1. Introduction

• The essential building blocks of the urban metaverse ecosystem – the so called MetaCyberCity – are surveyed concisely to visualise strengths in cybersecurity and shortcomings towards cyberthreats.
• The possible cyberthreats for the urban metaverse cyberspaces are revealed, and how these threats can be addressed with a series of countermeasures is analysed.
• A blockchain-based authentication approach, which uses the metaverse-immersive devices to generate Privacy-Preserving Machine Learning (PPML) models, is designed. This design, by avoiding single point failure and eliminating a trusted third party for the verification of the authenticity of models, can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars within urban metaverse cyberspaces – without renouncing targeted functional abilities of the immersive devices and the essential objectives of the urban metaverse cyberspaces.

2. Literature Survey

3. Components of the Urban Metaverse Ecosystem

3.3. Urban metaverse ecosystem: MetaCyberCity

The urban metaverse ecosystem, the so-called MetaCyberCity is the interconnected network of decentralised blockchain worlds, i.e. UMaaSs, and resident avatars of the MetaCyberCity can navigate from one UMaaS to another with interoperable abilities and they can build their UMaaS worlds (elaborated in Section 3.4). The general infrastructure of the urban metaverse with the key enabling technologies is depicted in Figure 3. The framework consists of five main building blocks, namely, A. SC, B. Users, C. Metaverse components, D. SC DTs and metaverse pools, and finally E. UMaaS. These components and their intertwined interactions with each other are elaborated in [3]. A metaverse can be defined as “democratised, decentralised, user-driven virtual and augmented immersive 3D spaces where two worlds – virtual and physical existence – can be more tangibly connected and people who are not in the same physical space can come together with their avatars to feel many different types of experiences” [3]. An urban metaverse can be defined as the expansion of DTs in the fields of people and society [54]. It provides us with an immersive environment to perform our daily routines in the physical world. SCs, with DTs, are expected to significantly benefit from the promising potentials of the metaverse in the most optimum way. The combination of metaverse and SC will increase further in the forthcoming periods, and this will affect urban life by spreading to all SC applications [57]. Metaverse cyberspaces can be classified as centralised that is controlled by a central entity (e.g. Meta) and decentralised (e.g. Decentraland) that is user-owned and most of the control is in the hands of their users. Urban metaverse cyberspaces are composed of both centralised and decentralised architecture regarding the objectives of the cyberspaces, some of which are controlled by the local city governments and some of which (i..e., user-owned, user-centric) may be managed by their users or together with the local government.

Figure 3. Architecture of a metaverse city: Main components and their interaction with each other [3].

Figure 3. Architecture of a metaverse city: Main components and their interaction with each other [3].

Blockchain, as a distributed database, provides unique data structures (i.e. crypto worlds) that were designed to make many people interact/transact with each other without thinking about privacy too much. On the other hand, Distributed Ledger Technology (DLT) aims to incorporate privacy into the transactions further. Blockchain, a type of DLT, is implemented as a decentralised Peer-to-Peer (P2P) network and stores a digital ledger in a distributed and secure manner; smart contracts extend the capabilities of blockchain technology; they are executable codes that can convert into software all the terms and conditions of an agreement between various entities and are deployed on the blockchain; some of the advantages provided by smart contracts are automation, access control, trust-building, and elimination of third-party execution [38]. The key components of the metaverse in developing urban worlds are summarised in Figure 3 C.

Figure 4. Left: Pivotal components of UMaaS. Blurring borders between virtual and physical worlds. Right: Various structural design models of UMaaSs: Jointed (dependent) and unconnected (independent) spaces (Figure 3E).

Figure 4. Left: Pivotal components of UMaaS. Blurring borders between virtual and physical worlds. Right: Various structural design models of UMaaSs: Jointed (dependent) and unconnected (independent) spaces (Figure 3E).

