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Michael S. Brown - IEEE Xplore Author Profile

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Author details

Michael S. Brown

Also published under: M. Brown, M. S. Brown, Michael Brown

Publications

118

Citations

6,531

Publications by Year

19972024

Co-Authors:

Abdelrahman AbdelhamedAbdullah AbuolaimMahmoud AfifiNikhil AkalwadiKazutoshi Akita

Show All Co-Authors (425)

Michael S. Brown

Also published under: M. Brown, M. S. Brown, Michael Brown

Affiliation

York University, Toronto, Canada

Publication Topics

Peak Signal-to-noise Ratio,
Raw Images,
Color Space,
Deep Neural Network,
Image Enhancement,
Image Sensor,
Input Image,
Convolutional Layers,
Convolutional Neural Network,
Generative Adversarial Networks,
Noisy Images,
White Balance

Biography

Michael S. Brown received the BS and PhD degrees in computer science from the University of Kentucky in 1995 and 2001, respectively. He is currently an associate professor and assistant dean (external relations) in the School of Computing at the National University of Singapore. His research interests include computer vision, image processing, and computer graphics. He has served as an area chair for CVPR, ICCV, ECCV, and ACCV and is currently an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).(Based on document published on 14 February 2013).

Publications

118

Citations

6,531

Publications by Year

19972024

Co-Authors:

Abdelrahman Abdelhamed
Abdullah Abuolaim
Mahmoud Afifi
Nikhil Akalwadi
Kazutoshi Akita

Show All Co-Authors (425)

Author's Published Works

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Showing 1-25 of 46 results

Conferences (33)

Journals (13)

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Xun Shao(46)
Zhi Liu(9)
Hiroshi Masui(7)
Bingyi Liu(6)
Ziji Ma(6)
Department of Electrical and Electronic Information Engineering, Toyohashi University of Technology, Japan(7)
Toyohashi University of Technology, Toyohashi, Japan(6)
School of Software, Jiangxi Normal University, Nanchang, China(5)
Management Science and Engineering Center, Jiangxi Normal University, Nanchang, China(4)
School of Software Engineering, Beijing Jiaotong University, China(4)
2024 IEEE Smart World Congress (SWC)(6)
IEEE Transactions on Green Communications and Networking(4)
2019 28th International Conference on Computer Communication and Networks (ICCCN)(2)
2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring)(2)
2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)(2)
IEEE(46)
Nadi, Fiji(6)
Kuala Lumpur, Malaysia(3)
Kaifeng, China(2)
Ocean Flower Island, China(2)
Rome, Italy(2)
Edge Computing(12)
Time Slot(12)
Mobile Edge Computing(10)
Deep Reinforcement Learning(8)
Edge Server(8)

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Results

Multi-hop Link Quality Prediction for DRL based Task Offloading in RSU-assisted IoV

Wei Zhao;Qixue Zuo;Tangjie Weng;Xinwei Xu;Xun Shao;Zhi Liu

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

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Roadside units (RSUs) with strong sensing abilities enhance the feasibility of the RSU-to-Everything (R2X) paradigm, providing crucial infrastructure support for mobile edge computing and reducing data processing latency. Due to the limited computing capacity of RSUs, it is beneficial to offload tasks to other RSUs, vehicles, and cloud servers for processing, allowing for the efficient utilization...Show More

Multi-hop Link Quality Prediction for DRL based Task Offloading in RSU-assisted IoV

Wei Zhao;Qixue Zuo;Tangjie Weng;Xinwei Xu;Xun Shao;Zhi Liu

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

A Privacy-Preserving Decentralized Federated Learning Framework Based on Differential Privacy

Lin Wang;Di Zhang;Min Guo;Xun Shao

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

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Federated learning allows machine learning models to be trained by local clients without sharing data directly, providing a degree of privacy protection. However, existing studies reveal that traditional federated learning is prone to issues like gradient leakage and model poisoning, which can compromise user privacy and negatively impact the performance of the global model. In this paper, we prop...Show More

