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Marcos S. Miranda - IEEE Xplore Author Profile

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Author details

Marcos S. Miranda

Also published under: M. S. Miranda

Publications

7

Citations

148

Publications by Year

19972007

Co-Authors:

Keith R. W. BellH. S. BrorazeadoRod W. DunnE. A. N. FeitosaF. Li

Show All Co-Authors (9)

Marcos S. Miranda

Also published under: M. S. Miranda

Affiliation

Department of Electrical Engineering

University of Bath

Bath, UK

Publication Topics

Power System,
Wind Power,
Weather Station,
Wind Speed Data,
Wind Turbine,
Autoregressive Model,
Average Wind Speed,
Bayesian Model,
Box-Cox Transformation,
Conventional Generation,
Degree Of Belief,
Hierarchical Model

Biography

Marcos S Miranda was born in Belo Horizonte, Brazil in 1973. He graduated from the Federal University of Minas Gerais (UFMG, Brazil) in Electrical Engineering in 1994 and obtained a Master's degree by Research in 1997, from the same university.,He then worked for one year as a Research Assistant at the Brazilian Wind Energy Centre at the Federal University of Pernambuco (UFPE, Brazil). In 1998 he started his PhD at the Centre for Renewable Energy Systems Technology (CREST) at Loughborough University (UK). His research project addressed wind-powered seawater desalination. Since the end of 2003 he has been with the Department of Electrical Engineering, University of Bath (UK) as a Research Associate, where he is currently involved in the EPSR...

Publications

7

Citations

148

Publications by Year

19972007

Co-Authors:

Keith R. W. Bell
H. S. Brorazeado
Rod W. Dunn
E. A. N. Feitosa
F. Li

Show All Co-Authors (9)

Author's Published Works

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Showing 1-12 of 12 results

Journals (6)

Conferences (3)

Magazines (2)

Early Access Articles (1)

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Qubeijian Wang(12)
Wen Sun(8)
Hong-Ning Dai(6)
Yan Zhang(3)
Yalin Liu(3)
School of Cybersecurity, Northwestern Polytechnical University, Xi’an, China(3)
School of Cyber Engineering, Xidian University, Xi’an, China(3)
Northwestern Polytechnical University, China(3)
Collaborative Innovation Center, Northwestern Polytechnical University, Shanghai, China(2)
School of Cybersecurity, Northwestern Polytechnical University, Xi'an, China(2)
IEEE Internet of Things Journal(2)
IEEE Network(2)
2021 IEEE Global Communications Conference (GLOBECOM)(1)
2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)(1)
GLOBECOM 2022 - 2022 IEEE Global Communications Conference(1)
IEEE(12)
Helsinki, Finland(1)
Madrid, Spain(1)
Rio de Janeiro, Brazil(1)
Unmanned Aerial Vehicles(7)
Directional Antenna(5)
Path Loss(5)
Antenna Gain(4)
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Smart Shield: Prevent Aerial Eavesdropping via Cooperative Intelligent Jamming Based on Multi-Agent Reinforcement Learning

Qubeijian Wang;Shiyue Tang;Wen Sun;Yin Zhang;Geng Sun;Hong-Ning Dai;Mohsen Guizani

IEEE Transactions on Mobile Computing

Year: 2025 | Volume: 24, Issue: 4 | Journal Article |

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The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. I...Show More

Smart Shield: Prevent Aerial Eavesdropping via Cooperative Intelligent Jamming Based on Multi-Agent Reinforcement Learning

Qubeijian Wang;Shiyue Tang;Wen Sun;Yin Zhang;Geng Sun;Hong-Ning Dai;Mohsen Guizani

IEEE Transactions on Mobile Computing

Year: 2025 | Volume: 24, Issue: 4 | Journal Article |

Evolutionary Game-Based Adaptive DT Association and Transfer for Wireless Computing Power Networks

Yadong Zhang;Peng Wang;Qubeijian Wang;Haibin Zhang;Lexi Xu;Wen Sun;Bin Wang

IEEE Transactions on Green Communications and Networking

Year: 2024 | Early Access Article |

Wireless Computing Power Networks (WCPN), guided by green principles, aim to provide efficient, flexible, and environmentally friendly computing services for Internet of Things (IoT) applications by seamlessly coordinating computational and networking resources across diverse nodes. The integration of Digital Twin (DT) technology is crucial for achieving these objectives. However, different DT ass...Show More

Evolutionary Game-Based Adaptive DT Association and Transfer for Wireless Computing Power Networks