4. Cyberthreats and Basic Countermeasures for Urban Cyberspaces

5. Blockchain-Based Decentralised Privacy-Preserving Machine Learning (DPPML) Authentication and Verification Approach

The urban metaverse cyberspaces and associated entities are distributed on the decentralised public and private ledgers (Figure 7). AI models are required to be trained at the edges locally and encrypted update gradients need to be transferred to construct larger or global models regarding the principles of CL/FL as expressed earlier in Section 2.3. In order to improve collaboration in learning, the privacy concerns of each data subject should be addressed by extending the concept of privacy protection to the original learning entity. In this vein, a DPPML scheme, based on transparency and personal consent (Figure 6), is developed using the cyber gesture signature with wearable immersive devices to protect users’ privacy while verifying the authenticity of the subject, where the data subjects in more control with further security measures. Cyber signatures, which make the subject different from other subjects, can be built through their body language using tightly coupled immersive wearable metaverse devices as visualised in Figure 9. The pseudo codes of model training with a MoCap device are presented on blockchain in Algorithms 1 and 2. More specifically, Algorithm 1 shows the local training of the model with epochs fed by the particular online instant features acquired from the device, which is worn by one of the active nodes on the blockchain whereas Algorithm 2 displays the global model update with the blockchain operations for verification of the update gradients acquired from all the active nodes on the blockchain through blockchain mining. Algorithm 1 is run by each node individually at the edges locally whereas Algorithm 2 is run on blockchain by all the active nodes where current nodes can leave and new nodes can join at any time. From a more technical standpoint, the gesture feature set for particular attributes, $F=\left\{A_1,A_2,\dots ,A_{size}\right\}$, of resident entities, $R=\left\{R_1,R_2,\dots ,R_{size}\right\}$, need to be trained per individual with an epoch sample size, $S=\left\{F_1,F_2,\dots ,F_{epoch}\right\}$. Local weights ($w^L$) and global weights ($w^G$) are synchronously updated after every epoch iteration to generate particular vocal or gesture models, $M_{ID}$, per immersive device, $ID$, as displayed in Eq. 1.

$$ID=\left\{I{D}_{MoCap},I{D}_{HeadSetFace},I{D}_{HeadSetLip},I{D}_{HandTrackingSet},I{D}_{VocalAtr},\dots ,I{D}_{size}\right\}$$

(1)

Residents, R, perform the PoW operations with a block generation rate of $\lambda$ and whoever is successful in reaching a hash key, by finding a nonce that is smaller than the target value based on the difficulty of PoW, places the candidate block with their locally trained, updated model gradient parameters along with the other emerging models updated successfully by other nodes similarly with the previous PoW operations. Then, they continue mining with the agreed-upon PoW and update their model parameters likewise obtained from the next local epoch operations until their models converge to a solution that satisfies a targeted accuracy rate, $Acc$, (i.e. $|w^G-w^{G-1}|\le ϵ$ where $ϵ$ is a very small value). The last blocks during the training process with block mining, which stores each resident’s individual aggregated local model updates, are added to the blockchain with their block headers and block bodies as a distributed ledger (Figure 9), and downloaded by other residents, R, as nodes in the blockchain to carry on the next PoW operations with a newly generated candidate block. The body of the block has the last generated hash key corresponding to the individual resident model. In other words, all the updated particular models are transferred to the last block with the hash keys that are used to update the gradients for those models. All the other residents/miners quit the current PoW operations when they receive the new block that is added to the blockchain to download this block and start the PoW operations from scratch, with the most recent updates using their candidate blocks with their updates, which are distributed to all other nodes. During this process, every resident, who performs the PoW for his/her model update parameter with a successful hashing, verifies all the previous model updates with the previous PoW operations as well, which are updated by other residents for their model training. The residents whose models have converged to a solution either stop the PoW operations and leave the mining as a node or continue as is to verify other residents’ model updates with their current, successful updates, without providing further input updates – considering that the mining reward is still applicable even though data reward is no longer offered. The creation of blocks in chronological order, through the PoW consensus mechanism per $ID$, stops when no resident remains as an active node, where all the models of residents – per $ID$ – that are expected to be completed as new nodes get added to the blockchain to build their models. Local model updates for all residents as nodes are aggregated at the last block separately, leading to final global models that correspond to individual residents. In other words, the blockchain expands further when new residents join the MetaCyberCity or UMaaSs. Users are not allowed to be successful for two consecutive PoW hashing in order not to verify their own model updates, which aims to include multiple verifications with distributed ledgers with timestamps. The final block is composed of the final aggregated individual models of residents per $ID$ as in Eq. 2 for $ID$, MoCap, until new nodes join.

