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Silvia Maria Zanoli - IEEE Xplore Author Profile

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Author details

Silvia Maria Zanoli

Also published under: Silvia M. Zanoli, S. M. Zanoli, S. Zanoli

Publications

51

Citations

203

Publications by Year

19942024

Co-Authors:

Mir Farooq AliG. AstolfiL. BarboniD. BarchiesiV. Calabro

Show All Co-Authors (31)

Silvia Maria Zanoli

Also published under: Silvia M. Zanoli, S. M. Zanoli, S. Zanoli

Affiliation

Dip. Ingegneria Dell’Informazione (DII)

Università Politecnica Delle Marche

Ancona (AN), Italy

Publication Topics

Model Predictive Control,
Control System,
Model Predictive Control Strategy,
Prediction Horizon,
Real Plant,
Control Horizon,
Control Strategy,
Control Variables,
Explanatory Variables,
Indoor Air Quality,
Nonlinear Model,
Programmable Logic Controllers

Biography

Silvia Maria Zanoli (Member, IEEE) received the M.Sc. degree in electronic engineering and the Ph.D. degree in artificial intelligent systems from the University of Ancona, Ancona, Italy in 1992 and 1996, respectively.,She is currently an Assistant Professor with the Università Politecnica Marche, Ancona, Italy, holding courses on the field of industrial automation. She has been a visiting professor with the MSEL of the Northeastern University, Boston, MA, USA. She works on the development of advanced process control systems and fault diagnosis systems. She has collaborated on many national and European projects on robotics and industrial automations. Her research interests include model predictive control, advanced process control, fault-d...

Publications

51

Citations

203

Publications by Year

19942024

Co-Authors:

Mir Farooq Ali
G. Astolfi
L. Barboni
D. Barchiesi
V. Calabro

Show All Co-Authors (31)

Author's Published Works

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Showing 1-25 of 37 results

Journals (23)

Conferences (12)

Magazines (2)

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Laisen Nie(37)
Zhaolong Ning(11)
Yixuan Wu(7)
Shengtao Li(7)
Xiaojie Wang(6)
School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China(19)
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China(9)
School of Software, Dalian University of Technology, Dalian, China(6)
School of Electronics and Information, Northwestern Polytechnical University, Xi'an, China(5)
School of Information Science and Engineering, Shandong Normal University, Jinan, China(5)
IEEE Transactions on Industrial Informatics(6)
IEEE Access(5)
IEEE Internet of Things Journal(3)
2019 IEEE Global Communications Conference (GLOBECOM)(2)
IEEE Transactions on Computational Social Systems(2)
IEEE(37)
Waikoloa, HI, USA(2)
Beijing, China(1)
Hangzhou, China(1)
Nanjing, China(1)
Rio de Janeiro, Brazil(1)
Traffic Flow(16)
Internet Of Things(14)
Network Management(12)
Convolutional Neural Network(11)
Distributed Denial Of Service(11)

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A Federated Learning Client Selection Method via Multi-Task Deep Reinforcement Learning

Le Hou;Laisen Nie;Xinyang Deng

2024 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2024 | Conference Paper |

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Federated Learning (FL) is a privacy-preserving paradigm for training machine learning (ML) models, crucial for data privacy and security protection. It has garnered significant attention from both industry and academia. Typically, clients are selected randomly for training and model aggregation in FL scenarios. However, heterogeneity in data distribution and hardware among devices leads to proble...Show More

A Federated Learning Client Selection Method via Multi-Task Deep Reinforcement Learning

Le Hou;Laisen Nie;Xinyang Deng

2024 IEEE International Conference on Unmanned Systems (ICUS)

Year: 2024 | Conference Paper |

A Federated Learning Mechanism with Feature Drift for Feature Distribution Skew

Jihao Yang;Xinyang Deng;Laisen Nie;Wen Jiang

2024 27th International Conference on Information Fusion (FUSION)

