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Xianghui Li - IEEE Xplore Author Profile
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Xianghui Li

Publications
2
Citations
6
Publications by Year
20212023
Co-Authors:
Show All Co-Authors (6)
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Xianghui Li

Affiliation

School of Information and Communication Engineering
Hainan University
Haikou, China

Publication Topics

Biography

Xianghui Li received the B.S. degree in 2022 from Hainan University, Haikou, China, where he is currently working toward the master’s degree with Tianjin University, Tianjin, China. His research interests include deep learning and wireless communications.(Based on document published on 2 May 2023).
Publications
2
Citations
6
Publications by Year
20212023
Co-Authors:
Show All Co-Authors (6)
Author's Published Works

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A Method of Flow Compensation Incentive based on Q-Learning for User Privacy Protection

Lu Chen;Degan Zhang;Jie Zhang;Ting Zhang;Hongrui Fan;Jinyu Du

2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
To solve the incentive problem for MCS (Mobile Crowdsensing) users based on privacy protection, we proposed an incentive method of flow compensation for the privacy protection of users and designed a system model which combined MCS with MEC (Mobile Edge Computing). The EC (Edge Center) uploaded the perception results to the MCS diminishing MCS’s overhead. We also designed an incentive model based ...Show More

A Method of Flow Compensation Incentive based on Q-Learning for User Privacy Protection

Lu Chen;Degan Zhang;Jie Zhang;Ting Zhang;Hongrui Fan;Jinyu Du

Year: 2021 | Conference Paper |

Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy

Yuya Cui;Degan Zhang;Jie Zhang;Ting Zhang;Lixiang Cao;Lu Chen

2021 IEEE 46th Conference on Local Computer Networks (LCN)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Mobile management is a challenging technology in Mobile Edge Computing (MEC). When the device is moving, computation tasks need to be dynamically migrated between multiple edge servers to maintain service continuity. This paper proposes a migration optimization of distributed task in MEC by deep reinforcement learning solution to optimize delay. In Multi-agent Deep Reinforcement Learning (MADRL), ...Show More

Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy

Yuya Cui;Degan Zhang;Jie Zhang;Ting Zhang;Lixiang Cao;Lu Chen

Year: 2021 | Conference Paper |

An Incentive Approach in Mobile Crowdsensing for Perceptual User

Lu Chen;Degan Zhang;Jie Zhang;Ting Zhang;Jinyu Du;Hongrui Fan

2021 IEEE 46th Conference on Local Computer Networks (LCN)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The privacy protection of perceptual user and their enthusiasm improvement for participating in perceptual tasks are two important problems in MCS (Mobile Crowdsensing) network. A mechanism of local differential privacy protection of attribute correlation can generate perceptual results with higher precision of attribute correlation and protect perceptual users’ privacy data. A flow compensation i...Show More

An Incentive Approach in Mobile Crowdsensing for Perceptual User

Lu Chen;Degan Zhang;Jie Zhang;Ting Zhang;Jinyu Du;Hongrui Fan

Year: 2021 | Conference Paper |

A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing

De-Gan Zhang;Lu Chen;Jie Zhang;Jie Chen;Ting Zhang;Ya-Meng Tang;Jian-Ning Qiu

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (94)
During mobile edge computing, due to the movement of nodes and the exhaustion of node energy, link failure occurs thus reducing the network lifetime in the mobile ad-hoc network. When the route fails, because the single-path protocols need to restart the route discovery process, the delay of the network is greatly increased. Therefore, the multi-path routing protocol is proposed, saving the cost o...Show More

A Multi-Path Routing Protocol Based on Link Lifetime and Energy Consumption Prediction for Mobile Edge Computing

De-Gan Zhang;Lu Chen;Jie Zhang;Jie Chen;Ting Zhang;Ya-Meng Tang;Jian-Ning Qiu

Year: 2020 | Volume: 8 | Journal Article |

A New Method of Mobile Ad Hoc Network Routing Based on Improved GF Strategy

Ke Li;De-gan Zhang;Jian-ning Qiu;Lu Chen

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
We propose a new mobile ad hoc network routing method based on improved GF (greedy forwarding) strategy in this paper. In the greedy forwarding phase, the reliable communication area is calculated, and then the quality of the link is evaluated according to the relative displacement between the nodes and the maintenance time of the link. Then, according to the quality of the link, the distance from...Show More

A New Method of Mobile Ad Hoc Network Routing Based on Improved GF Strategy

Ke Li;De-gan Zhang;Jian-ning Qiu;Lu Chen

Year: 2019 | Conference Paper |

A New Method of Mobile Ad Hoc Network Routing Based on Greed Forwarding Improvement Strategy

De-Gan Zhang;Peng-Zhen Zhao;Yu-ya Cui;Lu Chen;Ting Zhang;Hao Wu

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (79)
In resource-constrained mobile ad hoc network, geographical routing algorithms are relatively attractive routing algorithms. Due to the movement of nodes, energy exhaustion and the presence of obstacles, routing holes may occur. The existing routing algorithm only considers bypassing routing void through right-hand or left-hand criteria, and forwards data at the boundary of the hole. However, such...Show More

A New Method of Mobile Ad Hoc Network Routing Based on Greed Forwarding Improvement Strategy

De-Gan Zhang;Peng-Zhen Zhao;Yu-ya Cui;Lu Chen;Ting Zhang;Hao Wu

Year: 2019 | Volume: 7 | Journal Article |

A New Algorithm of the Best Path Selection Based on Machine Learning

Xiao-Huan Liu;De-Gan Zhang;Hao-Ran Yan;Yu-ya Cui;Lu Chen

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (90)
This paper proposes and designs a best path selection algorithm, which can solve the problem of path planning for intelligent driving vehicles in the case of restricted driving, traffic congestions and accidents. We tried to solve the problem under these emergency situations in path planing process for there's no driver in intelligent driving vehicle. We designed a new method of the best path sele...Show More

A New Algorithm of the Best Path Selection Based on Machine Learning

Xiao-Huan Liu;De-Gan Zhang;Hao-Ran Yan;Yu-ya Cui;Lu Chen

Year: 2019 | Volume: 7 | Journal Article |

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