$$M_{I{D}_{MoCap}}=\left\{R_{1_{\left(M_{I{D}_{MoCap}}\right)}},R_{2_{\left(M_{I{D}_{MoCap}}\right)}},R_{3_{\left(M_{I{D}_{MoCap}}\right)}},\dots ,R_{siz{e}_{\left(M_{I{D}_{MoCap}}\right)}}\right\}$$

(2)

Residents upload their local true gradient updates ($w^L$) to form their model truthfully, with the required timestamp history where models, generated using false parameters, cannot result in authenticating the model owners during the use of the particular immersive device. Every entity feeds the DL model training process with the model-specific encrypted parameters until the model converges to a desired solution within a UMaaS or MetaCyberCity. The original user data is retained with the data owner and not shared with third parties and all the communicated packets are delivered between the entities using P2P/E2E ciphertexts (Figure 7) to avoid any possible data leakage, which aims to preserve both the data’s sovereignty – and privacy, to a certain extent. Updated gradients may reveal individual private or actual information when associated with data attributes and structures. Therefore, encryption mechanisms provide further privacy protection even though the updated gradients or communicated packets have been anonymised. The above operations are repeated for all $ID$ using different blockchain forms.

Algorithm 1: Individual authentication modelling per immersive device: Local training (ID =$\left\{I{D}_{MoCap},I{D}_{HeadSetFace},I{D}_{HeadSetLip},I{D}_{HandTrackingSet},\dots ,I{D}_{size}\right\}$.

Global gesture models, which are verified by other residents in the MetaCyberCity or UMaaSs and employ a PoW consensus mechanism, are employed to be used for authentication mechanisms as proof, which has been implemented in Algorithm 3, regularly during the metaverse immersive actions/activities, when requested by any active user in UMaaSs, or when required under particular circumstances such as before completing asset transactions to ensure the identity of the other party. In our approach, the use of the model to authenticate a resident with the blockchain-based model can be allowed by the resident using the private key and the last hash key that is associated with the particular user-/device-based model in the body of the block. Here, the blockchain is employed to provide trust among entities in modelling gestures using every online training phase automated by $ID$, i.e. epoch, by avoiding single point failure regarding the training in a central server and not requiring a trusted third party for the verification of the authenticity of the model and data from which the model is generated. From a more technical standpoint, the gesture feature set for particular attributes from the particular immersive device, $F=\left\{A_1,A_2,\dots ,A_{size}\right\}$, of the resident entity, $R=meR$, need to be run with the model using a couple of sample size, $S=\left\{F_1,F_2,\dots ,F_k\right\}$. The model results in either providing the authentication proof with a successful outcome where one of the feature sets is recognised or rejecting the authentication with an unsuccessful outcome with no recognition for any of the attribute sets in the sample array. Each entity knows nothing about the trained data and its providers` identity while using the global ML models in an automated manner with the entity parameters to get a targeted classified outcome needed. The global model construction and use of this model should ensure that there is no adversarial entity collaborating with the process, which can be avoided using effective E2E/P2P encryption mechanisms (Figure 10). These gesture models, aiming at authenticating the other party through the use of immersive devices, can be instrumented effectively against the theft of credentials, identity, or avatars. Regular biometric checks can be implemented with the proposed approach to ensure that the avatar in action represents the intended correct person.

Algorithm 2: Individual authentication and verification modelling per immersive device: Global update with blockchain.

Algorithm 3: Proof of identity using blockchain-based DPPML pre-trained models with immersive devices where $ID=I{D}_{MoCap}$.

