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A Review on Cyber-Twin in Sixth Generation Wireless Networks: Architecture, Research Challenges & Issues

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Abstract

The concept of Cyber-twin (CT) in Sixth-Generation (6G) wireless technology represents a transformative leap in the realm of telecommunication, integrating advanced Artificial intelligence and digital twin technologies to enhance network performance and user experience. Cyber-twin establishes a digital representation of the ends in virtual cyberspaces in the edge cloud and can deliver the functions of communication assist, behavior logger, and digital asset functions. Cyber-twins are used to provide product-supplementary services for a customer and thus expand the range of services. Operations are carried out in the cyber-twin to obtain insights about the behavior of the physical twin both currently as well as in the future. In the context of 6G, cyber-twin facilitates ultra-reliable low-latency communication, massive machine-type communication, and enhanced mobile broadband. They leverage edge computing, AI-driven analytics, and blockchain for secure, efficient, and autonomous network operations. This innovative approach promises significant advancements in personalized services, predictive maintenance, and smart connectivity, driving the future of seamless and intelligent wireless communication. This article provides a comprehensive and up-to-date review of cyber-twin in 6G communication via a systematic literature survey from 2019 to 2024, 50 articles were identified. A complete survey of the implementation of Cyber-twin in 6G communication systems, use cases, application areas, open challenges, and future directions were summarized.

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Abbreviations

CT:

Cyber-twin

DT:

Digital twin

TDD:

Time division duplex

ML:

Machine learning

SDN:

Software defined network

IoE:

Internet of everything

DDoS:

Distributed denial of service

NFV:

Network function virtualization

CPS:

Cyber-physical system

CPT:

Cyber-physical twin

MQTT:

Message queueing telemetry transport

UML:

Unified modelling language

QoS:

Quality of service

CPSS:

Cyber-physical social systems

UL:

Uplink

DL:

Downlink

RAN:

Radio access network

QoE:

Quality of everything

BS:

Base station

UE:

User equipment

C2X:

Customer to everything

MEC:

Mobile edge computing, multi-access edge computing

IoT:

Internet of things

SIC:

Successive interference cancellation

IRS:

Intelligent reflecting surfaces

SDO:

Standard development organization

DNN:

Deep neural networks

IIoT:

Industrial internet of things

NOMA:

Non-orthogonal multiple access

gNodeB:

Next generation node B

FDD:

Frequency division duplex

ITU:

International telecommunication unit

AI:

Artificial intelligence

IP:

Internet Protocol

FD-RAN:

Fully decoupled radio access network

V2X:

Vehicle to everything

V2I:

Vehicle to infrastructures

V2V:

Vehicle to vehicle

RSU:

Road side unit

IoV:

Internet of vehicles

NIB:

Network in box

PLS:

Physical layer security

FL:

Federated learning

DRL:

Deep reinforcement learning

ANNs:

Artificial neural networks

DCNNs:

Deep convolutional neural networks

CPPS:

Cyber-physical production system

CCC:

Computing, communication, catching

AWHA:

Adaptively weighted heuristic algorithm

AES:

Advanced encryption standard

AVISPA:

Automated validation of internet security protocols and applications

CSCUCB:

Client selection with combinatorial upper confidence bound

TSA:

Traversal search algorithm

ESA:

Equilibrium solving algorithm

CSHE:

Client selection with hadamard exploration

i-VEA:

I-vector extraction algorithm

WVUA:

Weight vector update algorithm

D3QN:

Dueling double deep Q network

MADDPG:

Multi-agent deep deterministic policy gradient

BFOA:

Beam forming optimization algorithm

FNN:

Fully-connected neural network

UEPA:

User plane function and edge server placement algorithm

CSA:

Cluster sampling algorithm

STA:

Secret transmission algorithm

DEHM:

Differential evolution based hierarchical multi-strategy

RIA:

Row-based iterative algorithm

ASN:

Asynchronous federated learning

FPTN:

Fast polynomial time network

TcpCDRL:

Deep reinforcement learning based transmission control approach

MDSA:

Multinomial distribution sampling algorithm

SGA:

Signature generation algorithm

PRST:

Proxy ring signature technique

CIICP:

Cyber-twin-driven Intelligent IoV computing policy

DCNN:

Deep conventional neural network

QO-SRO:

Quasi oppositional search and rescue optimization algorithm

PCDEA:

Parallel compact differential evolution algorithm

P4C:

Privacy-preserving pedersen commitment

NIB:

Network in box

EA:

Efficient algorithm

CMTA:

Concurrent multipath transfer algorithm

BFSA:

Brute force search algorithm

CMAB:

Combinatorial multiarmed bandit

GATTD3:

Graph attention network twin delayed deep deterministic policy gradient

GA:

Genetic algorithm

PPO:

Proximal policy optimization

CSRE:

Client selection with random explorations

SFA:

Segment forest adjustment

EMT:

Efficient multi-vehicle task offloading

SEA:

Symmetric encryption algorithm

FM-DRL:

