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Deniz Unal - IEEE Xplore Author Profile

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Author details

Deniz Unal

Publications

9

Citations

38

Publications by Year

20202024

Co-Authors:

Stefano BasagniEmrecan DemirorsPatrick J. DonnellyKerem EnhosSara Falleni

Show All Co-Authors (8)

Deniz Unal

Affiliation

Hydronet Inc.

Burlington

MA, USA

Publication Topics

Bit Error Rate,
Orthogonal Frequency Division Multiplexing,
Bit Error,
Data Rate,
Underwater Communication,
Communication Links,
Communication Systems,
Doppler Shift,
Multiple-input Multiple-output,
Unmanned Underwater Vehicles,
Autonomous Surface Vehicles,
Carrier Frequency

Biography

Deniz Unal received the BS and MS degrees in electrical and electronics engineering from Bilkent University, Ankara, Turkey, in 2014 and 2018, respectively. He is currently pursuing the Ph.D. degree in electrical engineering with Institute for the Wireless Internet of Things, Northeastern University, where he is aResearch Assistant. His research interests include underwater acoustic communications and software-defined networking.(Based on document published on 9 January 2023).

Publications

9

Citations

38

Publications by Year

20202024

Co-Authors:

Stefano Basagni
Emrecan Demirors
Patrick J. Donnelly
Kerem Enhos
Sara Falleni

Show All Co-Authors (8)

Author's Published Works

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Showing 1-7 of 7 results

Conferences (4)

Journals (3)

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Lu Chen(7)
Ting Zhang(5)
Jie Zhang(4)
Degan Zhang(3)
De-Gan Zhang(3)
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China(3)
Tianjin Key Laboratory of Intelligent Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China(2)
Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China(2)
School of Sports Economics and Management, Tianjin University of Sport, Tianjin, China(2)
Tianjin Key Lab of Intelligent Computing and Novel Software Technology, Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin, China(2)
IEEE Access(3)
2021 IEEE 46th Conference on Local Computer Networks (LCN)(2)
2019 IEEE International Conference on Smart Internet of Things (SmartIoT)(1)
2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)(1)
IEEE(7)
Edmonton, AB, Canada(2)
Denver, CO, USA(1)
Tianjin, China(1)
Ad Hoc Networks(3)
Data Packets(3)
Energy Consumption(3)
Energy Of Nodes(3)
Mobile Edge Computing(3)

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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)

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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

2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)

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)

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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

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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)

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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

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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

Year: 2020 | Volume: 8 | Journal Article |

Cited by: Papers (94)

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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)

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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

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

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

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (79)

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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

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (90)

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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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