6. Challenges

7. Lessons Learned

8. Discussion

9. Conclusion

10. Limitations

Abbreviations

References

1. Stephenson, N. Snow Crash, 1 ed.; Bantam Books: New York, USA, 1992.
2. TheNexus. A Summary of “Snow Crash” by Neal Stephenson (1992), 2023.
3. Kuru, K. MetaOmniCity: Toward Immersive Urban Metaverse Cyberspaces Using Smart City Digital Twins. IEEE Access 2023, 11, 43844–43868. [CrossRef]
4. Park, S.M.; Kim, Y.G. A Metaverse: Taxonomy, Components, Applications, and Open Challenges. IEEE Access 2022, 10, 4209–4251. [CrossRef]
5. Al-Ghaili, A.M.; Kasim, H.; Al-Hada, N.M.; Hassan, Z.B.; Othman, M.; Tharik, J.H.; Kasmani, R.M.; Shayea, I. A Review of Metaverse’s Definitions, Architecture, Applications, Challenges, Issues, Solutions, and Future Trends. IEEE Access 2022, 10, 125835–125866. [CrossRef]
6. Ritterbusch, G.D.; Teichmann, M.R. Defining the Metaverse: A Systematic Literature Review. IEEE Access 2023, 11, 12368–12377. [CrossRef]
7. Huynh-The, T.; Pham, Q.V.; Pham, X.Q.; Nguyen, T.T.; Han, Z.; Kim, D.S. Artificial intelligence for the metaverse: A survey. Eng. Appl. Artif. Intell. 2023, 117, 105581. [CrossRef]
8. Yang, Q.; Zhao, Y.; Huang, H.; Xiong, Z.; Kang, J.; Zheng, Z. Fusing Blockchain and AI With Metaverse: A Survey. IEEE Open Journal of the Computer Society 2022, 3, 122–136. [CrossRef]
9. Maksymyuk, T.; Gazda, J.; Bugár, G.; Gazda, V.; Liyanage, M.; Dohler, M. Blockchain-Empowered Service Management for the Decentralized Metaverse of Things. IEEE Access 2022, 10, 99025–99037. [CrossRef]
10. Zhao, Y.; Jiang, J.; Chen, Y.; Liu, R.; Yang, Y.; Xue, X.; Chen, S. Metaverse: Perspectives from graphics, interactions and visualization. Visual Informatics 2022, 6, 56–67. [CrossRef]
11. Lv, Z.; Xie, S.; Li, Y.; Shamim Hossain, M.; El Saddik, A. Building the Metaverse by Digital Twins at All Scales, State, Relation. Virtual Reality & Intelligent Hardware 2022, 4, 459–470. [CrossRef]
12. Aloqaily, M.; Bouachir, O.; Karray, F.; Ridhawi, I.A.; Saddik, A.E. Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse. IEEE Consumer Electronics Magazine 2022, pp. 1–8. [CrossRef]
13. Kusuma, A.T.; Supangkat, S.H. Metaverse Fundamental Technologies for Smart City: A Literature Review. 2022 International Conference on ICT for Smart Society (ICISS), 2022, pp. 1–7. [CrossRef]
14. Musamih, A.; Dirir, A.; Yaqoob, I.; Salah, K.; Jayaraman, R.; Puthal, D. NFTs in Smart Cities: Vision, Applications, and Challenges. IEEE Consumer Electronics Magazine 2022, pp. 1–14. [CrossRef]
15. Bansal, G.; Rajgopal, K.; Chamola, V.; Xiong, Z.; Niyato, D. Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare. IEEE Access 2022, 10, 119914–119946. [CrossRef]
16. Almarzouqi, A.; Aburayya, A.; Salloum, S.A. Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach. IEEE Access 2022, 10, 43421–43434. [CrossRef]
17. Chengoden, R.; Victor, N.; Huynh-The, T.; Yenduri, G.; Jhaveri, R.H.; Alazab, M.; Bhattacharya, S.; Hegde, P.; Maddikunta, P.K.R.; Gadekallu, T.R. Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions. IEEE Access 2023, 11, 12765–12795. [CrossRef]