Federated multi-agent deep reinforcement learning

ePOW-CA:

Enhance proof of work consensus algorithm

CQ:

Conducting query

EFCIA:

Effective and fast convergent iterative algorithm

DQN:

Deep Q-network

RA:

Random algorithm

JMSDP:

Joint mode selection and dynamic pricing

PPT:

Pseudo polynomial time

MOA:

Metaheuristic optimization algorithm

SVM:

Support vector machine

MATD3:

Multi-agent twin delay deep deterministic policy gradient algorithm

FREC:

Federated reinforcement learning-based edge caching

ANN:

Artificial neural network

DNN:

Deep neural network

DDPG:

Deep deterministic policy gradient

DQN:

Deep Q-network.

eHEFT:

Enhanced heterogeneous earliest finish time algorithm

PAUSE:

Progressive adaptive user selection environment

PESA II:

Pareto envelope-based selection algorithm

OA:

Optimization algorithm

CA:

Cryptographic algorithm

References

  1. Abdelwahab, S., et al. (2014). Enabling smart cloud services through remote sensing: An internet of everything enabler. IEEE Internet of Things Journal, 1(3), 276–288. https://doi.org/10.1109/JIOT.2014.2325071

    Article  Google Scholar 

  2. David, K., & Berndt, H. (2018). 6G vision and requirements: Is there any need for beyond 5G? IEEE Vehicular Technology., 13, 72–80. https://doi.org/10.1109/MVT.2018.2848498

    Article  Google Scholar 

  3. Sonikumar, D. N. (2022). Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy. Journal of Network and Computer Applications, 205, 103419. https://doi.org/10.1016/j.jnca.2022.103419

    Article  Google Scholar 

  4. Matt, D. T., Modrak, V., Zsifkovits H. (2020). Industry for SMEs Challenges, Opportunities, and Requirements. https://doi.org/10.1007/978-3-030-25425-4

  5. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of a systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375

    Article  Google Scholar 

  6. Evangelista, P., & Durst, S. (2015). Knowledge management in environmental sustainability practices of third-party logistics service providers. Vine, 45(4), 509–529. https://doi.org/10.1108/VINE-02-2015-0012

    Article  Google Scholar 

  7. Yu, Q., Ren, J., Fu, Y., Li, Y., & Zhang, W. (2019). Cyber-twin: An origin of next-generation network architecture. IEEE Xplore. https://doi.org/10.1109/MWC.001.1900184

    Article  Google Scholar 

  8. Yu, Q., Ren, J., Zhou, H. and Zhang, W. (2020). A cyber-twin based network architecture for 6G. In Proc 2nd 6G wireless Summit. https://doi.org/10.1109/6GSUMMIT49458.2020.9083808

  9. Qi, W., & Hang, Su. (2022). A cyber-twin based multimodal network for ECG patterns monitoring using deep learning. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3159583

    Article  Google Scholar 

  10. Jain, D. K., Tyagi, S. K. S., Neelakandan, S., Prakash, M., & Natrayan, L. (2022). Metaheuristic optimization-based resource allocation technique for cyber-twin driven 6G on IoE environment. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3138915

    Article  Google Scholar 

  11. Chen, Y., Zhao, F., Chen, X., & Yuan, W. (2022). Efficient multi-vehicle task offloading for mobile edge computing in 6G networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3133586

    Article  Google Scholar 

  12. Yin, Z., Cheng, N., Luan, T. H., & Wang, P. (2022). Physical layer security in cyber-twin enabled integrated satellite-terrestrial vehicle networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3133574

    Article  Google Scholar 

  13. Haag, S., & Anderl, R. (2018). Digital twin–proof of concept. Manufacturing Letters, 15, 64–66. https://doi.org/10.1016/j.mfglet.2018.02.006

    Article  Google Scholar 

  14. Wang, Y., Zhou, S., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023). A survey on digital twins: architecture, enabling technologies, security and privacy, and future prospects. IEEE Xplore. https://doi.org/10.1109/JIOT.2023.32639099

    Article  Google Scholar 

  15. Juneja, S., Gahlan, M., Dhiman, G., & Kautish, S. (2021). Review article futuristic cyber-twin architecture for 6G technology to support internet of everything. Hindawi Scientific Programming, 2021, 7. https://doi.org/10.1155/2021/9101782. 9101782.

    Article  Google Scholar 

  16. Czwick, C., & Anderl, R. (2020). Cyber-physical twins-definition, conception, benefit. Procedia CIRP, 90, 584–588. https://doi.org/10.1016/j.procir.2020.01.070

    Article  Google Scholar 

  17. Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters., 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001

    Article  Google Scholar 

  18. Kim, H., & Ben-Othman, J. (2023). Eco-friendly low resource security surveillance framework toward green AI digital twin. IEEE Communication Letters. https://doi.org/10.1109/LCOMM.2022.3218050

    Article  Google Scholar 

  19. Kim, H., & Ben-Othman, J. (2020). Toward integrated virtual emotion system with AI applicability for secure CPS-enabled smart cities: AI-based research challenges and security issues. IEEE Network. https://doi.org/10.1109/MNET.011.1900299

    Article  Google Scholar 

  20. Quan, Y., Zhou, H., Chen, J., Li, Y., Jing, J., Zhao, J. J., Qian, B., & Wang, J. (2019). A Fully-decoupled RAN Architecture for 6G Inspired by Neurotransmission. Journal of Communications and Information Networks, 4(4), 15–23. https://doi.org/10.23919/JCIN.2019.9005430

    Article  Google Scholar 

  21. Dahlman, E., Parkvall, S., & Skold, J. (2018). 5G NR: The next generation wireless access technology. Cambridge: Academic Press.

