Talabi, Oluwasegun (2024) Multi-Encoder Semantic Communication for Human Digital Twin Synchronization. Masters thesis, Concordia University.
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Abstract
Human Digital Twin (HDT) concept introduces an innovative framework for creating digital
counterparts of individuals, enabling real-time synchronization between the physical twin (PT) and
the virtual twin (VT). This PT-VT synchronization underpins various human-centered services but
demands significant data processing and efficient communication resource allocation, particularly
in resource-constrained environments. Semantic communication has emerged as a promising al-
ternative to traditional data-driven methods; however, single-encoder models struggle to meet the
diverse and dynamic requirements of HDT applications.
To address these challenges, this thesis presents a multi-encoder semantic communication frame-
work that adaptively allocates resources—such as bandwidth, computational power, and processing
frequency—based on application-specific needs. The short-term optimization problem is formulated
as a mixed-integer nonlinear programming (MINLP) problem and solved using a genetic algorithm
(GA). Simulation results demonstrate that the proposed multi-encoder model significantly improves
synchronization quality and power efficiency, outperforming traditional single-encoder models in
terms of accuracy, latency, and resource utilization.
To achieve a balance between immediate performance and long-term objectives (e.g., queue sta-
bility, sustainable energy usage, and high throughput), the thesis explores the long-term optimiza-
tion problem, formulated as a Markov Decision Process (MDP) and solved using Lyapunov-assisted
deep reinforcement learning. This adaptable and scalable approach positions the multi-encoder se-
mantic communication model as a highly efficient solution for future HDT applications.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
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Item Type: | Thesis (Masters) |
Authors: | Talabi, Oluwasegun |
Institution: | Concordia University |
Degree Name: | M.A. Sc. |
Program: | Electrical and Computer Engineering |
Date: | December 2024 |
Thesis Supervisor(s): | Cai, Jun |
Keywords: | Human Digital Twin Multi-Encoder Semantic Communication Mixed Integer Non-linear Problem Genetic Algorithm Multistage Stochastic Optimization Problem Lyapunov Assisted Deep Reinforcement Learning |
ID Code: | 994928 |
Deposited By: | Oluwasegun Talabi |
Deposited On: | 17 Jun 2025 17:29 |
Last Modified: | 17 Jun 2025 17:29 |
References:
[1] J. Chen, C. Yi, S. D. Okegbile, J. Cai, and X. S. Shen, “Networking architecture and keysupporting technologies for human digital twin in personalized healthcare: a comprehensive
survey,” IEEE Communications Surveys & Tutorials, 2023.
[2] S. D. Okegbile, J. Cai, D. Niyato, and C. Yi, “Human digital twin for personalized healthcare:
Vision, architecture and future directions,” IEEE network, vol. 37, no. 2, pp. 262–269, 2022.
[3] J. Chen, C. Yi, H. Du, D. Niyato, J. Kang, J. Cai, and X. Shen, “A revolution of personalized
healthcare: Enabling human digital twin with mobile aigc,” IEEE Network, 2024.
[4] S. D. Okegbile, J. Cai, H. Zheng, J. Chen, and C. Yi, “Differentially private federated multitask
learning framework for enhancing human-to-virtual connectivity in human digital twin,”
IEEE Journal on Selected Areas in Communications, 2023.
[5] H. X. Nguyen, R. Trestian, D. To, and M. Tatipamula, “Digital twin for 5g and beyond,” IEEE
Communications Magazine, vol. 59, no. 2, pp. 10–15, 2021.
[6] C. K. Thomas, W. Saad, and Y. Xiao, “Causal semantic communication for digital twins: A
generalizable imitation learning approach,” IEEE Journal on Selected Areas in Information
Theory, 2023.
[7] J. Moyne, Y. Qamsane, E. C. Balta, I. Kovalenko, J. Faris, K. Barton, and D. M. Tilbury, “A
requirements driven digital twin framework: Specification and opportunities,” Ieee Access,
vol. 8, pp. 107781–107801, 2020.
