Terence Jie Chua - IEEE Xplore Author Profile
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Terence Jie Chua

Publications
13
Citations
49
Publications by Year
20222025
Co-Authors:
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Terence Jie Chua

Affiliation

Graduate College
Nanyang Technological University (NTU)
Jurong West, Singapore

Publication Topics

Biography

Terence Jie Chua received the B.S. degree from Nanyang Technological University (NTU), Singapore, where he is currently pursuing the Ph.D. degree with the School of Computer Science and Engineering. His research interests include wireless communications, adversarial machine learning, deep reinforcement learning, optimization, and metaverse.(Based on document published on 30 May 2023).
Publications
13
Citations
49
Publications by Year
20222025
Co-Authors:
Author's Published Works

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Play to Earn in Augmented Reality With Mobile Edge Computing Over Wireless Networks: A Deep Reinforcement Learning Approach

Terence Jie Chua;Wenhan Yu;Jun Zhao

IEEE Transactions on Wireless Communications
Year: 2025 | Volume: 24, Issue: 1 | Journal Article |
Play-to-earn (P2E) games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in augmented reality (AR) technologies, AR play-to-earn games become compute-intensive. In-game graphical scenes need to be offloaded from mobile devices to an edge server for computation. In this work, we consider an optimization ...Show More

Play to Earn in Augmented Reality With Mobile Edge Computing Over Wireless Networks: A Deep Reinforcement Learning Approach

Terence Jie Chua;Wenhan Yu;Jun Zhao

Year: 2025 | Volume: 24, Issue: 1 | Journal Article |

Orchestration of Emulator Assisted 6G Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach

Wenhan Yu;Terence Jie Chua;Jun Zhao

2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
Year: 2024 | Conference Paper |
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm inte-grating 6G Mobile Edge Computing (MEC) with foundation models, specifically designed to enhance local task performance on user equipment (UE). Central to our approach is the innovative Emulator-Adapter architecture, segmenting t...Show More

Orchestration of Emulator Assisted 6G Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach

Wenhan Yu;Terence Jie Chua;Jun Zhao

Year: 2024 | Conference Paper |

Counterfactual Reward Estimation for Credit Assignment in Multi-agent Deep Reinforcement Learning over Wireless Video Transmission

Y. Wenhan;Liangxin Qian;Terence Jie Chua;Jun Zhao

2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)
Year: 2024 | Conference Paper |
This study investigates frame-wise optimization in Mobile Edge Computing (MEC) for video transmission, emphasizing dynamic adaptation to diverse frame complexities and efficient resource utilization. The comprehensive system model captures the complexities of joint optimizations in MEC for real-time video transmission, addressing challenges associated with error concealment techniques, and enhanci...Show More

Counterfactual Reward Estimation for Credit Assignment in Multi-agent Deep Reinforcement Learning over Wireless Video Transmission

Y. Wenhan;Liangxin Qian;Terence Jie Chua;Jun Zhao

Year: 2024 | Conference Paper |

Optimization for the Metaverse over Mobile Edge Computing with Play to Earn

Chang Liu;Terence Jie Chua;Jun Zhao

IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
Year: 2024 | Conference Paper |
The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spurred the popularity of play-to-earn (P2E) games, where players are entitled to earn and own digital assets which they may trade for physical-world currencies. However, these computationally-intensive games are hardly pla...Show More

Optimization for the Metaverse over Mobile Edge Computing with Play to Earn

Chang Liu;Terence Jie Chua;Jun Zhao

Year: 2024 | Conference Paper |

Virtual Reality in Metaverse Over Wireless Networks with User-Centered Deep Reinforcement Learning

Wenhan Yu;Terence Jie Chua;Jun Zhao

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and interactive socialization. Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, ...Show More

Virtual Reality in Metaverse Over Wireless Networks with User-Centered Deep Reinforcement Learning

Wenhan Yu;Terence Jie Chua;Jun Zhao

Year: 2023 | Conference Paper |

Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning

Terence Jie Chua;Wenhan Yu;Jun Zhao

ICC 2023 - IEEE International Conference on Communications
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs to develop a...Show More

Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning

Terence Jie Chua;Wenhan Yu;Jun Zhao

Year: 2023 | Conference Paper |

Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Wenhan Yu;Terence Jie Chua;Jun Zhao

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)
Year: 2023 | Conference Paper |
The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactive and immersive socialization experience between people is one of the promises of the Metaverse. In spite of the rapid advancements in current technologies, the computation required for a smooth, seamless and immersive s...Show More

Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Wenhan Yu;Terence Jie Chua;Jun Zhao

Year: 2023 | Conference Paper |

Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse: A Deep Reinforcement Learning Approach

Terence Jie Chua;Wenhan Yu;Jun Zhao

IEEE Transactions on Wireless Communications
Year: 2023 | Early Access Article |
Cited by: Papers (1)
Digital Twinning of physical world scenes onto the Metaverse is necessary for augmented reality (AR)-assisted driving. In AR-assisted driving, physical environment scenes are first captured by AR vehicles and are uploaded to the Metaverse for the construction of the Metaverse Map. However, the development of AR-assisted driving applications invites adversaries. These attackers may place adversaria...Show More

Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse: A Deep Reinforcement Learning Approach

Terence Jie Chua;Wenhan Yu;Jun Zhao

Year: 2023 | Early Access Article |

Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse Over Wireless Communications

Wenhan Yu;Terence Jie Chua;Jun Zhao

IEEE Journal on Selected Areas in Communications
Year: 2023 | Volume: 41, Issue: 7 | Journal Article |
Cited by: Papers (14)
Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual objects is computationally intensive and requires computation offl...Show More

Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse Over Wireless Communications

Wenhan Yu;Terence Jie Chua;Jun Zhao

Year: 2023 | Volume: 41, Issue: 7 | Journal Article |

User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

Wenhan Yu;Terence Jie Chua;Jun Zhao

IEEE Transactions on Wireless Communications
Year: 2023 | Early Access Article |
Cited by: Papers (12)
The Metaverse emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the Metaverse to offer a highly immersive user experience. As mobility is emphasized in the Metaverse context, VR devices reduce their weights at the sacrifice of local computation abilities. In this paper, for a system consisti...Show More

User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

Wenhan Yu;Terence Jie Chua;Jun Zhao

Year: 2023 | Early Access Article |

Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Terence Jie Chua;Wenhan Yu;Jun Zhao

2022 IEEE 8th World Forum on Internet of Things (WF-IoT)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
Improving the interactivity and interconnectivity between people is one of the highlights of the Metaverse. The Metaverse relies on a core approach, digital twinning, which is a means to replicate physical world objects, people, actions and scenes onto the virtual world. Being able to access scenes and information associated with the physical world, in the Metaverse in real-time and under mobility...Show More

Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach

Terence Jie Chua;Wenhan Yu;Jun Zhao

Year: 2022 | Conference Paper |

Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer

Terence Jie Chua;Wenhan Yu;Chang Liu;Jun Zhao

2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
Year: 2022 | Conference Paper |
Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-processing patch-avoidance or patch-reconstruction. We quantify our prediction un...Show More

Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer

Terence Jie Chua;Wenhan Yu;Chang Liu;Jun Zhao

Year: 2022 | Conference Paper |

Time Minimization in Hierarchical Federated Learning

Chang Liu;Terence Jie Chua;Jun Zhao

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchic...Show More

Time Minimization in Hierarchical Federated Learning

Chang Liu;Terence Jie Chua;Jun Zhao

Year: 2022 | Conference Paper |

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