    Google Scholar 

  22. Sun, W., Zhang, H., Wang, R., & Zhang, Y. (2020). Reducing offloading latency for digital twin edge networks in 6G. IEEE Transaction on Vehicular Technology, 69(10), 12240–12251. https://doi.org/10.1109/TVT.2020.3018817

    Article  Google Scholar 

  23. Prathiba, S. B., Raja, G., Anbalagan, S., Gurumoorthy, S., Kumar, N., & Guizani, M. (2022). Cyber-twin driven federated learning based personalized service provision for 6G–V2X. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3133291

    Article  Google Scholar 

  24. Rodrigues, T. K., Liu, J., & Kato, N. (2021). Application of cyber-twin for offloading in mobile multiaccess edge computing for 6G networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3095308

    Article  Google Scholar 

  25. Chai, H., Leng, S., He, J., Zhang, K., & Cheng, B. (2022). Cyber chain: Cyber-twin empowered blockchain for lightweight and privacy-preserving authentication in internet of vehicles. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3132961

    Article  Google Scholar 

  26. Velliangiri, S., Manoharan, R., Ramachandran, S., & Rajasekar, V. (2021). Blockchain-based privacy-preserving framework for emerging 6G wireless communications. IEEE Transaction on Industrial Informatics. https://doi.org/10.1109/TII.2021.3107556

    Article  Google Scholar 

  27. Li, G., Lai, C., Rongxing, L., & Zheng, D. (2022). SecCDV: A security reference architecture for cyber-twin driven 6G V2X. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3133308

    Article  Google Scholar 

  28. He, M., Ni, J., He, Y., & Zhang, N. (2022). Low-complexity phased-array physical layer security in millimeter-wave communication for cyber-twin-driven V2X applications. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3138702

    Article  Google Scholar 

  29. Kumar, R., Kumar, P., Tripathi, R., Gupta, G. P., Garg, S., & Hassan, M. M. (2022). BDTwin: An integrated framework for enhancing security and privacy in cyber-twin driven automotive industrial internet of things. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3122021

    Article  Google Scholar 

  30. Li, Q., & Lin, X. (2021). Efficient and privacy-preserving speaker recognition for cyber-twin driven 6G. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3097266

    Article  Google Scholar 

  31. Zhang, X., Qian, B., Qin, X., Ma, T., Chen, J., Zhou, H., & Shen, X. S. (2022). Cybertwin-assisted mode selection in ultra-dense LEO integrated satellite-terrestrial network. Journal of Communications and Information Networks, 7(4), 360–374. https://doi.org/10.23919/JCIN.2022.10005214

    Article  Google Scholar 

  32. Artiga, X., et al. (2018). Shared access satellite-terrestrial reconfigurable backhaul network enabled by smart antennas at mmWave band. IEEE Networks., 32(5), 46–53. https://doi.org/10.1109/MNET.2018.1800030

    Article  Google Scholar 

  33. Yi, C., Park, S. O., Yang, C., Jiang, F., Ding, Z., Yang, C., Jiang, F., & Ding, Z. (2022). Muscular human cyber-twin for internet of everything: A pilot study. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3153305

    Article  Google Scholar 

  34. Bhat, J. R., & Alqahtani, S. A. (2021). 6G ecosystem: current status and future perspective. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3054833

    Article  Google Scholar 

  35. Thiong, G. M., et al. (2022). Digital twin technology: The future of predicting neurological complications of pediatric cancers and their treatment. Frontiers in Oncology. https://doi.org/10.3389/fonc.2021.781499

    Article  Google Scholar 

  36. Mazumder, O., Roy, D., Bhattacharya, S., Sinha, A., Pal, A. (2019). Synthetic PPG generation from a hemodynamic model with baroreflex autoregulation: a Digital twin of the cardiovascular system. 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC). https://doi.org/10.1109/EMBC.2019.8856691.