[8] H. Xie and Z. Qin, “A lite distributed semantic communication system for internet of things,”
IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 142–153, 2020.
[9] S. Okegbile, H. Gao, O. Talabi, J. Cai, C. Yi, D. Niyato, and X. S. Shen, “Fles: A federated
learning-enhanced semantic communication framework for mobile aigc-driven human digital
twins,” Authorea Preprints, 2024.
[10] J. Tang, J. Nie, J. Bai, J. Xu, S. Li, Y. Zhang, and Y. Yuan, “Uav-assisted digital twin synchronization
with tiny machine learning-based semantic communications,” IEEE Internet of
Things Journal, 2024.
[11] D. Gelernter, Mirror worlds: Or the day software puts the universe in a shoebox... How it will
happen and what it will mean. Oxford University Press, 1993.
[12] M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior
in complex systems,” Transdisciplinary perspectives on complex systems: New findings
and approaches, pp. 85–113, 2017.
[13] M. Singh, E. Fuenmayor, E. P. Hinchy, Y. Qiao, N. Murray, and D. Devine, “Digital twin:
Origin to future,” Applied System Innovation, vol. 4, no. 2, p. 36, 2021.
[14] F. Tao, H. Zhang, A. Liu, and A. Y. Nee, “Digital twin in industry: State-of-the-art,” IEEE
Transactions on industrial informatics, vol. 15, no. 4, pp. 2405–2415, 2018.
[15] W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital twin in manufacturing: A
categorical literature review and classification,” Ifac-PapersOnline, vol. 51, no. 11, pp. 1016–
1022, 2018.
[16] C. Cimino, E. Negri, and L. Fumagalli, “Review of digital twin applications in manufacturing,”
Computers in industry, vol. 113, p. 103130, 2019.
[17] L. Bao, Q. Wang, and Y. Jiang, “Review of digital twin for intelligent transportation system,”
in 2021 International Conference on Information Control, Electrical Engineering and Rail
Transit (ICEERT), pp. 309–315, IEEE, 2021.
[18] M. S. Irfan, S. Dasgupta, and M. Rahman, “Towards transportation digital twin systems for
traffic safety and mobility: A review,” IEEE Internet of Things Journal, 2024.
[19] T. Ruohom¨aki, E. Airaksinen, P. Huuska, O. Kes¨aniemi, M. Martikka, and J. Suomisto, “Smart
city platform enabling digital twin,” in 2018 International Conference on Intelligent Systems
(IS), pp. 155–161, IEEE, 2018.
[20] M. Jafari, A. Kavousi-Fard, T. Chen, and M. Karimi, “A review on digital twin technology in
smart grid, transportation system and smart city: Challenges and future,” IEEE Access, vol. 11,
pp. 17471–17484, 2023.
[21] M. E. Miller and E. Spatz, “A unified view of a human digital twin,” Human-Intelligent Systems
Integration, vol. 4, no. 1, pp. 23–33, 2022.
[22] B. R. Barricelli, E. Casiraghi, J. Gliozzo, A. Petrini, and S. Valtolina, “Human digital twin for
fitness management,” Ieee Access, vol. 8, pp. 26637–26664, 2020.
[23] Q. Wang, W. Jiao, P. Wang, and Y. Zhang, “Digital twin for human-robot interactive welding
and welder behavior analysis,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 2, pp. 334–
343, 2020.
[24] I. Graessler and A. P¨ohler, “Integration of a digital twin as human representation in a scheduling
procedure of a cyber-physical production system,” in 2017 IEEE international conference
on industrial engineering and engineering management (IEEM), pp. 289–293, IEEE, 2017.
[25] Y. Liu, L. Zhang, Y. Yang, L. Zhou, L. Ren, F. Wang, R. Liu, Z. Pang, and M. J. Deen, “A
novel cloud-based framework for the elderly healthcare services using digital twin,” IEEE
access, vol. 7, pp. 49088–49101, 2019.