Year: 2024 | Conference Paper |

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Federated learning is a nascent distributed machine learning paradigm that enables multiple clients to collaborate in training a model for a specific task under the coordination of a central server, all while safeguarding the privacy of the user’s local data. Nevertheless, the constraint that distributed datasets must remain within local nodes introduces data heterogeneity in federated learning tr...Show More

A Federated Learning Mechanism with Feature Drift for Feature Distribution Skew

Jihao Yang;Xinyang Deng;Laisen Nie;Wen Jiang

2024 27th International Conference on Information Fusion (FUSION)

Year: 2024 | Conference Paper |

Intrusion Detection for Unmanned Aerial Vehicles Security: A Tiny Machine Learning Model

Yixuan Wu;Lin Yang;Long Zhang;Laisen Nie;Li Zheng

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 12 | Journal Article |

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Unmanned aerial vehicles (UAVs) are vulnerable to network attacks. Designing an effective intrusion detection system (IDS) for UAVs is crucial. However, UAVs have limited computing resources and need to deal with massive amounts of network data, which further increases the difficulty of detection. Moreover, most existing IDSs have large parameters. In this study, we develop a tiny machine learning...Show More

Intrusion Detection for Unmanned Aerial Vehicles Security: A Tiny Machine Learning Model

Yixuan Wu;Lin Yang;Long Zhang;Laisen Nie;Li Zheng

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 12 | Journal Article |

Incremental Update Intrusion Detection for Industry 5.0 Security: A Graph Attention Network-Enabled Approach

Yixuan Wu;Laisen Nie;Xuanrui Xiong;Balqies Sadoun;Lin Yang;Zhaolong Ning

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

Cited by: Papers (3)

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The Industry 5.0 (I5) incorporates numerous emerging technologies that enable the perception, management, interaction, and control of real physical objects. As cyber attacks are becoming more intelligent and accessible, I5 security issues are gaining increasing prominence. An intrusion detection system plays an important role in network security by detecting and identifying various security threat...Show More

Incremental Update Intrusion Detection for Industry 5.0 Security: A Graph Attention Network-Enabled Approach

Yixuan Wu;Laisen Nie;Xuanrui Xiong;Balqies Sadoun;Lin Yang;Zhaolong Ning

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

Hypernetworks-Based Hierarchical Federated Learning on Hybrid Non-IID Datasets for Digital Twin in Industrial IoT

Jihao Yang;Wen Jiang;Laisen Nie

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 2 | Journal Article |

Cited by: Papers (1)

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Digital twin technology deployed in the industrial Internet of Things (IoT) will be unavailable because of data heterogeneity and data islands. As a machine learning approach for distributed architectures, federated learning generates a global model for edge devices by only transferring model parameters, and it ensures that the private data of each device is not leaked. This is naturally suitable ...Show More

Hypernetworks-Based Hierarchical Federated Learning on Hybrid Non-IID Datasets for Digital Twin in Industrial IoT

Jihao Yang;Wen Jiang;Laisen Nie

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 2 | Journal Article |

Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Laisen Nie;Xiaojie Wang;Qinglin Zhao;Zhigang Shang;Li Feng;Guojun Li

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 1 | Journal Article |

Cited by: Papers (26)

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Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system h...Show More

Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Laisen Nie;Xiaojie Wang;Qinglin Zhao;Zhigang Shang;Li Feng;Guojun Li

IEEE Transactions on Intelligent Transportation Systems

Year: 2024 | Volume: 25, Issue: 1 | Journal Article |

Rumors Suppression in Healthcare System: Opinion-Based Comprehensive Learning Particle Swarm Optimization

Qiang He;Wei Qiao;Ali Kashif Bashir;Yuliang Cai;Laisen Nie;Yasser D. Al-Otaibi;Keping Yu

IEEE Transactions on Computational Social Systems

Year: 2023 | Volume: 10, Issue: 4 | Journal Article |

Cited by: Papers (2)