Yadong Zhang;Peng Wang;Qubeijian Wang;Haibin Zhang;Lexi Xu;Wen Sun;Bin Wang

IEEE Transactions on Green Communications and Networking

Year: 2024 | Early Access Article |

Space-Air-Ground Integrated Networks: Spherical Stochastic Geometry-Based Uplink Connectivity Analysis

Yalin Liu;Hong-Ning Dai;Qubeijian Wang;Om Jee Pandey;Yaru Fu;Ning Zhang;Dusit Niyato;Chi Chung Lee

IEEE Journal on Selected Areas in Communications

Year: 2024 | Volume: 42, Issue: 5 | Journal Article |

Cited by: Papers (4)

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By integrating the merits of aerial, terrestrial, and satellite communications, the space-air-ground integrated network (SAGIN) is an emerging solution that can provide massive access, seamless coverage, and reliable transmissions for global-range applications. In SAGINs, the uplink connectivity from ground users (GUs) to the satellite is essential because it ensures global-range data collections ...Show More

Space-Air-Ground Integrated Networks: Spherical Stochastic Geometry-Based Uplink Connectivity Analysis

Yalin Liu;Hong-Ning Dai;Qubeijian Wang;Om Jee Pandey;Yaru Fu;Ning Zhang;Dusit Niyato;Chi Chung Lee

IEEE Journal on Selected Areas in Communications

Year: 2024 | Volume: 42, Issue: 5 | Journal Article |

Federated Learning With Privacy-Preserving Incentives for Aerial Computing Networks

Peng Wang;Yi Yang;Wen Sun;Qubeijian Wang;Bin Guo;Jianhua He;Yuanguo Bi

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

Cited by: Papers (2)

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With the help of artificial intelligence (AI) model, aerial computing can help analyze and predict the network dynamics and support intelligent decision-making to improve the performance of 6G space-air-ground integrated networks. Federated learning has been proposed to tackle the challenges of limited energy and data shortage for the application of AI models in aerial computing networks. A critic...Show More

Federated Learning With Privacy-Preserving Incentives for Aerial Computing Networks

Peng Wang;Yi Yang;Wen Sun;Qubeijian Wang;Bin Guo;Jianhua He;Yuanguo Bi

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

Low-Latency Communications for Digital Twin Empowered Web 3.0

Qubeijian Wang;Peng Wang;Wen Sun;Yan Zhang

Year: 2023 | Volume: 37, Issue: 6 | Magazine Article |

Cited by: Papers (1)

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The vision of Web 3.0 is to promote decentralized, intelligent, and autonomous Internet by self-learning and self-adaption of user demands. Such Internet evolution is enabled by ubiquitous and low-latency communication and powerful computing, imposing significant challenges on the Web 3.0 architecture and its network orchestration. The successful implementation of Web 3.0 relies on dynamic percept...Show More

Low-Latency Communications for Digital Twin Empowered Web 3.0

Qubeijian Wang;Peng Wang;Wen Sun;Yan Zhang

Year: 2023 | Volume: 37, Issue: 6 | Magazine Article |

Aerial Bridge: A Secure Tunnel Against Eavesdropping in Terrestrial-Satellite Networks

Qubeijian Wang;Hao Wang;Wen Sun;Nan Zhao;Hong-Ning Dai;Wei Zhang

IEEE Transactions on Wireless Communications

Year: 2023 | Volume: 22, Issue: 11 | Journal Article |

Cited by: Papers (1)

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Terrestrial-satellite networks (TSNs) can provide worldwide users with ubiquitous and seamless network services. Meanwhile, malicious eavesdropping is posing tremendous challenges on secure transmissions of TSNs due to their widescale wireless coverage. In this paper, we propose an aerial bridge scheme to establish secure tunnels for legitimate transmissions in TSNs. With the assistance of unmanne...Show More

Aerial Bridge: A Secure Tunnel Against Eavesdropping in Terrestrial-Satellite Networks

Qubeijian Wang;Hao Wang;Wen Sun;Nan Zhao;Hong-Ning Dai;Wei Zhang

IEEE Transactions on Wireless Communications

Year: 2023 | Volume: 22, Issue: 11 | Journal Article |

FedTAR: Task and Resource-Aware Federated Learning for Wireless Computing Power Networks

Wen Sun;Zongjun Li;Qubeijian Wang;Yan Zhang

IEEE Internet of Things Journal

Year: 2023 | Volume: 10, Issue: 5 | Journal Article |

Cited by: Papers (24)