[26] H. Laaki, Y. Miche, and K. Tammi, “Prototyping a digital twin for real time remote control
over mobile networks: Application of remote surgery,” Ieee Access, vol. 7, pp. 20325–20336,
2019.
[27] J. Zhang, L. Li, G. Lin, D. Fang, Y. Tai, and J. Huang, “Cyber resilience in healthcare digital
twin on lung cancer,” IEEE access, vol. 8, pp. 201900–201913, 2020.
[28] Z. Hu, S. Lou, Y. Xing, X.Wang, D. Cao, and C. Lv, “Review and perspectives on driver digital
twin and its enabling technologies for intelligent vehicles,” IEEE Transactions on Intelligent
Vehicles, vol. 7, no. 3, pp. 417–440, 2022.
[29] R. Martinez-Velazquez, R. Gamez, and A. El Saddik, “Cardio twin: A digital twin of the
human heart running on the edge,” in 2019 IEEE international symposium on medical measurements
and applications (MeMeA), pp. 1–6, IEEE, 2019.
[30] H. Elayan, M. Aloqaily, and M. Guizani, “Digital twin for intelligent context-aware iot healthcare
systems,” IEEE Internet of Things Journal, vol. 8, no. 23, pp. 16749–16757, 2021.
[31] K. Amara, O. Kerdjidj, and N. Ramzan, “Emotion recognition for affective human digital twin
by means of virtual reality enabling technologies,” IEEE Access, vol. 11, pp. 74216–74227,
2023.
[32] S. D. Okegbile and J. Cai, “Edge-assisted human-to-virtual twin connectivity scheme
for human digital twin frameworks,” in 2022 IEEE 95th Vehicular Technology
Conference:(VTC2022-Spring), pp. 1–6, IEEE, 2022.
[33] X. Li, B. He, Z. Wang, Y. Zhou, G. Li, and R. Jiang, “Semantic-enhanced digital twin system
for robot–environment interaction monitoring,” IEEE Transactions on Instrumentation and
Measurement, vol. 70, pp. 1–13, 2021.
[34] B. Du, H. Du, H. Liu, D. Niyato, P. Xin, J. Yu, M. Qi, and Y. Tang, “Yolo-based semantic
communication with generative ai-aided resource allocation for digital twins construction,”
IEEE Internet of Things Journal, 2023.
[35] Z. Ji and Z. Qin, “Computational offloading in semantic-aware cloud-edge-end collaborative
networks,” arXiv preprint arXiv:2402.18183, 2024.
[36] L. Yan, Z. Qin, R. Zhang, Y. Li, and G. Y. Li, “Resource allocation for text semantic communications,”
IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1394–1398, 2022.
[37] L. Yan, Z. Qin, R. Zhang, Y. Li, and G. Ye Li, “Qoe-aware resource allocation for semantic
communication networks,” in GLOBECOM 2022 - 2022 IEEE Global Communications
Conference, pp. 3272–3277, 2022.
[38] A. Yekanlou, A. I. Salameh, and J. Cai, “Buffer-state aware task offloading in edge networks
with task splitting for iov,” in 2023 Biennial Symposium on Communications (BSC), pp. 13–18,
IEEE, 2023.
[39] Y. Dai, K. Zhang, S. Maharjan, and Y. Zhang, “Deep reinforcement learning for stochastic
computation offloading in digital twin networks,” IEEE Transactions on Industrial Informatics,
vol. 17, no. 7, pp. 4968–4977, 2020.
[40] S. Bi, L. Huang, H.Wang, and Y.-J. A. Zhang, “Lyapunov-guided deep reinforcement learning
for stable online computation offloading in mobile-edge computing networks,” IEEE Transactions
on Wireless Communications, vol. 20, no. 11, pp. 7519–7537, 2021.
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