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The rumors in the healthcare system have the attributes of fast spread and severe social influence. Even worse, it may cause the collapse of medical services and the death of many patients. To prevent its serious impact on society, the target of rumor suppression for the healthcare system is to restrain the spread of rumors (negative opinions) and maximize the spread of antirumors (positive opinio...Show More

Rumors Suppression in Healthcare System: Opinion-Based Comprehensive Learning Particle Swarm Optimization

Qiang He;Wei Qiao;Ali Kashif Bashir;Yuliang Cai;Laisen Nie;Yasser D. Al-Otaibi;Keping Yu

IEEE Transactions on Computational Social Systems

Year: 2023 | Volume: 10, Issue: 4 | Journal Article |

Intelligent Intrusion Detection for Internet of Things Security: A Deep Convolutional Generative Adversarial Network-Enabled Approach

Yixuan Wu;Laisen Nie;Shupeng Wang;Zhaolong Ning;Shengtao Li

IEEE Internet of Things Journal

Year: 2023 | Volume: 10, Issue: 4 | Journal Article |

Cited by: Papers (35)

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With the rapid advance of Internet of Things (IoT), it is difficult for cloud-centric computing to meet the requirements of low latency and ease of use. As an open and distributed system, edge computing integrates computing, networking, storage, and applications. It provides intelligent services on the edge of an IoT. The edge network is composed of various wireless and wired networks, and the com...Show More

Intelligent Intrusion Detection for Internet of Things Security: A Deep Convolutional Generative Adversarial Network-Enabled Approach

Yixuan Wu;Laisen Nie;Shupeng Wang;Zhaolong Ning;Shengtao Li

IEEE Internet of Things Journal

Year: 2023 | Volume: 10, Issue: 4 | Journal Article |

Network Traffic Prediction for Intelligent Transportation Systems: A Reinforcement Learning Approach

Jian Song;Hua Liu;Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

Cited by: Papers (3)

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Vehicular Ad-Hoc Networks (VANETs), as the cru-cial support of Intelligent Transportation Systems (ITS), have received a great attention in recent years. Network traffic prediction is useful for network management and security in VANETs, such as network planning and anomaly detection. Due to the movement of nodes, the traffic flow in VANETs consists of a great number of irregular fluctuations, whi...Show More

Network Traffic Prediction for Intelligent Transportation Systems: A Reinforcement Learning Approach

Jian Song;Hua Liu;Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

A Redactable Blockchain Framework for Secure Federated Learning in Industrial Internet of Things

Jiannan Wei;Qinchuan Zhu;Qianmu Li;Laisen Nie;Zhangyi Shen;Kim-Kwang Raymond Choo;Keping Yu

IEEE Internet of Things Journal

Year: 2022 | Volume: 9, Issue: 18 | Journal Article |

Cited by: Papers (42)

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Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because m...Show More

A Redactable Blockchain Framework for Secure Federated Learning in Industrial Internet of Things

Jiannan Wei;Qinchuan Zhu;Qianmu Li;Laisen Nie;Zhangyi Shen;Kim-Kwang Raymond Choo;Keping Yu

IEEE Internet of Things Journal

Year: 2022 | Volume: 9, Issue: 18 | Journal Article |

A NOMA-Enabled Framework for Relay Deployment and Network Optimization in Double-Layer Airborne Access VANETs

Yixin He;Laisen Nie;Tan Guo;Kuljeet Kaur;Mohammad Mehedi Hassan;Keping Yu

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 11 | Journal Article |

Cited by: Papers (42)

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A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme ...Show More

A NOMA-Enabled Framework for Relay Deployment and Network Optimization in Double-Layer Airborne Access VANETs

Yixin He;Laisen Nie;Tan Guo;Kuljeet Kaur;Mohammad Mehedi Hassan;Keping Yu

IEEE Transactions on Intelligent Transportation Systems

Year: 2022 | Volume: 23, Issue: 11 | Journal Article |

A Multitask Learning-Based Network Traffic Prediction Approach for SDN-Enabled Industrial Internet of Things

