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A Prediction-Enhanced Physical-to-Virtual Twin Connectivity Framework for Human Digital Twin
  • +2
  • Samuel Okegbile ,
  • Jun Cai,
  • Junjie Wu,
  • Jiayuan Chen,
  • Changyan Yi
Samuel Okegbile

Corresponding Author:[email protected]

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Jun Cai
Junjie Wu
Jiayuan Chen
Changyan Yi


This paper proposes a new secure and privacy-preserving prediction-enhanced solution for reliable physical-to-virtual communications in human digital twin (HDT) systems. With such a prediction-enhanced connectivity (PeHDT) framework, the evolution of any virtual twin (VT) could be triggered in real-time or in advance using the expected state of its physical counterpart. This ensures the continuous maintenance of a true replica of each physical twin (PT), thus relieving the need for timely PT-VT synchronization while the VT-experienced delay is reduced to zero or close to zero. We adopted a secured federated multi-task learning technique to meet the security and privacy constraints of HDT and employed a single server discrete-time batch-service queue framework when characterizing the batching process to reduce the communication burden. Furthermore, we introduced a prediction verification framework to improve the performance of the proposed PeHDT framework. The resulting problem was formulated as a constrained Markov decision process and was solved by introducing a primary-dual deep deterministic policy gradient (DDPG) algorithm. Through a joint investigation of communication, batching and prediction verification schemes, the simulation results show that the proposed PeHDT framework can greatly reduce both the VT-experienced delay and the PT-VT communication time without compromising the specific requirements of HDT.
14 Dec 2023Submitted to TechRxiv
22 Dec 2023Published in TechRxiv