KO‘P BULUTLI MUHITDAGI IOT QURILMALARI UCHUN BLOKCHEYNINTEGRATSIYALASHGAN VA DRL-AGENTIGA ASOSLANGAN XAVFSIZ VA SAMARALI ARXITEKTURA
DOI:
https://doi.org/10.65164/tcnrzq59Ключевые слова:
Internet of Things (IoT); ko‘p bulutli hisoblash (multi-cloud computing); blokcheyn; chuqur mustahkamlab o‘rganish (DRL); resurslarni optimallashtirish; ma'lumotlar xavfsizligi; aqlli shartnomalar; past kechikish; kiber-fizik tizimlarАннотация
Ushbu tadqiqot ko‘p bulutli IoT tizimlarida xavfsizlik va samaradorlik o‘rtasidagi muvozanatni ta'minlash maqsadida blokcheyn hamda chuqur mustahkamlab o‘rganish (DRL) texnologiyalariga asoslangan yangi arxitekturani taklif etadi. Ishlab chiqilgan DRL agenti tarmoq sharoitlariga avtonom moslashib, xizmatlarni joylashtirishda kechikish, energiya va xarajatlarni bir vaqtda optimallashtiradi. Ruxsatga asoslangan blokcheyn esa tranzaksiyalar shaffofligi va kiberhujumlardan himoyani kafolatlaydi. NS-3 muhitidagi simulyatsiyalar taklif etilgan yondashuv statik algoritmlarga nisbatan kechikishni 27% ga va energiya sarfini 18% ga qisqartirishini tasdiqladi. Mazkur yechim V2X va IIoT kabi real vaqt tizimlarida ishonchli ma'lumot almashinuvini ta'minlash uchun samarali vosita hisoblanadi
Библиографические ссылки
1. M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, "Deep learning for cyber security
intrusion detection: A survey," Journal of Information Security and Applications, vol. 50, art.
102419, 2020.
2. N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y.-C. Liang, and D. I. Kim,
"Applications of Deep Reinforcement Learning in Communications and Networking: A Survey,"
IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3133-3175, Fourthquarter 2019.
3. N. Z. Aitzhan and D. Svetinovic, "Security and Privacy in Decentralized Energy Trading Through
Multi-Signatures, Blockchain and Anonymous Messaging Streams," IEEE Transactions on
Dependable and Secure Computing, vol. 15, no. 5, pp. 840-852, Sept.-Oct. 2018.
4. S. Shresthamali, M. Kondo, and H. Nakamura, "Multi-Objective Resource Scheduling for IoT
Systems Using Reinforcement Learning," Journal of Low Power Electronics and Applications,
vol. 12, no. 4, art. 53, 2022.
5. Y. Zhang, M. R. M. N. K. Ratnayake, S. K. H. Islam, and M. S. A. E. Kabir, "A Survey on Deep
Reinforcement Learning for Resource Allocation in Edge Computing," IEEE Access, vol. 10, pp.
34866-34891, 2022.