18. Wang, M.; Yu, H.; Bell, Z.; Chu, X. Constructing an Edu-Metaverse Ecosystem: A New and Innovative Framework. IEEE Transactions on Learning Technologies 2022, 15, 685–696. [CrossRef]
19. Suanpang, P.; Niamsorn, C.; Pothipassa, P.; Chunhapataragul, T.; Netwong, T.; Jermsittiparsert, K. Extensible Metaverse Implication for a Smart Tourism City. Sustainability 2022, 14. [CrossRef]
20. Gong, M.; Zhang, Y.; Gao, Y.; Qin, A.K.; Wu, Y.; Wang, S.; Zhang, Y. A Multi-Modal Vertical Federated Learning Framework Based on Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 2024, 19, 1826–1839. [CrossRef]
21. Yang, Q.; Liu, Y.; Chen, T.; Tong, Y. Federated Machine Learning: Concept and Applications. ACM Trans. Intell. Syst. Technol. 2019, 10. [CrossRef]
22. Li, P.; Zhang, Z.; Al-Sumaiti, A.S.; Werghi, N.; Yeun, C.Y. A Robust Adversary Detection-Deactivation Method for Metaverse-oriented Collaborative Deep Learning. IEEE Sensors Journal 2023, pp. 1–1. [CrossRef]
23. Hitaj, B.; Ateniese, G.; Perez-Cruz, F. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security; Association for Computing Machinery: New York, NY, USA, 2017; CCS ’17, p. 603–618. [CrossRef]
24. Chen, Z.; Wu, J.; Fu, A.; Su, M.; Deng, R.H. MP-CLF: An effective Model-Preserving Collaborative deep Learning Framework for mitigating data leakage under the GAN. Knowledge-Based Systems 2023, 270, 110527. [CrossRef]
25. Lyu, L.; Li, Y.; Nandakumar, K.; Yu, J.; Ma, X. How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning. IEEE Transactions on Dependable and Secure Computing 2022, 19, 1003–1017. [CrossRef]
26. Kuru, K. Management of geo-distributed intelligence: Deep Insight as a Service (DINSaaS) on Forged Cloud Platforms (FCP). Journal of Parallel and Distributed Computing 2021, 149, 103–118. [CrossRef]
27. Zhang, L.; Xu, J.; Vijayakumar, P.; Sharma, P.K.; Ghosh, U. Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System. IEEE Transactions on Network Science and Engineering 2023, 10, 2864–2880. [CrossRef]
28. Bos, J.W.; Lauter, K.; Loftus, J.; Naehrig, M. Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme. Cryptography and Coding; Stam, M., Ed.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2013; pp. 45–64.
29. Phong, L.T.; Aono, Y.; Hayashi, T.; Wang, L.; Moriai, S. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 2018, 13, 1333–1345. [CrossRef]
30. Zhou, C.; Ansari, N. Securing Federated Learning Enabled NWDAF Architecture With Partial Homomorphic Encryption. IEEE Networking Letters 2023, 5, 299–303. [CrossRef]
31. Lee, J.W.; Kang, H.; Lee, Y.; Choi, W.; Eom, J.; Deryabin, M.; Lee, E.; Lee, J.; Yoo, D.; Kim, Y.S.; No, J.S. Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network. IEEE Access 2022, 10, 30039–30054. [CrossRef]
32. Zhou, X.; Liang, W.; Ma, J.; Yan, Z.; Wang, K.I.K. 2D Federated Learning for Personalized Human Activity Recognition in Cyber-Physical-Social Systems. IEEE Transactions on Network Science and Engineering 2022, 9, 3934–3944. [CrossRef]
33. Huq, N.; Reyes, R.; Lin, P.; Swimmer, M. Cybersecurity Threats Against the Internet of Experiences. Trend Micro Research 2022.