  37. Hussain, A. A., Bouachir, O., Al-Turjman, F., & Aloqaily, M. (2020). AI techniques for Covid-19. IEEE Access., 8, 128776–128795. https://doi.org/10.1109/access.2020.3007939

    Article  Google Scholar 

  38. Deepak, B. D., Al-Turjman, F., Aloqaily, M., & Alfandi, O. (2019). An authentic-based privacy preservation protocol for smart e-healthcare systems in IoT. IEEE Access., 7, 135632–135649. https://doi.org/10.1109/ACCESS.2019.2941575

    Article  Google Scholar 

  39. Tang, Q., & Wu, B. (2022). Multilayer game collaborative optimization based on Elman neural network system diagnosis in shared manufacturing mode. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/6135970

    Article  Google Scholar 

  40. Yang, W., Zhao, Q., Yan, X., & Chen, Z. (2021). A system framework of model quality analysis for product model in collaborative manufacturing. International Journal of Advanced Manufacturing Technology., 117, 1351–1374. https://doi.org/10.1007/s00170-021-07622-1

    Article  Google Scholar 

  41. Group IW et al. (2013). Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative Industrie 4.0. Forschungsunion Stifterverband die Deutsche Wirtschaft e.V., Berlin, Germany, Final Rep. Industrie 4.0 Working Group, 4.

  42. Lyu, F., et al. (2020). Characterizing urban vehicle-to-vehicle communications for reliable safety applications. IEEE Transaction on Intelligent Transport Systems, 21(6), 2586–2602. https://doi.org/10.1109/TITS.2019.2920813

    Article  Google Scholar 

  43. Shi, W., Zhou, H., Li, J., Xu, W., Zhang, N., & Shen, X. (2018). Drone assisted vehicular networks: Architecture, challenges, and opportunities. IEEE Networks., 32(3), 130–137. https://doi.org/10.1109/MNET.2017.1700206

    Article  Google Scholar 

  44. Cheng, N., et al. (2020). A comprehensive simulation platform for space-air ground integrated network. IEEE Wireless Communications., 27(1), 178–185. https://doi.org/10.1109/MWC.001.1900072

    Article  Google Scholar 

  45. Ma, B., Ren, Z., & Cheng, W. (2022). Traffic routing-based computation offloading in cyber-twin driven internet of vehicles for V2X applications. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3134715

    Article  Google Scholar 

  46. Feng, Q., He, D., Zeadally, S., & Liang, K. (2020). BPAS: Blockchain-assisted privacy-preserving authentication system for vehicular ad hoc networks. IEEE Transaction on Industrial Informatics., 16(6), 4146–4155. https://doi.org/10.1109/TII.2019.2948053

    Article  Google Scholar 

  47. Zhou, H., et al. (2017). TVwhite space enabled connected vehicle networks: Challenges and solutions. IEEE Networks., 31(3), 6–13. https://doi.org/10.1109/MNET.2017.1600049NM

    Article  Google Scholar 

  48. Yunting, Xu., Zhou, H., Chen, J., & MaShen, T. S. (2021). Cyber-twin assisted wireless asynchronous federated learning mechanism for edge Computing. IEEE Global Communication Conference. https://doi.org/10.1109/GLOBECOM46510.2021.9685076

    Article  Google Scholar 

  49. Liang, H., Li, H., & Zhang, W. (2021). A combinatorial auction resource trading mechanism for cyber-twin based 6G network. IEEE Internet Things Journal. https://doi.org/10.1109/JIOT.2021.3095554

    Article  Google Scholar 

  50. Shen, S., Chong, Y., Zhang, K., & Ci, S. (2021). Adaptive artificial intelligence for resource-constrained connected vehicles in cyber-twin driven 6G network. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3101231

    Article  Google Scholar 

  51. Li, J., Shi, W., Ye, Q., Zhang, S., Zhuang, W., & Shen, X. (2021). Joint virtual network topology design and embedding for cyber-twin enabled 6G core networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3097053

    Article  Google Scholar 

  52. Chen, Z., Zhang, R., Liu, Y., Cai, L. X., & Chen, Q. (2021). Performance study of cyber-twin-assisted random access NOMA. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3100457

    Article  Google Scholar 

  53. Abouaomar, A., Cherkaoui, S., Mlika, Z., & Kobbane, A. (2021). Resource provisioning in edge computing for latency-sensitive applications. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3052082

    Article  Google Scholar 

  54. Lv, Z., & Qiao, L. (2020). Optimization of collaborative resource allocation for mobile edge computing. Computer Communication, 161, 19–27. https://doi.org/10.1016/j.comcom.2020.07.022

    Article  Google Scholar 

  55. Li, H., Xu, H., Zhou, C., Lü, X., & Han, Z. (2020). Joint optimization strategy of computation offloading and resource allocation in multi-access edge computing environment. IEEE Transaction on Vehicular Technology., 69(9), 10214–10226. https://doi.org/10.1109/TVT.2020.3003898

    Article  Google Scholar 

  56. Tang, Q., Xie, R., Feng, L., Fei Richard, Y., Chen, T., Zhang, R., & Huang, T. (2024). SIaTS: A service intent-aware task scheduling framework for computing power networks. IEEE Network. https://doi.org/10.1109/MNET.2023.3326239

    Article  Google Scholar 

  57. Tang, Q., Xie, R., Fang, Z., Huang, T., Chen, T., Zhang, R., & Richard Yu, F. (2024). Joint service deployment and task scheduling for satellite edge computing: A two-timescale hierarchical approach. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.2024.3365889