A Privacy-Preserving Decentralized Federated Learning Framework Based on Differential Privacy

Lin Wang;Di Zhang;Min Guo;Xun Shao

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

Optimal partitioning method among heterogeneous edge nodes in CNN models

Ryoya Okamoto;Xun Shao

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

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In recent years, addressing the challenges of edge computing in executing complex CNN models has become crucial, with a focus on constructing lightweight models. While model compression can lead to accuracy degradation, model partitioning offers a method to reduce model size without compromising accuracy. This paper studies optimal partitioning methods between heterogeneous edge nodes, verifying t...Show More

Optimal partitioning method among heterogeneous edge nodes in CNN models

Ryoya Okamoto;Xun Shao

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

CPIMAC: MEC-assisted Message Dissemination Based on Traffic Prediction at Intersections

Bingyi Liu;Xun Shao;Haoxiang Zhao;Enshu Wang;Zhi Liu

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

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The Internet of Vehicles (IoV) is gaining increasing attention for its ability to improve traffic safety and efficiency by providing situational awareness and detecting potential hazards beyond the visual range. Time division multiple access (TDMA) based communication protocol effectively prevents transmission collisions in IoV. However, road intersections pose challenges for the implementation of...Show More

CPIMAC: MEC-assisted Message Dissemination Based on Traffic Prediction at Intersections

Bingyi Liu;Xun Shao;Haoxiang Zhao;Enshu Wang;Zhi Liu

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

Efficient Resource Allocation in Vehicular Networks Using Embedding-Enhanced MAPPO

Wen Qiu;Xun Shao;Wei Zhao;Hiroshi Masui

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

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With the swift advancement of automotive technology, the need for efficient allocation of resources in V2V (vehicle-to-vehicle) communications has grown significantly. However, the dynamic nature of channels in vehicular networks introduces considerable uncertainty in resource allocation. This research presents a novel approach to addressing these challenges by utilizing multi-agent deep reinforce...Show More

Efficient Resource Allocation in Vehicular Networks Using Embedding-Enhanced MAPPO

Wen Qiu;Xun Shao;Wei Zhao;Hiroshi Masui

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

MAPPO based Single-hop Task Offloading with Dynamic Vehicle Prediction in RSU-assisted IoV

Wei Zhao;Dongling Yang;Tangjie Weng;Xinwei Xu;Xun Shao;Zhi Liu

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

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In the realm of Internet of Vehicles (IoV), Roadside Units (RSUs) play a vital role due to their advanced sensing and computing capabilities. They support Mobile Edge Computing (MEC) by enabling real-time data processing and reducing latency and energy consumption. Given the large volume of data and the limited computing capacity of RSUs, tasks are often offloaded to other nodes, such as the cloud...Show More

MAPPO based Single-hop Task Offloading with Dynamic Vehicle Prediction in RSU-assisted IoV

Wei Zhao;Dongling Yang;Tangjie Weng;Xinwei Xu;Xun Shao;Zhi Liu

2024 IEEE Smart World Congress (SWC)

Year: 2024 | Conference Paper |

DualLight: Intelligent Traffic Signal Control Method Based on Deep Reinforcement Learning

Huanqiang Tang;Fan He;Yanfen Cheng;Xun Shao

2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)

Year: 2024 | Conference Paper |

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In recent years, research combining max pressure (MP) with reinforcement learning for traffic signal control has become a hot topic in the field of intelligent transportation. However, existing MP methods ignore the importance of moving vehicles, which prevents the model from focusing on sufficient key information when extracting state features, thus making it suffer from unstable learning, slow c...Show More

DualLight: Intelligent Traffic Signal Control Method Based on Deep Reinforcement Learning

Huanqiang Tang;Fan He;Yanfen Cheng;Xun Shao

2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)

Year: 2024 | Conference Paper |

HEGD-FL: A Privacy-Preserving Decentralized Federated Learning Framework Based on Homomorphic Encryption