34. Pooyandeh, M.; Han, K.J.; Sohn, I. Cybersecurity in the AI-Based Metaverse: A Survey. Applied Sciences 2022, 12. [CrossRef]
35. Huang, Y.; Li, Y.J.; Cai, Z. Security and Privacy in Metaverse: A Comprehensive Survey. Big Data Mining and Analytics 2023, 6, 234–247. [CrossRef]
36. Wang, Y.; Su, Z.; Zhang, N.; Xing, R.; Liu, D.; Luan, T.H.; Shen, X. A Survey on Metaverse: Fundamentals, Security, and Privacy. IEEE Communications Surveys & Tutorials 2022, pp. 1–1. [CrossRef]
37. Rostami, S.; Maier, M. The Metaverse and Beyond: Implementing Advanced Multiverse Realms With Smart Wearables. IEEE Access 2022, 10, 110796–110806. [CrossRef]
38. Kalla, A.; De Alwis, C.; Gur, G.; Gochhayat, S.P.; Liyanage, M.; Porambage, P. Emerging Directions for Blockchainized 6G. IEEE Consumer Electronics Magazine 2022, pp. 1–1. [CrossRef]
39. Ryu, J.; Son, S.; Lee, J.; Park, Y.; Park, Y. Design of Secure Mutual Authentication Scheme for Metaverse Environments Using Blockchain. IEEE Access 2022, 10, 98944–98958. [CrossRef]
40. Hemdan, E.E.D.; Mahmoud, A.S.A., BlockTwins: A Blockchain-Based Digital Twins Framework. In Blockchain Applications in IoT Ecosystem; Choudhury, T.; Khanna, A.; Toe, T.T.; Khurana, M.; Gia Nhu, N., Eds.; Springer International Publishing: Cham, 2021; pp. 177–186. [CrossRef]
41. Kwabena, O.A.; Qin, Z.; Zhuang, T.; Qin, Z. MSCryptoNet: Multi-Scheme Privacy-Preserving Deep Learning in Cloud Computing. IEEE Access 2019, 7, 29344–29354. [CrossRef]
42. Kim, H.; Park, J.; Bennis, M.; Kim, S.L. Blockchained On-Device Federated Learning. IEEE Communications Letters 2020, 24, 1279–1283. [CrossRef]
43. Chen, T.; Zhong, S. Privacy-Preserving Backpropagation Neural Network Learning. IEEE Transactions on Neural Networks 2009, 20, 1554–1564. [CrossRef]
44. Kuru, K.; Ansell, D. TCitySmartF: A Comprehensive Systematic Framework for Transforming Cities Into Smart Cities. IEEE Access 2020, 8, 18615–18644. [CrossRef]
45. Kuru, K.; Yetgin, H. Transformation to Advanced Mechatronics Systems Within New Industrial Revolution: A Novel Framework in Automation of Everything (AoE). IEEE Access 2019, 7, 41395–41415. [CrossRef]
46. Harrison, C.; Eckman, B.; Hamilton, R.; Hartswick, P.; Kalagnanam, J.; Paraszczak, J.; Williams, P. Foundations for Smarter Cities. IBM Journal of Research and Development 2010, 54, 1–16. [CrossRef]
47. Tang, F.; Chen, X.; Zhao, M.; Kato, N. The Roadmap of Communication and Networking in 6G for the Metaverse. IEEE Wireless Communications 2022, pp. 1–15. [CrossRef]
48. White, G.; Zink, A.; Codecá, L.; Clarke, S. A digital twin smart city for citizen feedback. Cities 2021, 110, 103064. [CrossRef]
49. El Saddik, A. Digital Twins: The Convergence of Multimedia Technologies. IEEE MultiMedia 2018, 25, 87–92. [CrossRef]
50. Laamarti, F.; Badawi, H.F.; Ding, Y.; Arafsha, F.; Hafidh, B.; Saddik, A.E. An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities. IEEE Access 2020, 8, 105950–105961. [CrossRef]
51. Kuru, K. Conceptualisation of Human-on-the-Loop Haptic Teleoperation With Fully Autonomous Self-Driving Vehicles in the Urban Environment. IEEE Open Journal of Intelligent Transportation Systems 2021, 2, 448–469. [CrossRef]
