Method for Synthesising Resilient Architectures of Virtual and Augmented Reality Systems
DOI:
https://doi.org/10.32515/2664-262X.2026.13(44).59-72Keywords:
virtual reality (VR), resilience, VR architecture, fault tolerance, error mitigation, system reliabilityAbstract
The article considers the problem of ensuring the stability of augmented (AR) and virtual (VR) reality systems, which are characterized by high sensitivity to performance violations and hardware and software failures. Most existing approaches focus on the analysis of fixed architectures, leaving open the issue of automated synthesis of systems that are stable by design.
A new method for analytical synthesis of AR/VR architectures is proposed, which is based on a formal model that links a 20-dimensional vector of mitigation parameters with seven key stability metrics: reliability, availability, fault tolerance, integrity, recovery time, performance stability, and user safety. The design problem is formulated as a multi-criteria optimization problem, for the solution of which a genetic algorithm is used.
Experimental validation conducted in the Simulink environment confirmed the effectiveness of the method. The results showed that the synthesized architecture provides an improvement in the overall resilience index by 19.4% compared to the base configuration under strict operational constraints. In particular, a significant increase in the availability indicators (+82.2%) and recovery time (+39.9%) was achieved. The proposed approach allows for quantitative assessment and optimization of architectural solutions at the design stage.
References
Список літератури
1. Resilience engineering. *Wikipedia*. URL: [https://en.wikipedia.org/wiki/Resilience_engineering]
(https://en.wikipedia.org/wiki/Resilience_engineering) (дата звернення: 13.02.2026).
2. System Resilience. *SEBoK Wiki*. URL: [https://sebokwiki.org/wiki/System_Resilience] (https://sebokwiki.org/wiki/System_Resilience) (дата звернення: 13.02.2026).
3. The k-out-of-n System Model. *Wiley*. URL: [https://catalogimages.wiley.com/images/db/
pdf/047139761X.07.pdf](https://catalogimages.wiley.com/images/db/pdf/047139761X.07.pdf) (дата звернення: 13.02.2026).
4. RBDs and Analytical System Reliability. *ReliaSoft*. URL: [https://help.reliasoft.com/reference/ system_analysis/sa/rbds_and_analytical_system_reliability.html](https://help.reliasoft.com/reference/system_analysis/sa/rbds_and_analytical_system_reliability.html) (дата звернення: 13.02.2026).
5. Reliability analysis of a k-out-of-n:F system under a linear model. Ann. Inst. Stat. Math. URL: [https://www.ism.ac.jp/editsec/aism/71/0537.pdf](https://www.ism.ac.jp/editsec/aism/71/0537.pdf) (дата звернення: 13.02.2026).
6. Availability. *Wikipedia*. URL: [https://en.wikipedia.org/wiki/Availability] (https://en.wikipedia.org/wiki/Availability) (дата звернення: 13.02.2026).
7. k-out-of-n Systems. *ReliaSoft*. URL: [https://help.reliasoft.com/articles/content/reference_examples/
blocksim/k-out-of-n_systems.html](https://help.reliasoft.com/articles/content/reference_examples/
blocksim/k-out-of-n_systems.html) (дата звернення: 13.02.2026).
8. Multi-Blockchain-Based IoT Data Processing Techniques for Big Data Integrity Verification. *Sensors*. 2021. Vol. 21, no. 10. Art. no. 3515. URL: [https://www.mdpi.com/1424-8220/21/10/3515](https://www.mdpi.com/1424-8220/21/10/3515) (дата звернення: 13.02.2026).
9. Recovery Time Objective (RTO). *NIST CSRC*. URL: [https://csrc.nist.gov/glossary/term/recovery_time_objective](https://csrc.nist.gov/glossary/term/recovery_time_objective) (дата звернення: 13.02.2026).
10. Mean time to recovery. *Wikipedia*. URL: [https://en.wikipedia.org/wiki/Mean_time_to_recovery](https://en.wikipedia.org/wiki/Mean_time_to_recovery) (дата звернення: 13.02.2026).
11. Measuring System Visual Latency through Cognitive Tests. *Proc. IEEE VR 2020*. URL: [https://www.microsoft.com/en-us/research/wp-content/uploads/2020/01/ieee_vr_2020___latency.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2020/01/ieee_vr_2020___latency.pdf) (дата звернення: 13.02.2026).
