Editor

  • 2021年
    -
    継続中

    Wireless Communications and Mobile Computing, Special Issue on “Learning Methods for Urban Computing and Intelligence”  Guest Editor

  • 2021年
    -
    継続中

    Wireless Communications and Mobile Computing, Special Issue on “Recent Advances in Next Generation Cybersecurity Technologies”  Guest Editor

  • 2021年
    -
    継続中

    IET Intelligent Transport Systems, Special Issue on “Transportation Knowledge Graph for Intelligent Transportation Systems”  Guest Editor

  • 2021年
    -
    継続中

    Computer Communications, Special Section on AI-Empowered Internet of Things for Next Generation Industrial CPSs  Guest Editor

  • 2021年
    -
    継続中

    IEICE Transactions on Information and Systems, Special Section on Computational Intelligence and Big Data for Scientific and Technological Resources and Services  Guest Associate Editor

  • 2021年
    -
    継続中

    Sustainable Energy Technologies and Assessments, Special Issue “State-of-the-art Renewable Energy Harvesting from Agricultural Residues“  Leading Guest Editor

  • 2021年
    -
    継続中

    Control Engineering Practice, Special Issue “Peer-to-Peer Transactive Energy Management in Power Distribution Systems“ Leading Guest Editor: Sustainable Energy Technologies and Assessments  Leading Guest Editor

  • 2021年
    -
    継続中

    Journal of Electronic Imaging, Special Issue “Biologically Inspired Computer Vision and Image Processing“  Leading Guest Editor

  • 2021年
    -
    継続中

    Cluster Computing, Special Issue Green Edge/Fog/Cloud Computing: Advancements and Practices“  Leading Guest Editor

  • 2021年
    -
    継続中

    Journal of Database Management, Special Issue “Energy-Efficient Machine Learning and Big Data Analytics for Data-Intensive Applications“  Leading Guest Editor

  • 2021年
    -
    継続中

    Journal of Internet Technology, Special Issue “Current Trends, Challenges, and New Enablers of E-Commerce Applications“  Leading Guest Editor

  • 2021年
    -
    継続中

    Electric Power Systems Research, Special Issue " Peer-to-Peer Transactive Energy Management in Power Distribution Systems”  Leading Guest Editor

  • 2021年
    -
    継続中

    Cognitive Processing, Special Issue "Role of Multi-Modal Affective Computing in large-scale multimedia data analytics”  Leading Guest Editor

  • 2021年
    -
    継続中

    Journal of Circuits, Systems and Computers  Associate Editor

  • 2021年
    -
    継続中

    Journal of Intelligent Manufacturing  Associate Editor

  • 2020年02月
    -
    継続中

    The Second IEEE International Workshop on Blockchain and Mobile Applications (BlockApp 2020)  TPC Member

  • 2020年02月
    -
    継続中

    The 9th International Symposium on Security and Privacy on the Internet of Things (SPIoT)  Program Committee Member

  • 2020年02月
    -
    継続中

    Internatioanl Symposium on Community-centric Systems (CcS2020)  Session Co-chair

  • 2020年02月
    -
    継続中

    Springer Peer-to-Peer Networking and Applications, Special Issue on “Blockchain for Peer-to-Peer Computing”  Leading Guest Editor

  • 2020年02月
    -
    継続中

    MDPI Sensors, Special Issue "Emerging Blockchain Technology Solutions for Real-world Applications (EBTSRA)"  Leading Guest Editor

  • 2020年
    -
    継続中

    2020 International Conference on Soft Computing & Machine Learning (SCML2020)  TPC Co-chair

  • 2020年
    -
    継続中

    IEEE VTC2020-Spring Workshop on Emerging Blockchain Technology Solutions for Real-world Applications (EBTSRA)  Publicity Co-chairs

  • 2020年
    -
    継続中

    IEEE VTC2020-Spring Workshop on Emerging Blockchain Technology Solutions for Real-world Applications (EBTSRA)  General Co-chairs

  • 2020年
    -
    継続中

    IEEE 91st Vehicular Technology Conference: VTC2020-Spring  Technical Program Committee member

  • 2020年
    -
    継続中

    IEEE Consumer Communications & Networking Conference 2020  Technical Program Committee member

  • 2020年
    -
    継続中

    The 2020 IEEE Wireless Communications and Networking Conference (WCNC 2020)  Technical Program Committee member

  • 2020年
    -
    継続中

    Energies, Special Issue " Advances on Blockchain Technologies for Energy Systems”  Leading Guest Editor

  • 2020年01月
    -
    継続中

    ITU Kaleidoscope Academic Conferences 2020  TPC Member

  • 2019年
    -
    継続中

    2019 International Conference on Intelligent Transportation and Vehicle Engineering (ICITVE 2019)  Committee member

  • 2019年
    -
    継続中

    The 2019 International Conference on Electronic Engineering and Informatics (EEI 2019)  Committee member

  • 2019年
    -
    継続中

    IEEE VTC2019-Spring, First International Workshop on Heterogenous Mobile/Multi-Access Edge Computing (HMEC 2019)  TPC Member

  • 2019年
    -
    継続中

    ITU Kaleidoscope Academic Conferences 2019  Technical Program Committee member

  • 2019年
    -
    継続中

    IEEE 2019 2nd International Conference on Hot Information-Centric Networking (HotICN 2019)  Technical Program Committee member

  • 2019年
    -
    継続中

    The 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC - 2019)  Technical Program Committee member

  • 2019年
    -
    継続中

    2019 IEEE 5th International Conference on Computer and Communications (ICCC)  Technical Program Committee member

  • 2019年
    -
    継続中

    IEEE Open Journal of Vehicular Technology  Editor

  • 2016年
    -
    継続中

    2016 Hanyang-Waseda IT Workshop  Session Chair

  • 2016年
    -
    継続中

    ITU Kaleidoscope Academic Conferences 2016  Session Chair

  • 2021年
    -
     

    International Journal of System Assurance Engineering and Management, Special Issue “Advances in Machine Learning and Computational Intelligence in Health Care “  Leading Guest Editor

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