52. Kuru, K.; Khan, W. A Framework for the Synergistic Integration of Fully Autonomous Ground Vehicles With Smart City. IEEE Access 2021, 9, 923–948. [CrossRef]
53. Kuru, K.; Worthington, S.; Ansell, D.; Pinder, J.M.; Sujit, A.; Jon Watkinson, B.; Vinning, K.; Moore, L.; Gilbert, C.; Jones, D.; others. AITL-WING-HITL: Telemanipulation of autonomous drones using digital twins of aerial traffic interfaced with WING. IEEE Access 2023, 11.
54. Lv, Z.; Qiao, L.; Li, Y.; Yuan, Y.; Wang, F.Y. BlockNet: Beyond reliable spatial Digital Twins to Parallel Metaverse. Patterns 2022, 3, 100468. [CrossRef]
55. Hudson-Smith, A.; Signorelli, V. Digital Innovation for Data Visualisations in Participatory Urban Planning, 2022.
56. Wu, Y.; Zhang, K.; Zhang, Y. Digital Twin Networks: A Survey. IEEE Internet of Things Journal 2021, 8, 13789–13804. [CrossRef]
57. Arslan, M. Metaverse’in Akıllı Kent Hizmetlerine Etkisi. Akademik Araştırmalar ve Çalışmalar Dergisi (AKAD) 2022, 14, 292 – 303. [CrossRef]
58. Kuru, K. Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework. IEEE Access 2021, 9, 6571–6595. [CrossRef]
59. Pérez, G.O.; Ebrahimzadeh, A.; Maier, M.; Hernández, J.A.; López, D.L.; Veiga, M.F. Decentralized Coordination of Converged Tactile Internet and MEC Services in H-CRAN Fiber Wireless Networks. Journal of Lightwave Technology 2020, 38, 4935–4947. [CrossRef]
60. Chang, L.; Zhang, Z.; Li, P.; Xi, S.; Guo, W.; Shen, Y.; Xiong, Z.; Kang, J.; Niyato, D.; Wu, X.Q.Y. 6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions. Journal of Communications and Information Networks 2022, 7, 107. [CrossRef]
61. Xie, J.; Tang, H.; Huang, T.; Yu, F.R.; Xie, R.; Liu, J.; Liu, Y. A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges. IEEE Communications Surveys Tutorials 2019, 21, 2794–2830. [CrossRef]
62. Xu, X.; Cizmeci, B.; Schuwerk, C.; Steinbach, E. Model-Mediated Teleoperation: Toward Stable and Transparent Teleoperation Systems. IEEE Access 2016, 4, 425–449. [CrossRef]
63. Ebrahimzadeh, A.; Maier, M. Delay-Constrained Teleoperation Task Scheduling and Assignment for Human+Machine Hybrid Activities Over FiWi Enhanced Networks. IEEE Transactions on Network and Service Management 2019, 16, 1840–1854. [CrossRef]
64. Wang, D.; Ohnishi, K.; Xu, W. Novel Emerging Sensing, Actuation, and Control Techniques for Haptic Interaction and Teleoperation. IEEE Transactions on Industrial Electronics 2020, 67, 624–626. [CrossRef]
65. .
66. S. Punla, C.; C. Farro, R. Are we there yet?: An analysis of the competencies of BEED graduates of BPSU-DC. International Multidisciplinary Research Journal 2022, 4, 50–59.
67. Cui, L.; Liu, J. Virtual Human: A Comprehensive Survey on Academic and Applications. IEEE Access 2023, 11, 123830–123845. [CrossRef]
68. Vladimirov, I.; Nenova, M.; Nikolova, D.; Terneva, Z. Security and Privacy Protection Obstacles with 3D Reconstructed Models of People in Applications and the Metaverse: A Survey. 2022 57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), 2022, pp. 1–4. [CrossRef]
69. Frenkel, S.; Browning, K. The Metaverse’s Dark Side: Here Come Harassment and Assaults, 2021.