12. Measuring motion-to-photon latency for sensorimotor experiments with VR. *PLOS ONE*. 2022. URL: [https://pmc.ncbi.nlm.nih.gov/articles/PMC10616216/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10616216/) (дата звернення: 13.02.2026).
13. Investigating guardian awareness techniques to promote VR user safety. *SMU*. URL: [https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=9024&context=sis_research](https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=9024&context=sis_research) (дата звернення: 13.02.2026).
14. Exploring Metaphorical Transformations of a Safety Boundary Wall for VR. *Applied Sciences*. URL: [https://pmc.ncbi.nlm.nih.gov/articles/PMC11125283/](https://pmc.ncbi.nlm.nih.gov/articles/PMC11125283/) (дата звернення: 13.02.2026).
15. Communication, Computing and Caching for Mobile VR Delivery: Modeling and Trade-off / Sun Y. et al. *arXiv preprint arXiv:1804.10335*. 2018. URL: [https://arxiv.org/abs/1804.10335](https://arxiv.org/abs/1804.10335) (дата звернення: 13.02.2026).
16. Zhang X., Xia Y., Ali M. A Smartphone Thermal Temperature Analysis for Virtual and Augmented Reality : National Science Foundation Technical Report. 2020. URL: [https://par.nsf.gov/servlets/purl/10219090](https://par.nsf.gov/servlets/purl/10219090) (дата звернення: 13.02.2026).
17. Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation / Gomez J. et al. *arXiv preprint arXiv:2203.07474*. 2022. URL: [https://arxiv.org/abs/2203.07474](https://arxiv.org/abs/2203.07474) (дата звернення: 13.02.2026).
18. Augmented Reality and Virtual Reality Displays: Emerging Technologies and Future Perspectives / Xiong J. et al. *Light: Science & Applications*. 2021. Vol. 10, no. 1. Art. no. 215. URL: [https://www.nature.com/articles/s41377-021-00658-8](https://www.nature.com/articles/s41377-021-00658-8) (дата звернення: 13.02.2026).
19. Hazarika A. Towards an Evolved Immersive Experience: Exploring 5G. *Sensors*. 2023. Vol. 23, no. 7. P. 3682. URL: [https://www.mdpi.com/1424-8220/23/7/3682](https://www.mdpi.com/1424-8220/23/7/3682) (дата звернення: 13.02.2026).
20. Transparent Fault Tolerance for Stateful Applications in Kubernetes with Checkpoint/Restore / Foerster K. T. et al. *Proc. IEEE SRDS 2023*. URL: [https://ktfoerster.github.io/paper/2023-srds.pdf](https://ktfoerster.github.io/paper/2023-srds.pdf) (дата звернення: 13.02.2026).
21. Zhao B., Guo S., Liu X. A Multipath Scheduler Based on Cross-Layer Information for Cloud VR in 5G Edge Networks. *Computer Networks*. 2024. Vol. 244. P. 110333. DOI: [https://doi.org/10.1016/j.comnet.2024.110333](https://doi.org/10.1016/j.comnet.2024.110333).
22. Kelkkanen V. Evaluation and Reduction of Temporal Issues in Remote VR : Master’s Thesis / Univ. of Oulu. Oulu, Finland, 2023. URL: [https://www.diva-portal.org/smash/get/diva2:1744902/FULLTEXT02.pdf](https://www.diva-portal.org/smash/get/diva2:1744902/FULLTEXT02.pdf) (дата звернення: 13.02.2026).
23. Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning / Yang P. et al. *arXiv preprint arXiv:2107.01001*. 2021. URL: [https://arxiv.org/abs/2107.01001](https://arxiv.org/abs/2107.01001) (дата звернення: 13.02.2026).
24. Exploring Metaphorical Transformations of a Safety Boundary Wall for VR / Qin H. et al. *Applied Sciences*. 2024. Vol. 14, no. 6. Art. no. 2520. URL: [https://pmc.ncbi.nlm.nih.gov/articles/PMC11125283](https://pmc.ncbi.nlm.nih.gov/articles/PMC11125283) (дата звернення: 13.02.2026).
25. Recent Advances in Wearable Thermal Devices for Virtual and Augmented Reality / Park M. et al. *Micromachines*. 2025. Vol. 16, no. 3. Art. no. 338. URL: [https://pmc.ncbi.nlm.nih.gov/articles/PMC12029164](https://pmc.ncbi.nlm.nih.gov/articles/PMC12029164) (дата звернення: 13.02.2026).