Pengyu Yao;Di Zhang;Min Guo;Xun Shao

2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)

Year: 2024 | Conference Paper |

Cited by: Papers (1)

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Decentralized Federated Learning (DFL) has been proposed to address the potential single point of failure issues in traditional federated learning. However, existing DFL frameworks face challenges such as significant computational and communication overhead, along with low node collaboration efficiency, which limits their applicability in lightweight scenarios. Moreover, privacy-preserving mechani...Show More

HEGD-FL: A Privacy-Preserving Decentralized Federated Learning Framework Based on Homomorphic Encryption

Pengyu Yao;Di Zhang;Min Guo;Xun Shao

2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)

Year: 2024 | Conference Paper |

Motion Deblurring by Fusing Multi-Stage Image Features

Shihua Zhang;Fan He;Xun Shao

2024 IEEE Cyber Science and Technology Congress (CyberSciTech)

Year: 2024 | Conference Paper |

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Motion blur, which is very common in daily life, will greatly affect the observation and analysis of images, not only reducing the quality of images but also hindering other research work in the field of images. Motion blur usually comes from the relative displacement between the target object and the camera during the exposure process. Coarse-to-Fine strategy has been widely used in various image...Show More

Motion Deblurring by Fusing Multi-Stage Image Features

Shihua Zhang;Fan He;Xun Shao

2024 IEEE Cyber Science and Technology Congress (CyberSciTech)

Year: 2024 | Conference Paper |

Multi-dimensional Visual Fusion: Open-Pit Mine Safety Video Summarization Approach Based on Attention Mechanism and Dynamic Feature Fusion

Zhiping Yi;Qi Wang;Ziji Ma;Xun Shao

2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

Year: 2024 | Conference Paper |

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Video summarization technology is crucial for video surveillance applications, particularly in open-pit mine security and disaster relief contexts. This study proposes a novel video summarization model architecture that enhances and integrates multi-dimensional visual features. The model utilizes multi-dimensional visual features to predict importance scores. This method plays a crucial role in re...Show More

Multi-dimensional Visual Fusion: Open-Pit Mine Safety Video Summarization Approach Based on Attention Mechanism and Dynamic Feature Fusion

Zhiping Yi;Qi Wang;Ziji Ma;Xun Shao

2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

Year: 2024 | Conference Paper |

Adaptive load balancing Strategy for VR Microservices with Edge AI

Liang Liu;Yuxiang Chen;Xun Shao;Kailin Wang;Yu Huang

2024 7th International Conference on Electronics Technology (ICET)

Year: 2024 | Conference Paper |

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The pervasiveness of information and communications technologies, such as 5 G and edge AI, has led to the booming growth of video applications, particularly in the realm of virtual reality (VR). However, due to the random distribution of the video traffic and the fluctuation of the computing power in Mobile Edge Computing (MEC) networks, traditional load balancing strategies lead to high video ser...Show More

Adaptive load balancing Strategy for VR Microservices with Edge AI

Liang Liu;Yuxiang Chen;Xun Shao;Kailin Wang;Yu Huang

2024 7th International Conference on Electronics Technology (ICET)

Year: 2024 | Conference Paper |

Leveraging CAVs to Improve Traffic Efficiency: An MARL-Based Approach

Weizhen Han;Enshu Wang;Bingyi Liu;Zhi Liu;Xun Shao;Libing Wu;Jianping Wang

2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)

Year: 2024 | Conference Paper |

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With the capability of intelligent control and communicating with surrounding vehicles and infrastructures, connected and automated vehicles (CAVs) can drive cooperatively and have more positive effects on traffic efficiency. Cooperative and real-time path planning for CAVs stands as a pivotal solution to mitigate traffic congestion and augment travel efficiency. However, most of the existing path...Show More

Leveraging CAVs to Improve Traffic Efficiency: An MARL-Based Approach

Weizhen Han;Enshu Wang;Bingyi Liu;Zhi Liu;Xun Shao;Libing Wu;Jianping Wang

2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)