    Article  Google Scholar 

  58. Guan, Y., Rongxing, L., Zheng, Y., Zhang, S., Shao, J., & Wei, G. (2021). Towards privacy-preserving cyber-twin based spatiotemporal keyword query for ITS in 6G era. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3096674

    Article  Google Scholar 

  59. Li, Y., Ma, X., Mengwei, X., Zhou, A., Sun, Q., Zhang, N., & Wang, S. (2021). Joint placement of UPF and edge server for 6G network. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3095236

    Article  Google Scholar 

  60. Kuang, L., Chen, X., Jiang, C., Zhang, H., & Wu, S. (2017). Radio resource management in future terrestrial-satellite communication networks. IEEE Wireless Communication., 24(5), 81–87. https://doi.org/10.1109/MWC.2017.1700043

    Article  Google Scholar 

  61. Gui, G., Liu, M., Tang, F., Kato, N., & Adachi, F. (2020). 6G: Opening new horizons for integration of comfort, security, and intelligence. IEEE Wireless Communication., 27(5), 126–132. https://doi.org/10.1109/MWC.001.1900516

    Article  Google Scholar 

  62. K. Maine, C. Devieux, and P. Swan. (1995). Overview of IRIDIUM satellite network. In Proc. IEEE WESCON, https://doi.org/10.1109/WESCON.1995.485428.

  63. del Portillo, I., Cameron, B. G., & Crawley, E. F. (2019). A technical comparison of three low earth orbit satellite constellation systems to provide global broadband. Acta Astronautica., 159, 123–135. https://doi.org/10.1016/j.actaastro.2019.03.040

    Article  Google Scholar 

  64. Choi, J. P., & Joo, C. (2015). Challenges for efficient and seamless space terrestrial heterogeneous networks. IEEE Communication Magazine., 53(5), 156–162. https://doi.org/10.1109/MCOM.2015.7105655

    Article  Google Scholar 

  65. Jia, M., Gu, X., Guo, Q., Xiang, W., & Zhang, N. (2016). Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5G. IEEE Wireless Communication., 23(6), 96–106. https://doi.org/10.1109/MWC.2016.1500108WC

    Article  Google Scholar 

  66. Ding, Z., et al. (2017). Application of non-orthogonal multiple access in LTE and 5G networks. IEEE Communication Magazine., 55(2), 185–191. https://doi.org/10.1109/MCOM.2017.1500657CM

    Article  Google Scholar 

  67. Ding, Z., Lei, X., Karagiannidis, G. K., Schober, R., Yuan, J., & Bhargava, V. K. (2017). A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends. IEEE Journal on Selected Areas in Communication, 35(10), 2181–2195. https://doi.org/10.1109/JSAC.2017.2725519

    Article  Google Scholar 

  68. Maraqa, O., Rajasekaran, A. S., Al-Ahmadi, S., Yanikomeroglu, H., & Sait, S. M. (2019). A survey of rate-optimal power domain NOMA with enabling technologies of future wireless networks. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2020.3013514

    Article  Google Scholar 

  69. Liu, Y., Qin, Z., Elkashlan, M., Ding, Z., Nallanathan, A., & Hanzo, L. (2017). Nonorthogonal multiple access for 5G and beyond. Proceedings of the IEEE, 105(12), 2347–2381. https://doi.org/10.1109/JPROC.2017.2768666

    Article  Google Scholar 

  70. Ding, Z., Fan, P., & Poor, H. V. (2016). Impact of user pairing on 5G nonorthogonal multiple-access downlink transmissions. IEEE Transaction on Vehicular Technology., 65(8), 6010–6023. https://doi.org/10.1109/TVT.2015.2480766

    Article  Google Scholar 

  71. Al Rabee, F., Davaslioglu, K., and Gitlin, R. (2017). The optimum received power levels of uplink non-orthogonal multiple access (NOMA) signals. Proceedings IEEE 18th WirelessMicrowave Technology Conference (WAMICON). (pp. 1–4), https://doi.org/10.1109/WAMICON.2017.7930242.

  72. Vaezi, M., Schober, R., Ding, Z., & Vincent Poor, H. (2019). Non-orthogonal multiple access: Common myths and critical questions. IEEE Wireless Communications. https://doi.org/10.1109/MWC.2019.1800598

    Article  Google Scholar 

  73. Omoniwa, B., Hussain, R., Javed, M. A., Bouk, S. H., & Malik, S. A. (2019). Fog/edge computing-based IoT (FECIoT): Architecture, applications, and research issues. IEEE Internet of Things Journal, 6(3), 4118–4149. https://doi.org/10.1109/JIOT.2018.2875544

    Article  Google Scholar 

  74. Xu, H., et al. (2020). Blockchain-enabled resource management and sharing for 6G communications. Digital Communication Networks, 6(3), 261–269. https://doi.org/10.1016/j.dcan.2020.06.002