Shupeng Wang;Laisen Nie;Guojun Li;Yixuan Wu;Zhaolong Ning

IEEE Transactions on Industrial Informatics

Year: 2022 | Volume: 18, Issue: 11 | Journal Article |

Cited by: Papers (17)

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With the rapid advance of industrial Internet of Things (IIoT), to provide flexible access for various infrastructures and applications, software-defined networks (SDNs) have been involved in constructing current IIoT networks. To improve the quality of services of industrial applications, network traffic prediction has become an important research direction, which is beneficial for network manage...Show More

A Multitask Learning-Based Network Traffic Prediction Approach for SDN-Enabled Industrial Internet of Things

Shupeng Wang;Laisen Nie;Guojun Li;Yixuan Wu;Zhaolong Ning

IEEE Transactions on Industrial Informatics

Year: 2022 | Volume: 18, Issue: 11 | Journal Article |

Distributed Orchestration of Service Function Chains for Edge Intelligence in the Industrial Internet of Things

Handi Chen;Shupeng Wang;Guojun Li;Laisen Nie;Xiaojie Wang;Zhaolong Ning

IEEE Transactions on Industrial Informatics

Year: 2022 | Volume: 18, Issue: 9 | Journal Article |

Cited by: Papers (17)

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Network virtualization techniques are promising to overcome the obstacle of applying and expanding costly traditional networks in the industrial Internet of things (IIoT). Artificial intelligence (AI)-enhanced distributed resource management in edge networks has aroused researchers’ widespread attention. However, dynamically arrived service requests and limited edge resources complicate the servic...Show More

Distributed Orchestration of Service Function Chains for Edge Intelligence in the Industrial Internet of Things

Handi Chen;Shupeng Wang;Guojun Li;Laisen Nie;Xiaojie Wang;Zhaolong Ning

IEEE Transactions on Industrial Informatics

Year: 2022 | Volume: 18, Issue: 9 | Journal Article |

Delay-Sensitive Secure NOMA Transmission for Hierarchical HAP–LAP Medical-Care IoT Networks

Dawei Wang;Yixin He;Keping Yu;Gautam Srivastava;Laisen Nie;Ruonan Zhang

IEEE Transactions on Industrial Informatics

Year: 2022 | Volume: 18, Issue: 8 | Journal Article |

Cited by: Papers (59)

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Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple acce...Show More

Delay-Sensitive Secure NOMA Transmission for Hierarchical HAP–LAP Medical-Care IoT Networks

Dawei Wang;Yixin He;Keping Yu;Gautam Srivastava;Laisen Nie;Ruonan Zhang

IEEE Transactions on Industrial Informatics

Year: 2022 | Volume: 18, Issue: 8 | Journal Article |

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Laisen Nie;Yixuan Wu;Xiaojie Wang;Lei Guo;Guoyin Wang;Xinbo Gao;Shengtao Li

IEEE Transactions on Computational Social Systems

Year: 2022 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (89)

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The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices,...Show More

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Laisen Nie;Yixuan Wu;Xiaojie Wang;Lei Guo;Guoyin Wang;Xinbo Gao;Shengtao Li

IEEE Transactions on Computational Social Systems

Year: 2022 | Volume: 9, Issue: 1 | Journal Article |

Imitation-Learning-Enabled Vehicular Edge Computing: Toward Online Task Scheduling

Laisen Nie;Xiaojie Wang;Wentao Sun;Yongkang Li;Shengtao Li;Peng Zhang

Year: 2021 | Volume: 35, Issue: 3 | Magazine Article |

Cited by: Papers (10)

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Vehicular edge computing (VEC) is a promising paradigm to enable information sharing and acquisition among vehicles. Since information freshness has significantly influence on task scheduling, an emerging metric, named the age of information (AoI), has been utilized to evaluate it. Recent research generally focuses on AoI minimization but pays little attention to information personality. However, ...Show More