26. Weech S., Kenny S., Barnett-Cowan M. Presence and Cybersickness in Virtual Reality Are Negatively Related: A Review. *Frontiers in Psychology*. 2019. Vol. 10. P. 158. URL: [https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00158/full](https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00158/full) (дата звернення: 13.02.2026).
27. Lysenko S., Kachur A. A Resilience Assurance Process Model for Enhancing Virtual Reality Architectural Design. *Proc. 14th Int. Conf. on Dependable Systems, Services and Technologies (DESSERT)*. Athens, Greece, 2024. P. 1–9. DOI: [https://doi.org/10.1109/DESSERT65323.2024.11122129](https://doi.org/10.1109/DESSERT65323.2024.11122129).
28. A Technique for Detection of Bots which are Using Polymorphic Code / Pomorova O. et al. *Communications in Computer and Information Science*. 2014. Vol. 431. P. 265–276.
29. Botnet Detection Technique for Corporate Area Network / Savenko O. et al. *Proc. 7th IEEE Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2013)*. Berlin, Germany, 2013. P. 363–368.
30. Method and Rules for Determining the Next Centralization Option in Multicomputer System Architecture / Kashtalian A. et al. *International Journal of Computing*. 2025. Vol. 24, no. 1. P. 35–51. DOI: 10.47839/ijc.24.1.3875.
31. Evaluation Criteria of Centralization Options in the Architecture of Multicomputer Systems with Traps and Baits / Kashtalian A. et al. *Radioelectronic and Computer Systems*. 2025. No. 1. P. 264–297. DOI: 10.32020/reks.2025.1.18.
32. Multi-Computer Malware Detection Systems with Metamorphic Functionality / Kashtalian A. et al. *Radioelectronic and Computer Systems*. 2024. No. 1. P. 152–175. DOI: 10.32020/reks.2024.1.13.
33. A Botnet Detection Approach Based on the Clonal Selection Algorithm / Lysenko S. et al. *Proc. 2018 IEEE 9th Int. Conf. on Dependable Systems, Services and Technologies (DeSSerT 2018)*. Kyiv, Ukraine, 2018. P. 424–428.
34. A Technique for the Botnet Detection Based on DNS-Traffic Analysis / Pomorova O. et al. *Proc. 22nd Int. Conf. Computer Networks (CN 2015)*. Brunów, Poland, 2015. Vol. 522. P. 127–138.
35. UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception / Di Geronimo L. et al. *Proc. CHI*. 2020. P. 1–14. DOI: 10.1145/3313831.3376600.
36. Alshehri A., Alrehili K., Alhumaid F., Alessa A. Exploring the Privacy Concerns of Bystanders in Smart Homes. *Proc. Priv. Enhancing Technol. (PoPETs)*. 2022. Vol. 2022, no. 4. P. 253–270. DOI: 10.56553/popets-2022-0064.
37. Lin H., Bergmann N. W. IoT Privacy and Security Challenges for Smart Home Environments. *Information*. 2016. Vol. 7, no. 3. Art. 44. DOI: 10.3390/info7030044.
38. Wang Q., Cai Z., Li R., Fang X. A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis. *Sensors*. 2020. Vol. 20, no. 24. Art. 7030. DOI: 10.3390/s20247030.
39. Yao Y., Zimmermann T., Chin A., Schaub F. Privacy Perceptions and Designs of Bystanders in Smart Homes. *Proc. ACM Hum.–Comput. Interact. (CSCW)*. 2019. Vol. 3, no. CSCW. P. 1–24. DOI: 10.1145/3359161.
40. Bystander Privacy in Smart Homes: A Systematic Review of Concerns and Solutions / Saqib E. et al. *ACM Trans. Comput.–Hum. Interact. (TOCHI)*. 2025. P. 1–?. DOI: 10.1145/3731755.
41. Marky K., Prange S., Krell F., Mühlhäuser M., Alt F. ‘You Just Can’t Know About Everything’: Privacy Perceptions of Smart Home Visitors. *Proc. MUM*. 2020. P. 1–13. DOI: 10.1145/3428361.3428464.
42. Android Permissions: User Attention, Comprehension, and Behavior / Felt A. P. et al. *Proc. SOUPS@CHI*. 2012. P. 3–14. DOI: 10.1145/2335356.2335360.