Year: 2024 | Conference Paper |

Efficient Online Resource Management in MEC: A Chance-Constrained Approach for Handling Uncertain Demands

Kailin Wang;Xun Shao;Heli Zhang;Kaoru Ota;Xi Li;Mianxiong Dong

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 11 | Journal Article |

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With the proliferation of resource-hungry applications, a common paradigm to quickly respond to the ever-increasing demands of tasks is mobile edge computing (MEC), which deploys resources on the premise of grasping task information (including time and quantity of task release, etc). Actually, task generation is dynamic in many practical scenarios, and it is unrealistic for MEC servers to acquire ...Show More

Efficient Online Resource Management in MEC: A Chance-Constrained Approach for Handling Uncertain Demands

Kailin Wang;Xun Shao;Heli Zhang;Kaoru Ota;Xi Li;Mianxiong Dong

IEEE Transactions on Vehicular Technology

Year: 2024 | Volume: 73, Issue: 11 | Journal Article |

SCcheck: A Novel Graph-Driven and Attention- Enabled Smart Contract Vulnerability Detection Framework for Web 3.0 Ecosystem

Yuanlong Cao;Fan Jiang;Jianmao Xiao;Shaolong Chen;Xun Shao;Celimuge Wu

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 5 | Journal Article |

Cited by: Papers (2)

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With the rapid progress of technology, Web 3.0 has emerged as a transformative force in the digital realm. It is characterized by decentralization, user-centric data ownership, and the implementation of cryptographic techniques. Smart contracts, as a core component of Web 3.0, play a pivotal role in driving its evolution by enabling novel functionalities and various application. However, given the...Show More

SCcheck: A Novel Graph-Driven and Attention- Enabled Smart Contract Vulnerability Detection Framework for Web 3.0 Ecosystem

Yuanlong Cao;Fan Jiang;Jianmao Xiao;Shaolong Chen;Xun Shao;Celimuge Wu

IEEE Transactions on Network Science and Engineering

Year: 2024 | Volume: 11, Issue: 5 | Journal Article |

A Fast Recommendation Algorithm for Data Streams Based on Differential Privacy

Chang Guo;Xiujun Wang;Xiao Zheng;Xun Shao;Lei Mo;Gaoming Yang

2023 Eleventh International Conference on Advanced Cloud and Big Data (CBD)

Year: 2023 | Conference Paper |

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Recommendation systems are an essential tools for people to access interesting information from vast amounts of data and have a wide range of applications. However, current recommendation algorithms encounter persistent challenges, including significant time overhead and limitations in safeguarding user privacy effectively. These challenges stem from their reliance on static clustering algorithms ...Show More

A Fast Recommendation Algorithm for Data Streams Based on Differential Privacy

Chang Guo;Xiujun Wang;Xiao Zheng;Xun Shao;Lei Mo;Gaoming Yang

2023 Eleventh International Conference on Advanced Cloud and Big Data (CBD)

Year: 2023 | Conference Paper |

Game Theoretic Resource Allocation for Information Freshness in Mobile Edge Computing

Jingjing Gu;Di Zhang;Hongcheng Bao;Weiwei Xing;Xindong Zheng;Xun Shao

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2023 | Conference Paper |

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Age of information (AoI) is an important metric used to quantify the freshness of data. By utilizing resources of the edge server near the source nodes, mobile edge computing (MEC) can speed up the processing of information updates and ensure data freshness. However, the resources of the edge server are usually limited, so it is necessary to study the resource allocation strategy to ensure data fr...Show More

Game Theoretic Resource Allocation for Information Freshness in Mobile Edge Computing

Jingjing Gu;Di Zhang;Hongcheng Bao;Weiwei Xing;Xindong Zheng;Xun Shao

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2023 | Conference Paper |

Distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning

Liwei Peng;Fan He;Go Hasegawa;Yanfen Cheng;Xun Shao

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2023 | Conference Paper |

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In recent years, there’s been a surge in the popularity and affordability of distributed power generation equipment, such as photovoltaic systems (PV) and energy storage systems. At present, however, most solutions target individual user’s energy management. Given the varied energy consumption habits, networking neighboring users and managing energy as a unified system could boost efficiency. Yet,...Show More

Distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning

Liwei Peng;Fan He;Go Hasegawa;Yanfen Cheng;Xun Shao

2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2023 | Conference Paper |

DRLFcc: Deep Reinforcement Learning-empowered Congestion Control Mechanism for TCP Fast Recovery in High Loss Wireless Networks

Yuanlong Cao;Jinquan Nie;Yuehua Fan;Xun Shao;Gang Lei

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

Year: 2023 | Conference Paper |

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TCP is currently the most widely used Internet transmission protocol, which is extensively applied to applications on the Internet to enable reliable data transmission. The TCP congestion control algorithm has a significant performance impact on all applications that use the TCP. However, traditional TCP congestion control algorithms rely on fixed feedback mechanisms, which can be challenging to a...Show More

DRLFcc: Deep Reinforcement Learning-empowered Congestion Control Mechanism for TCP Fast Recovery in High Loss Wireless Networks

Yuanlong Cao;Jinquan Nie;Yuehua Fan;Xun Shao;Gang Lei

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

Year: 2023 | Conference Paper |

Optimal Energy Scheduling for Communities in Smart Microgrids with Multi-Agent Reinforcement Learning

Hongyang Yang;Xun Shao;Go Hasegawa;Hiroshi Masui

2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

Year: 2023 | Conference Paper |

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Energy management in buildings is becoming increasingly important with the proposed carbon neutrality target. Home energy management systems(HEMS) can reduce electricity costs and carbon emissions by scheduling the energy consumption of household loads and using solar power to generate electricity. Cooperation between multiple HEMS can more effectively reduce electricity costs under the peak load ...Show More

Optimal Energy Scheduling for Communities in Smart Microgrids with Multi-Agent Reinforcement Learning

Hongyang Yang;Xun Shao;Go Hasegawa;Hiroshi Masui

2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

Year: 2023 | Conference Paper |

CSFRL: A Reinforcement Learning Technology Enabled Computing Power Scheduling Framework Based on Kubernetes

Wenliang Cheng;Yueqiang Xu;Quansheng Xu;Heli Zhang;Xi Li;Xun Shao

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

Year: 2023 | Conference Paper |

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This paper presents a computing power scheduling framework based on reinforcement learning (CSFRL) for custom fine-grained resource scheduling on Kubernetes. With the rise of edge computing networks, efficiently adapting computing resources is essential to support various services. While Kubernetes is widely used for container orchestration, few studies have implemented fine-grained resource sched...Show More

CSFRL: A Reinforcement Learning Technology Enabled Computing Power Scheduling Framework Based on Kubernetes

Wenliang Cheng;Yueqiang Xu;Quansheng Xu;Heli Zhang;Xi Li;Xun Shao

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

Year: 2023 | Conference Paper |

A Truthful Auction for Green Continuous Task Allocation and Pricing in Edge Computing

Yuru Liu;Di Zhang;Xun Shao;Keping Yu;Shahid Mumtaz

ICC 2023 - IEEE International Conference on Communications

Year: 2023 | Conference Paper |

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With the advent of edge computing, more and more tasks are offloaded to edge servers, but the computing and storage capabilities of edge servers are limited. Although some works propose efficient schemes for task allocation and pricing, they may ignore users' preferences for continuous tasks. However, the combinatorial preference causes high computational complexity. In this paper, we propose a do...Show More

A Truthful Auction for Green Continuous Task Allocation and Pricing in Edge Computing

Yuru Liu;Di Zhang;Xun Shao;Keping Yu;Shahid Mumtaz

ICC 2023 - IEEE International Conference on Communications

Year: 2023 | Conference Paper |

A Vehicle Density Prediction Based Routing Protocol for Green VANET Powered by VFC