Imitation-Learning-Enabled Vehicular Edge Computing: Toward Online Task Scheduling

Laisen Nie;Xiaojie Wang;Wentao Sun;Yongkang Li;Shengtao Li;Peng Zhang

Year: 2021 | Volume: 35, Issue: 3 | Magazine Article |

Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm

Laisen Nie;Wentao Sun;Shupeng Wang;Zhaolong Ning;Joel J. P. C. Rodrigues;Yixuan Wu;Shengtao Li

IEEE Transactions on Green Communications and Networking

Year: 2021 | Volume: 5, Issue: 2 | Journal Article |

Cited by: Papers (33)

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Internet of Things (IoT) is an expanded application of Internet, which are used to provide various services for users. Up to now, IoTs have received more attention, because it can provide ubiquitous connectivity for various devices. With the development of IoTs, its security has become a main issue. Attackers use various techniques to implement cyber attacks for the IoT, which threats the users' p...Show More

Intrusion Detection in Green Internet of Things: A Deep Deterministic Policy Gradient-Based Algorithm

Laisen Nie;Wentao Sun;Shupeng Wang;Zhaolong Ning;Joel J. P. C. Rodrigues;Yixuan Wu;Shengtao Li

IEEE Transactions on Green Communications and Networking

Year: 2021 | Volume: 5, Issue: 2 | Journal Article |

Network Traffic Prediction in Industrial Internet of Things Backbone Networks: A Multitask Learning Mechanism

Laisen Nie;Xiaojie Wang;Shupeng Wang;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun;Shengtao Li

IEEE Transactions on Industrial Informatics

Year: 2021 | Volume: 17, Issue: 10 | Journal Article |

Cited by: Papers (30)

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Industrial Internet of Things (IIoT), as a common industrial application of Internet of Things, has been widely deployed in recent years. End-to-end network traffic is an essential information for many network security and management functions. This article investigates the issues of IIoT-oriented backbone network traffic prediction. Predicting the traffic of IIoT backbone networks is intractable ...Show More

Network Traffic Prediction in Industrial Internet of Things Backbone Networks: A Multitask Learning Mechanism

Laisen Nie;Xiaojie Wang;Shupeng Wang;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun;Shengtao Li

IEEE Transactions on Industrial Informatics

Year: 2021 | Volume: 17, Issue: 10 | Journal Article |

Blockchain Based IIoT Data Sharing Framework for SDN-Enabled Pervasive Edge Computing

Ying Gao;Yijian Chen;Xiping Hu;Hongliang Lin;Yangliang Liu;Laisen Nie

IEEE Transactions on Industrial Informatics

Year: 2021 | Volume: 17, Issue: 7 | Journal Article |

Cited by: Papers (39)

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Pervasive edge computing (PEC) is an emerging paradigm for the industrial Internet of Things (IIoT), and software-defined networks (SDN) offer lower latency services, and massive intelligent devices connectivity for the IIoT. However, the PEC has some issues with data security, and privacy while PEC devices sharing data among edges. What's more, the centralized SDN suffers from single point of att...Show More

Blockchain Based IIoT Data Sharing Framework for SDN-Enabled Pervasive Edge Computing

Ying Gao;Yijian Chen;Xiping Hu;Hongliang Lin;Yangliang Liu;Laisen Nie

IEEE Transactions on Industrial Informatics

Year: 2021 | Volume: 17, Issue: 7 | Journal Article |

A Reinforcement Learning-Based Network Traffic Prediction Mechanism in Intelligent Internet of Things

Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun;Huizhi Wang;Shengtao Li;Lei Guo;Guoyin Wang

IEEE Transactions on Industrial Informatics

Year: 2021 | Volume: 17, Issue: 3 | Journal Article |

Cited by: Papers (59)

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Intelligent Internet of Things (IIoT) is comprised of various wireless and wired networks for industrial applications, which makes it complex and heterogeneous.The openness of IIoT has led to the intractable problems of network security and management. Many network security and management functions rely on network traffic prediction techniques, such as anomaly detection and predictive network plan...Show More