    Article  Google Scholar 

  75. Zhang, J., Zhong, H., Cui, J., Xu, Y., & Liu, L. (2020). An extensible and effective anonymous batch authentication scheme for smart vehicular networks. IEEE Internet of Things Journal, 7(4), 3462–3473. https://doi.org/10.1109/JIOT.2020.2970092

    Article  Google Scholar 

  76. Wang, C., et al. (2021). B-TSCA: Blockchain-assisted trustworthiness scalable computation for V2I authentication in VANETs. IEEE Transactions on Emerging Topics in Computing, 9(3), 1386–1396. https://doi.org/10.1109/TETC.2020.2978866

    Article  Google Scholar 

  77. Gabay, D., Akkaya, K., & Cebe, M. (2020). Privacy-preserving authentication scheme for connected electric vehicles using blockchain and zero-knowledge proofs. IEEE Transaction on Vehicular Technology, 69(6), 5760–5772. https://doi.org/10.1109/TVT.2020.2977361

    Article  Google Scholar 

  78. Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry., 101, 1–12. https://doi.org/10.1016/j.compind.2018.04.015

    Article  Google Scholar 

  79. Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., Liebenberg, M., Michael, J., Pennekamp, J., Quix, C., & Rumpe, B. (2022). A computer science perspective on digital transformation in production. ACM Transactions on Internet of Things. https://doi.org/10.1145/3502265

    Article  Google Scholar 

  80. Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE Transaction on Industrial Informatics, 14(11), 4724–4734. https://doi.org/10.1109/TII.2018.2852491

    Article  Google Scholar 

  81. Kaur K., and Sachdeva, M. (2020). Fog computing in IoT: An overview of new opportunities. Proceedings ICETIT. (pp. 59–68). Springer. https://doi.org/10.1007/978-3-030-30577-2_5.

  82. Koroniotis, N., Moustafa, N., & Sitnikova, E. (2019). Forensics and deep learning mechanisms for botnets in the internet of things: A survey of challenges and solutions. IEEE Access, 7, 61764–61785. https://doi.org/10.1109/ACCESS.2019.2916717

    Article  Google Scholar 

  83. Tripathi, A. K., Sharma, K., Bala, M., Kumar, A., Menon, V. G., & Bashir, A. K. (2021). A parallel military-dog-based algorithm for clustering big data in the cognitive industrial internet of things. IEEE Transaction on Industrial Informatics., 17(3), 2134–2142. https://doi.org/10.1109/TII.2020.2995680

    Article  Google Scholar 

  84. Zhou, H., Xu, S., Ren, D., Huang, C., & Zhang, H. (2017). Analysis of event-driven warning message propagation in vehicular Ad Hoc networks. Ad Hoc Network, 55, 87–96. https://doi.org/10.1016/j.adhoc.2016.09.018

    Article  Google Scholar 

  85. Lyu, F., et al. (2021). Service-oriented dynamic resource slicing and optimization for space-air-ground integrated vehicular networks. IEEE Transaction on Intelligent Transport Systems. https://doi.org/10.1109/TITS.2021.3070542

    Article  Google Scholar 

  86. Ni, Y., He, J., Cai, L., & Bo, Y. (2018). Data uploading in hybrid V2V/V2I vehicular networks: Modeling and cooperative strategy. IEEE Transaction on Vehicular Technology, 67(5), 4602–4614. https://doi.org/10.1109/TVT.2018.2796563

    Article  Google Scholar 

  87. Omar, H. A., Zhuang, W., & Li, L. (2015). Gateway placement and packet routing for multihop in-vehicle internet access. IEEE Transaction on Emerging Topics on Computing, 3(3), 335–351. https://doi.org/10.1109/TETC.2015.2395077

    Article  Google Scholar 

  88. Wang, Y., Zheng, J., & Mitton, N. (2016). Delivery delay analysis for roadside unit deployment in vehicular ad-hoc networks with intermittent connectivity. IEEE Transaction on Vehicular Technology., 65(10), 8591–8602. https://doi.org/10.1109/TVT.2015.2506599

    Article  Google Scholar 

  89. Heo, J., Kang, B., Yang, J. M., Paek, J., & Bahk, S. (2019). Performance-cost tradeoff of using mobile roadside units for V2X communication. IEEE Transaction on Vehicular Technology., 68(9), 9049–9059. https://doi.org/10.1109/TVT.2019.2925849

    Article  Google Scholar 

  90. Adhikari, M., Hazra, A., Menon, V. G., Chaurasia, B. K., & Mumtaz, S. (2021). A roadmap of next-generation wireless technology for 6G-enabled vehicular networks. IEEE Internet of Things Magazine. https://doi.org/10.1109/IOTM.001.2100075

    Article  Google Scholar 

  91. Wu, Q., Zhang, S., Zheng, B., You, C., & Zhang, R. (2021). Intelligent reflecting surface aided wireless communications: A tutorial. IEEE Transaction on Communication., 69(5), 3313–3351. https://doi.org/10.1109/TCOMM.2021.3051897