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In the 6G era, the proliferation of data and data-intensive applications poses unprecedented challenges on the current communication and computing networks. The collaboration among cloud computing, edge computing, and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this article, we propose a wireless computing power network (WCP...Show More

FedTAR: Task and Resource-Aware Federated Learning for Wireless Computing Power Networks

Wen Sun;Zongjun Li;Qubeijian Wang;Yan Zhang

IEEE Internet of Things Journal

Year: 2023 | Volume: 10, Issue: 5 | Journal Article |

Aerial Assistant: Safeguarding Ground-to-Satellite Communication Networks

Hao Wang;Qubeijian Wang;Wen Sun;Nan Zhao;Hong-Ning Dai;Lexi Xu

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

Cited by: Papers (7)

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The ground-to-satellite communication network (G2SN) has highlighted the significance of constructing ubiquitous and seamless networks for the next-generation communication system. However, in the presence of secret eavesdroppers, securing massive transmission links is posing tremendous challenges for G2SNs. In this paper, we propose an aerial assistant scheme to safeguard legitimate transmissions...Show More

Aerial Assistant: Safeguarding Ground-to-Satellite Communication Networks

Hao Wang;Qubeijian Wang;Wen Sun;Nan Zhao;Hong-Ning Dai;Lexi Xu

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Year: 2022 | Conference Paper |

Energy-Efficient Federated Learning for Wireless Computing Power Networks

Zongjun Li;Haibin Zhang;Qubeijian Wang;Wen Sun;Yan Zhang

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

HTML

In the 6G era, the proliferation of data poses unprecedented challenges on the current computing networks. The collaboration among cloud computing, edge computing and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this paper, we propose a Wireless Computing Power Networks (WCPN) by orchestrating the computing and networking res...Show More

Energy-Efficient Federated Learning for Wireless Computing Power Networks

Zongjun Li;Haibin Zhang;Qubeijian Wang;Wen Sun;Yan Zhang

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

Year: 2022 | Conference Paper |

Wireless Powering Internet of Things with UAVs: Challenges and Opportunities

Yalin Liu;Hong-Ning Dai;Qubeijian Wang;Muhammad Imran;Nadra Guizani

Year: 2022 | Volume: 36, Issue: 2 | Magazine Article |

Cited by: Papers (10)

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Unmanned aerial vehicles (UAVs) have the potential to overcome the deployment constraint of The Internet of Things (IoT) in remote or rural areas. Wirelessly powered communications (WPC) can address the battery limitation of IoT devices through transferring wireless power to IoT devices. The integration of UAVs and WPC, namely UAV-enabled wireless powering IoT (Ue-WPI-o T) can greatly extend the I...Show More

Wireless Powering Internet of Things with UAVs: Challenges and Opportunities

Yalin Liu;Hong-Ning Dai;Qubeijian Wang;Muhammad Imran;Nadra Guizani

Year: 2022 | Volume: 36, Issue: 2 | Magazine Article |

Ear in the Sky: Terrestrial Mobile Jamming to Prevent Aerial Eavesdropping

Qubeijian Wang;Yalin Liu;Hong–Ning Dai;Muhammad Imran;Nidal Nasser

2021 IEEE Global Communications Conference (GLOBECOM)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping...Show More

Ear in the Sky: Terrestrial Mobile Jamming to Prevent Aerial Eavesdropping

Qubeijian Wang;Yalin Liu;Hong–Ning Dai;Muhammad Imran;Nidal Nasser

2021 IEEE Global Communications Conference (GLOBECOM)

Year: 2021 | Conference Paper |

On Connectivity of UAV-Assisted Data Acquisition for Underwater Internet of Things

Qubeijian Wang;Hong-Ning Dai;Qiu Wang;Mahendra K. Shukla;Wei Zhang;Carlos Guedes Soares

IEEE Internet of Things Journal

Year: 2020 | Volume: 7, Issue: 6 | Journal Article |

Cited by: Papers (65)

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Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires a huge cost of deploying and maintaining communication infrastructures. In this article, we propose an unmanned aerial vehicles (UAVs)-assisted underwater data acquisition scheme by ...Show More

On Connectivity of UAV-Assisted Data Acquisition for Underwater Internet of Things

Qubeijian Wang;Hong-Ning Dai;Qiu Wang;Mahendra K. Shukla;Wei Zhang;Carlos Guedes Soares

IEEE Internet of Things Journal

Year: 2020 | Volume: 7, Issue: 6 | Journal Article |

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