Bingyi Liu;Yang Lu;Weizhen Han;Xun Shao;Weiyang Chen

ICC 2023 - IEEE International Conference on Communications

Year: 2023 | Conference Paper |

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In Green Vehicular Ad-hoc Networks (VANET), how to establish and maintain stable routes to ensure fast and efficient transmission of messages is quite an important and demanding task because of the intrinsic characteristics of VANET, such as uneven distribution and high mobility of vehicles. In this paper, we propose a novel vehicle density prediction-based routing protocol assisted by Green Vehic...Show More

A Vehicle Density Prediction Based Routing Protocol for Green VANET Powered by VFC

Bingyi Liu;Yang Lu;Weizhen Han;Xun Shao;Weiyang Chen

ICC 2023 - IEEE International Conference on Communications

Year: 2023 | Conference Paper |

Temporal Segment Neural Networks-Enabled Dynamic Hand-Gesture Recognition for Industrial Cyber-Physical Authentication Systems

Yuanlong Cao;Junjie Li;Chinmay Chakraborty;Le Qin;Lei Tao;Xun Shao

IEEE Systems Journal

Year: 2023 | Volume: 17, Issue: 4 | Journal Article |

Cited by: Papers (4)

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In recent years, the data security and privacyprotection of human cyber-physical systems (CPSs) in the industrial Internet of Things (IoT) have attracted much research interest. In the meantime, applying human–machine identity authentication mechanism to the CPSs has been considered as a promising solution for secure and privacy-enhancing authentication in industrial IoT. In view of the applicatio...Show More

Temporal Segment Neural Networks-Enabled Dynamic Hand-Gesture Recognition for Industrial Cyber-Physical Authentication Systems

Yuanlong Cao;Junjie Li;Chinmay Chakraborty;Le Qin;Lei Tao;Xun Shao

IEEE Systems Journal

Year: 2023 | Volume: 17, Issue: 4 | Journal Article |

Edge-edge Collaboration Based Micro-service Deployment in Edge Computing Networks

Junjie Qi;Heli Zhang;Xi Li;Hong Ji;Xun Shao

2023 IEEE Wireless Communications and Networking Conference (WCNC)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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With the sixth generation (6G) proposal, collaboration at the edge of the Internet of Things (IoT) has been widely studied to coordinate limited edge resources. Kubernetes has emerged as a promising solution for flexible and efficient resource scheduling. However, the default scheduler of Kubernetes only allocates pods separately according to the resource utilization condition of the cluster, whic...Show More

Edge-edge Collaboration Based Micro-service Deployment in Edge Computing Networks

Junjie Qi;Heli Zhang;Xi Li;Hong Ji;Xun Shao

2023 IEEE Wireless Communications and Networking Conference (WCNC)

Year: 2023 | Conference Paper |

Collaborative Intelligence Enabled Routing in Green IoV: A Grid and Vehicle Density Prediction-Based Protocol

Bingyi Liu;Yang Sheng;Xun Shao;Yusheng Ji;Weizhen Han;Enshu Wang;Shengwu Xiong

IEEE Transactions on Green Communications and Networking

Year: 2023 | Volume: 7, Issue: 2 | Journal Article |

Cited by: Papers (13)

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Green Internet of Vehicles (IoV) is a newly-emerged research area which focuses on reducing networking overhead and improving communication efficiency in future vehicular cyber-physical systems (VCPS). A critical requirement to realize such tasks in Green IoV is the achievement of effective routing protocols for data dissemination. However, maintaining end-to-end communication and reducing the com...Show More

Collaborative Intelligence Enabled Routing in Green IoV: A Grid and Vehicle Density Prediction-Based Protocol

Bingyi Liu;Yang Sheng;Xun Shao;Yusheng Ji;Weizhen Han;Enshu Wang;Shengwu Xiong

IEEE Transactions on Green Communications and Networking

Year: 2023 | Volume: 7, Issue: 2 | Journal Article |

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