    Article  Google Scholar 

  92. Gong, S., et al. (2020). Toward smart wireless communications via intelligent reflecting surfaces: A contemporary survey. IEEE Communications on Surveys & Tutorials., 22(4), 2283–2314. https://doi.org/10.1109/COMST.2020.3004197

    Article  Google Scholar 

  93. Adhikari, M., & Hazra, A. (2022). 6G-enabled ultra-reliable low-latency communication in edge networks. IEEE Communication Standards Magazine. https://doi.org/10.1109/MCOMSTD.0001.2100098

    Article  Google Scholar 

  94. Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber-physical system toward smart manufacturing and Industry 4.0: Correlation and comparison. Engineering. https://doi.org/10.1016/j.eng.2019.01.014

    Article  Google Scholar 

  95. Dash, S. P., Joshi, S., Satapathy, S. C., Shandilya, S. K., & Panda, G. (2022). A cyber-twin based 6G cooperative IoE communication network: Secrecy outage analysis. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3140125

    Article  Google Scholar 

  96. Nivetha, A., & Preetha, K. S. (2024). Meta-algorithmic optimized power allocation in cybertwin-based sixth generation cooperative communication system. Results in Engineering Journal (online). https://doi.org/10.1016/j.rineng.2024.102740

    Article  Google Scholar 

  97. Pethuru Raj, Chellammal Surianarayanan, (2020). The digital twin paradigm for smarter systems and environments: The industry use cases. In advances in computers

  98. Liu, X., TaoJiang, B. D., Xiang, F., Jiang, G., Sun, Y., Kong, J., & Li, G. (2023). A systematic review of digital twin about physical entities, virtual models, twin data, and applications. Advanced Engineering Informatics. https://doi.org/10.1016/j.aei.2023.101876

    Article  Google Scholar 

  99. Mahbub, M., & Shubair, R. M. (2023). Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2023.103726

    Article  Google Scholar 

  100. Syed, S. A., SheelaSobanaRani, K., Mohammad, G. B., AnilKumar, G., Chennam, K. K., Jaikumar, R., Natarajan, Y., Srihari, K., BarakkathNisha, U., & Sundramurthy, V. P. (2022). Design of resources allocation in 6G cyber-twin technology using the fuzzy neuro model in healthcare systems. Hindawi Journal of Healthcare Engineering, 2, 9. https://doi.org/10.1155/2022/5691203. 5691203.

    Article  Google Scholar 

  101. Manoharan, H., Teekaraman, Y., Kuppusamy, R., Kaliyan, N., & Thelkar, A. R. (2022). Examining the effect of cyber-twin and blockchain technologies for industrial applications using AI. Hindawi, Mathematical Problems in Engineering, 2022, 10. https://doi.org/10.1155/2022/3048038. 3048038.

    Article  Google Scholar 

  102. Liang, H., Zhang, W. (2020). A barter and combinatorial auction based hierarchical resource trade mechanism for cyber-twin network. 3rd international conference on hot information-centric networking. https://doi.org/10.1109/HotICN50779.2020.9350841.

  103. Zhang, E., Zhao, L., Lin, N., Zhang, W., Hawbani, A., Min, G. (2022). Cooperative task offloading in cyber-twin-assisted vehicular edge computing. IEEE 20th international conference on embedded and ubiquitous computing (EUC), https://doi.org/10.1109/EUC57774.2022.00020.

  104. Zhong, X., He, Y. (2021). A cyber-twin driven task offloading scheme based on deep reinforcement learning and graph attention networks. 13th international conference on wireless communications and signal processing (WCSP), https://doi.org/10.1109/WCSP52459.2021.9613687.

  105. Peichen Liu, Kai Peng, Bohai Zhao. (2022). A cyber-twin driven intelligent offloading method for IoV applications using DRL in smart cities. IEEE international symposium on dependable, autonomic and secure computing (DASC). https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927948.

  106. Sun, R., Yang, X., Cheng, N., Wang, X., Li, C. (2023). Knowledge-driven multi-agent reinforcement learning for computation offloading in cyber-twin-enabled internet of vehicles. IEEE 98th vehicular technology conference (VTC2023-Fall). https://doi.org/10.1109/VTC2023-Fall60731.2023.10333855.

  107. Vibha Jain, A., Bijendra Kumar, A., & Gupta, A. (2022). Cyber-twin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment. Journal of King Saud University-Computer and Information Sciences, 34, 5708–5720. https://doi.org/10.1016/j.jksuci.2022.02.005

    Article  Google Scholar 

  108. Hou, W., Wen, H., Song, H., Lei, W., & Zhang, W. (2021). Multiagent deep reinforcement learning for task offloading and resource allocation in cyber-twin-based networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3095677

    Article  Google Scholar 

  109. Liu, J., Yong, Y., Li, K., & Gao, L. (2021). Post-quantum secure ring signatures for security and privacy in the cyber-twin driven 6G. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3102385