70. Sharma, V. Introducing a personal boundary for horizon worlds and venues, 2022.
71. Wiederhold, B.K. Metaverse Games: Game Changer for Healthcare? Cyberpsychology, Behavior, and Social Networking 2022, 25, 267–269. [CrossRef] [PubMed]
72. TheWachowskis. The Relationship Between Body, Brain, and Mind, 2023.
73. Eckhoff, D.; Wagner, I. Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions. IEEE Communications Surveys Tutorials 2018, 20, 489–516. [CrossRef]
74. Yau, S.S.; An, H.G.; Buduru, A.B. An Approach to Data Confidentiality Protection in Cloud Environments. International Journal of Web Services Research 2012, 9, 67–83. [CrossRef]
75. Podschwadt, R.; Takabi, D.; Hu, P.; Rafiei, M.H.; Cai, Z. A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption. IEEE Access 2022, 10, 117477–117500. [CrossRef]
76. Dwork, C.; McSherry, F.; Nissim, K.; Smith, A. Calibrating Noise to Sensitivity in Private Data Analysis. Theory of Cryptography; Halevi, S.; Rabin, T., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg, 2006; pp. 265–284.
77. Latif, S.; Ali, H.S.; Usama, M.; Rana, R.; Schuller, B.; Qadir, J. AI-Based Emotion Recognition: Promise, Peril, and Prescriptions for Prosocial Path, 2022, [arXiv:cs.HC/2211.07290].
78. McStay, A. Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy. Big Data & Society 2020, 7, 2053951720904386. [CrossRef]
79. Peng, X.B.; Abbeel, P.; Levine, S.; van de Panne, M. DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills. ACM Trans. Graph. 2018, 37. [CrossRef]
80. Duan, S.; Zhao, F.; Yang, H.; Hong, J.; Shi, Q.; Lei, W.; Wu, J. A Pathway into Metaverse: Gesture Recognition Enabled by Wearable Resistive Sensors. Advanced Sensor Research 2023, 2, 2200054. Available online: https://onlinelibrary.wiley.com/doi/pdf/10.1002/adsr.202200054. [CrossRef]
81. Zhang, Z.; Song, X.; Liu, L.; Yin, J.; Wang, Y.; Lan, D. Recent Advances in Blockchain and Artificial Intelligence Integration: Feasibility Analysis, Research Issues, Applications, Challenges, and Future Work. Security and Communication Networks 2021, 2021, 1–15. [CrossRef]
82. Falchuk, B.; Loeb, S.; Neff, R. The Social Metaverse: Battle for Privacy. IEEE Technology and Society Magazine 2018, 37, 52–61. [CrossRef]
83. Huang, Y.; Zhu, Y.; Qiao, X.; Su, X.; Dustdar, S.; Zhang, P. Toward Holographic Video Communications: A Promising AI-Driven Solution. IEEE Communications Magazine 2022, 60, 82–88. [CrossRef]
84. Chowdhury, M.Z.; Shahjalal, M.; Ahmed, S.; Jang, Y.M. 6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions. IEEE Open Journal of the Communications Society 2020, 1, 957–975. [CrossRef]
85. Wilson, P. State of smart cities in UK and beyond. IET Smart Cities 2019, 1, 19–22. [CrossRef]
86. Sun, Y.; Song, H.; Jara, A.J.; Bie, R. Internet of Things and Big Data Analytics for Smart and Connected Communities. IEEE Access 2016, 4, 766–773. [CrossRef]
87. Kiestra, P. Safe Cities Index 2019: Urban security and resilience in an interconnected world, 2019.
88. Neirotti, P.; Marco, A.D.; Cagliano, A.C.; Mangano, G.; Scorrano, F. Current trends in Smart City initiatives: Some stylised facts. Cities 2014, 38, 25 – 36. [CrossRef]