43. Zimmermann V., Gerber P., Marky K., Böck L., Kirchbuchner F. Assessing Users’ Privacy and Security Concerns of Smart Home Technologies. *i-com*. 2019. Vol. 18, no. 3. P. 197–216. DOI: 10.1515/icom-2019-0015.
44. Alshamsi O., Shaalan K., Butt U. Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach. *Information*. 2024. Vol. 15, no. 10. Art. 631. DOI: 10.3390/info15100631.
45. Ogonji M. M., Okeyo G., Wafula J. M. A Survey on Privacy and Security of Internet of Things. *Computer Science Review*. 2020. Vol. 38. Art. 100312. DOI: 10.1016/j.cosrev.2020.100312.
References
1. Resilience engineering," Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Resilience_engineering
2. "System Resilience," SEBoK Wiki. [Online]. Available: https://sebokwiki.org/wiki/System_Resilience
3. "The k-out-of-n System Model," Wiley. [Online]. Available: https://catalogimages.wiley.com/images/db/pdf/047139761X.07.pdf
4. "RBDs and Analytical System Reliability," ReliaSoft. [Online]. Available: https://help.reliasoft.com/reference/system_analysis/sa/rbds_and_analytical_system_reliability.html
5. "Reliability analysis of a k-out-of-n:F system under a linear model," Ann. Inst. Stat. Math. [Online]. Available: https://www.ism.ac.jp/editsec/aism/71/0537.pdf
6. "Availability," Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Availability
7. "k-out-of-n Systems," ReliaSoft. [Online]. Available: https://help.reliasoft.com/articles/content/reference_examples/blocksim/k-out-of-n_systems.html
8. "Multi-Blockchain-Based IoT Data Processing Techniques for Big Data Integrity Verification," Sensors, vol. 21, no. 10, 2021, Art. no. 3515. [Online]. Available: https://www.mdpi.com/1424-8220/21/10/3515 DOI: https://doi.org/10.3390/s21103515
9. "Recovery Time Objective (RTO)," NIST CSRC. [Online]. Available: https://csrc.nist.gov/glossary/term/recovery_time_objective
10. ["Mean time to recovery," Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Mean_time_to_recovery
11. "Measuring System Visual Latency through Cognitive Tests," in Proc. IEEE VR 2020. [Online]. Available: https://www.microsoft.com/en-us/research/wp-content/uploads/2020/01/ieee_vr_2020___latency.pdf
12. "Measuring motion-to-photon latency for sensorimotor experiments with VR," PLOS ONE, 2022. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10616216/
13. "Investigating guardian awareness techniques to promote VR user safety," SMU. [Online]. Available: https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=9024&context=sis_research
14. "Exploring Metaphorical Transformations of a Safety Boundary Wall for VR," Applied Sciences. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11125283/
15. Y. Sun, Z. Jiang, C. Xu, S. Zhou, and Z. Niu, “Communication, Computing and Caching for Mobile VR Delivery: Modeling and Trade-off,” arXiv preprint arXiv:1804.10335, 2018. [Online]. Available: https://arxiv.org/abs/1804.10335
16. X. Zhang, Y. Xia, and M. Ali, “A Smartphone Thermal Temperature Analysis for Virtual and Augmented Reality,” National Science Foundation Technical Report, 2020. [Online]. Available: https://par.nsf.gov/servlets/purl/10219090 DOI: https://doi.org/10.1109/AIVR50618.2020.00061
17. J. Gomez, S. Raghunathan, and K. Chowdhury, “Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation,” arXiv preprint arXiv:2203.07474, 2022. [Online]. Available: https://arxiv.org/abs/2203.07474
18. J. Xiong, Y. Li, K. Liu, X. Liu, and Z. Liu, “Augmented Reality and Virtual Reality Displays: Emerging Technologies and Future Perspectives,” Light: Science & Applications, vol. 10, no. 1, 2021, Art. no. 215. [Online]. Available: https://www.nature.com/articles/s41377-021-00658-8 DOI: https://doi.org/10.1038/s41377-021-00658-8