    Article  Google Scholar 

  110. Yang, M., Wang, X., Qian, H., Zhu, Y., Zhu, H., Guizani, M., & Chang, V. (2022). An improved federated learning algorithm for privacy preserving in cyber-twin driven 6G system. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3149516

    Article  Google Scholar 

  111. Soleymani, S. A., Goudarzi, S., Anisi, M. H., Movahedi, Z., Jindal, A., & Kama, N. (2022). PACMAN: Privacy-preserving authentication scheme for managing cyber-twin based 6G networking. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3121505

    Article  Google Scholar 

  112. Koroniotis, N., Moustafa, N., Schiliro, F., Gauravaram, P., & Janicke, H. (2023). The SAir-IIoT cyber testbed as a service: A novel cyber-twins architecture in IIoT-based smart airports. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3106378

    Article  Google Scholar 

  113. Zhang, X., Xing, H., Zang, W., Jin, Z., & Shen, Y. (2022). Cyber-twin driven multi-intelligent reflecting surfaces aided vehicular edge computing leveraged by deep reinforcement learning. IEEE 96th Vehicular Technology Conference. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012694

    Article  Google Scholar 

  114. Qi, L., Xiaolong, X., Xiaotong, W., Ni, Q., Yuan, Y., & Zhang, X. (2023). Digital-twin-enabled 6g mobile network video streaming using mobile crowdsourcing. IEEE Journal on Selected Areas in Communication. https://doi.org/10.1109/JSAC.2023.3310077

    Article  Google Scholar 

  115. Ni, Y., Zhao, C., & Cai, L. (2022). Hybrid RSU management in cyber-twin-IoV for temporal and spatial service coverage. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2021.3138749

    Article  Google Scholar 

  116. Quan, W., Liu, M., Cheng, N., Zhang, X., Gao, D., & Zhang, H. (2022). Cyber-twin driven DRL-based adaptive transmission scheduling for software defined vehicular networks. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2022.3151750

    Article  Google Scholar 

  117. Yan, Si., Ye, Q., & Zhuang, W. (2021). Learning-based transmission protocol customization for VoD streaming in cyber-twin enabled next-generation core networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3097628

    Article  Google Scholar 

  118. Zhou, Z., Abawajy, J., Shojafar, M., & Chowdhury, M. (2022). DEHM: An improved differential evolution algorithm using hierarchical multistrategy in a cyber-twin 6G network. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3140854

    Article  Google Scholar 

  119. Quan, Y., Liang, D., Qin, M., Chen, J., Zhou, H., Ren, J., Li, Y., Jun, W., Gao, Y., & Zhang, W. (2023). Cybertwin based cloud native networks. Journal of Communications and Information Networks, 8(3), 187–202. https://doi.org/10.23919/JCIN.2023.10272347

    Article  Google Scholar 

  120. Liang, H., & Zhang, W. (2023). A game-theoretic access strategy for satellite edge computing enabled massive IoT networks. IEEE Global Communications Conference: IoT and Sensor Networks. https://doi.org/10.1109/ACCESS.2019.2963068

    Article  Google Scholar 

  121. Hansong, X., Jun, W., Li, J., & Lin, X. (2021). Deep-reinforcement-learning-based cyber-twin architecture for 6G IIoT: An integrated design of control, communication, and computing. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3098441

    Article  Google Scholar 

  122. Chengxiao, Y., Quan, W., Gao, D., Zhang, Y., Liu, K., Wen, W., Zhang, H., & Shen, X. (2021). Reliable cyber-twin driven concurrent multipath transfer with deep reinforcement learning. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3101447

    Article  Google Scholar 

  123. Xue, X., & Jiang, C. (2021). Matching sensor ontologies with multi-context similarity measure and parallel compact differential evolution algorithm. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2021.3115471

    Article  Google Scholar 

  124. Javed, M. A., Nguyen, T., Mirza, J., Ahmed, J., & Ali, B. (2022). Reliable communications for cyber-twin driven 6G IoVs using intelligent reflecting surfaces. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3151773

    Article  Google Scholar 

  125. Zhu, D., Bilal, M., & Xiaolong, X. (2022). Edge task migration with 6G-enabled network in box for cyber-twin based internet of vehicles. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3113879

    Article  Google Scholar 

  126. Adhikari, M., Munusamy, A., Kumar, N., & Srirama, S. N. (2021). Cyber-twin-driven resource provisioning for IoE applications at 6G-enabled edge networks. IEEE Transaction on Industrial Information. https://doi.org/10.1109/TII.2021.3096672

    Article  Google Scholar 

  127. Yang, L., Wang, L., Zheng, Z., & Zhang, Z. (2022). A continual learning-based framework for developing a single wind turbine cyber-twin adaptively serving multiple modeling tasks. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3130721

    Article  Google Scholar 

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Nivetha, A., Preetha, K.S. A Review on Cyber-Twin in Sixth Generation Wireless Networks: Architecture, Research Challenges & Issues. Wireless Pers Commun 138, 1815–1865 (2024). https://doi.org/10.1007/s11277-024-11577-3

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