A Reinforcement Learning-Based Network Traffic Prediction Mechanism in Intelligent Internet of Things

Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Balqies Sadoun;Huizhi Wang;Shengtao Li;Lei Guo;Guoyin Wang

IEEE Transactions on Industrial Informatics

Year: 2021 | Volume: 17, Issue: 3 | Journal Article |

Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method

Laisen Nie;Zhaolong Ning;Xiaojie Wang;Xiping Hu;Jun Cheng;Yongkang Li

IEEE Transactions on Network Science and Engineering

Year: 2020 | Volume: 7, Issue: 4 | Journal Article |

Cited by: Papers (101)

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As an industrial application of Internet of Things (IoT), Internet of Vehicles (IoV) is one of the most crucial techniques for Intelligent Transportation System (ITS), which is a basic element of smart cities. The primary issue for the deployment of ITS based on IoV is the security for both users and infrastructures. The Intrusion Detection System (IDS) is important for IoV users to keep them away...Show More

Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method

Laisen Nie;Zhaolong Ning;Xiaojie Wang;Xiping Hu;Jun Cheng;Yongkang Li

IEEE Transactions on Network Science and Engineering

Year: 2020 | Volume: 7, Issue: 4 | Journal Article |

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

Laisen Nie;Huizhi Wang;Xin Jiang;Yi Guo;Shengtao Li

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (6)

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The end-to-end network traffic information is the basis of network management for a large-scale intelligent transportation systems-oriented backbone network. To obtain exact network traffic data, a prevalent idea is to deploy NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, but also affects the network load. Motivated by this issue, ...Show More

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale ITS-Oriented Backbone Networks

Laisen Nie;Huizhi Wang;Xin Jiang;Yi Guo;Shengtao Li

Year: 2020 | Volume: 8 | Journal Article |

Study on the range equation modeling of MEO SAR-GMTI systems

Yongkang Li;Laisen Nie

2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

Year: 2019 | Conference Paper |

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Due to the advantages of large coverage, short revisit time, and long synthetic aperture time, medium-earth-orbit (MEO) synthetic aperture radar (SAR)-ground moving target indication (GMTI) system is an attractive tool for space-to-ground surveillance and reconnaissance. However, the nonlinear motion of MEO SAR and the significant earth rotation dramatically complicate the relative motion between ...Show More

Study on the range equation modeling of MEO SAR-GMTI systems

Yongkang Li;Laisen Nie

2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

Year: 2019 | Conference Paper |

Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Laisen Nie;Huizhi Wang;Shimin Gong;Zhaolong Ning;Mohammad S. Obaidat;Kuei-Fang Hsiao

2019 IEEE Global Communications Conference (GLOBECOM)

Year: 2019 | Conference Paper |

Cited by: Papers (9)

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Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection f...Show More

Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Laisen Nie;Huizhi Wang;Shimin Gong;Zhaolong Ning;Mohammad S. Obaidat;Kuei-Fang Hsiao

2019 IEEE Global Communications Conference (GLOBECOM)

Year: 2019 | Conference Paper |

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale IP Backbone Networks

Huizhi Wang;Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Runze Shang

2019 IEEE Global Communications Conference (GLOBECOM)

Year: 2019 | Conference Paper |

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The end-to-end network traffic information is the basis of network management in large-scale IP backbone networks. To obtain exact network traffic data, a prevalent idea is to employ NetFlow or sFlow on all routers of the network. However, this method not only increases operational expenditures, it also affects the network load. Motivated by this issue, we propose an optimized traffic measurement ...Show More

Traffic Measurement Optimization Based on Reinforcement Learning in Large-Scale IP Backbone Networks

Huizhi Wang;Laisen Nie;Zhaolong Ning;Mohammad S. Obaidat;Runze Shang

2019 IEEE Global Communications Conference (GLOBECOM)

Year: 2019 | Conference Paper |

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