19. A. Hazarika, “Towards an Evolved Immersive Experience: Exploring 5G,” Sensors, vol. 23, no. 7, p. 3682, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/7/3682 DOI: https://doi.org/10.3390/s23073682
20. K. T. Foerster, H. Schmidt, and D. P. van der Meer, “Transparent Fault Tolerance for Stateful Applications in Kubernetes with Checkpoint/Restore,” in Proc. IEEE SRDS 2023. [Online]. Available: https://ktfoerster.github.io/paper/2023-srds.pdf
21. B. Zhao, S. Guo, and X. Liu, “A Multipath Scheduler Based on Cross-Layer Information for Cloud VR in 5G Edge Networks,” Computer Networks, vol. 244, p. 110333, 2024. [Online]. Available: https://doi.org/10.1016/j.comnet.2024.110333 DOI: https://doi.org/10.1016/j.comnet.2024.110333
22. V. Kelkkanen, “Evaluation and Reduction of Temporal Issues in Remote VR,” Master’s Thesis, Univ. of Oulu, Oulu, Finland, 2023. [Online]. Available: https://www.diva-portal.org/smash/get/diva2:1744902/FULLTEXT02.pdf
23. P. Yang, Y. Zhao, J. Wu, and J. Zhang, “Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning,” arXiv preprint arXiv:2107.01001, 2021. [Online]. Available: https://arxiv.org/abs/2107.01001
24. H. Qin, Z. Sun, S. Yao, and C. Zhang, “Exploring Metaphorical Transformations of a Safety Boundary Wall for VR,” Applied Sciences, vol. 14, no. 6, 2024, Art. no. 2520. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11125283
25. M. Park, J. Kim, and D. Kang, “Recent Advances in Wearable Thermal Devices for Virtual and Augmented Reality,” Micromachines, vol. 16, no. 3, 2025, Art. no. 338. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC12029164 DOI: https://doi.org/10.3390/mi16040383
26. S. Weech, S. Kenny, and M. Barnett-Cowan, “Presence and Cybersickness in Virtual Reality Are Negatively Related: A Review,” Frontiers in Psychology, vol. 10, p. 158, 2019. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00158/full DOI: https://doi.org/10.3389/fpsyg.2019.00158
27. S. Lysenko and A. Kachur, “A Resilience Assurance Process Model for Enhancing Virtual Reality Architectural Design,” Proc. 14th Int. Conf. on Dependable Systems, Services and Technologies (DESSERT), Athens, Greece, 2024, pp. 1–9. doi: https://doi.org/10.1109/DESSERT65323.2024.11122129 DOI: https://doi.org/10.1109/DESSERT65323.2024.11122129
28. [O. Pomorova, O. Savenko, S. Lysenko, A. Kryshchuk, and A. Nicheporuk, “A Technique for Detection of Bots which are Using Polymorphic Code,” Communications in Computer and Information Science, vol. 431, pp. 265–276, 2014. ISSN 1865-0929. DOI: https://doi.org/10.1007/978-3-319-07941-7_27
29. O. Savenko, S. Lysenko, A. Kryshchuk, and Y. Klots, “Botnet Detection Technique for Corporate Area Network,” in Proc. 7th IEEE Int. Conf. on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2013), Berlin, Germany, Sept. 12–14, 2013, pp. 363–368. ISBN 978-1-4799-1426-5. DOI: https://doi.org/10.1109/IDAACS.2013.6662707
30. A. Kashtalian, S. Lysenko, T. Kysil, A. Sachenko, O. Savenko, and B. Savenko, “Method and Rules for Determining the Next Centralization Option in Multicomputer System Architecture,” International Journal of Computing, vol. 24, no. 1, pp. 35–51, 2025. doi: 10.47839/ijc.24.1.3875. DOI: https://doi.org/10.47839/ijc.24.1.3875
31. A. Kashtalian, S. Lysenko, A. Sachenko, B. Savenko, O. Savenko, and A. Nicheporuk, “Evaluation Criteria of Centralization Options in the Architecture of Multicomputer Systems with Traps and Baits,” Radioelectronic and Computer Systems, no. 1, pp. 264–297, 2025. doi: 10.32620/reks.2025.1.18. DOI: https://doi.org/10.32620/reks.2025.1.18
32. A. Kashtalian, S. Lysenko, O. Savenko, A. Nicheporuk, T. Sochor, and V. Avsiyevych, “Multi-Computer Malware Detection Systems with Metamorphic Functionality,” Radioelectronic and Computer Systems, no. 1, pp. 152–175, 2024. doi: 10.32620/reks.2024.1.13. DOI: https://doi.org/10.32620/reks.2024.1.13
33. [S. Lysenko, K. Bobrovnikova, and O. Savenko, “A Botnet Detection Approach Based on the Clonal Selection Algorithm,” in Proc. 2018 IEEE 9th Int. Conf. on Dependable Systems, Services and Technologies (DeSSerT 2018), Kyiv, Ukraine, May 24–27, 2018, pp. 424–428. DOI: https://doi.org/10.1109/DESSERT.2018.8409171
34. O. Pomorova, O. Savenko, S. Lysenko, A. Kryshchuk, and K. Bobrovnikova, “A Technique for the Botnet Detection Based on DNS-Traffic Analysis,” in Proc. 22nd Int. Conf. Computer Networks (CN 2015), Brunów, Poland, June 16–19, 2015, vol. 522, pp. 127–138. DOI: https://doi.org/10.1007/978-3-319-19419-6_12
35. L. Di Geronimo, L. Braz, E. Fregnan, F. Palomba, and A. Bacchelli, “UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception,” in Proc. CHI, 2020, pp. 1–14. doi: 10.1145/3313831.3376600. DOI: https://doi.org/10.1145/3313831.3376600
36. A. Alshehri, K. Alrehili, F. Alhumaid, and A. Alessa, “Exploring the Privacy Concerns of Bystanders in Smart Homes,” Proc. Priv. Enhancing Technol. (PoPETs), vol. 2022, no. 4, pp. 253–270, 2022. doi: 10.56553/popets-2022-0064. (Open PDF available.) DOI: https://doi.org/10.56553/popets-2022-0064
37. H. Lin and N. W. Bergmann, “IoT Privacy and Security Challenges for Smart Home Environments,” Information, vol. 7, no. 3, art. 44, 2016. doi: 10.3390/info7030044. DOI: https://doi.org/10.3390/info7030044
38. Q. Wang, Z. Cai, R. Li, and X. Fang, “A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis,” Sensors, vol. 20, no. 24, art. 7030, 2020. doi: 10.3390/s20247030. DOI: https://doi.org/10.3390/s20247030
39. Y. Yao, T. Zimmermann, A. Chin, and F. Schaub, “Privacy Perceptions and Designs of Bystanders in Smart Homes,” Proc. ACM Hum.–Comput. Interact. (CSCW), vol. 3, no. CSCW, pp. 1–24, 2019. doi: 10.1145/3359161. DOI: https://doi.org/10.1145/3359161
40. E. Saqib, S. He, J. Choy, R. Abu-Salma, J. Such, J. Bernd, and M. Javed, “Bystander Privacy in Smart Homes: A Systematic Review of Concerns and Solutions,” ACM Trans. Comput.–Hum. Interact. (TOCHI), pp. 1–?, 2025. doi: 10.1145/3731755. DOI: https://doi.org/10.1145/3731755
41. K. Marky, S. Prange, F. Krell, M. Mühlhäuser, and F. Alt, “‘You Just Can’t Know About Everything’: Privacy Perceptions of Smart Home Visitors,” in Proc. MUM, 2020, pp. 1–13. doi: 10.1145/3428361.3428464. (Open PDF available.) DOI: https://doi.org/10.1145/3428361.3428464
42. A. P. Felt, E. Ha, S. Egelman, A. Haney, E. Chin, and D. Wagner, “Android Permissions: User Attention, Comprehension, and Behavior,” in Proc. SOUPS@CHI, 2012, pp. 3–14. doi: 10.1145/2335356.2335360. DOI: https://doi.org/10.1145/2335356.2335360
43. V. Zimmermann, P. Gerber, K. Marky, L. Böck, and F. Kirchbuchner, “Assessing Users’ Privacy and Security Concerns of Smart Home Technologies,” i-com, vol. 18, no. 3, pp. 197–216, 2019. doi: 10.1515/icom-2019-0015. DOI: https://doi.org/10.1515/icom-2019-0015
44. O. Alshamsi, K. Shaalan, and U. Butt, “Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach,” Information, vol. 15, no. 10, art. 631, 2024. doi: 10.3390/info15100631. DOI: https://doi.org/10.3390/info15100631
45. M. M. Ogonji, G. Okeyo, and J. M. Wafula, “A Survey on Privacy and Security of Internet of Things,” Computer Science Review, vol. 38, art. 100312, 2020. doi: 10.1016/j.cosrev.2020.100312. DOI: https://doi.org/10.1016/j.cosrev.2020.100312
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sergii Lysenko, Artem Kachur, Liudmyla Koretska
This work is licensed under a Creative Commons Attribution 4.0 International License.
