AVTONOM IOT TUGUNLARIDA ENERGIYA HOLATI VA NOSOZLIK XAVFIGA MOS UCH REJIMLI DIAGNOSTIKA BOSHQARUVI
Keywords:
Kalit so‘zlar: avtonom IoT tuguni, energiya holati, nosozlik xavfi, adaptiv diagnostika, edge hisoblash, Monte-Karlo.Abstract
Annotatsiya. Avtonom IoT tugunlarida diagnostikani doimiy yuqori jadallikda bajarish nosozliklarni tez aniqlash imkonini beradi, biroq batareya energiyasini sezilarli sarflaydi. Ushbu tezisda batareya holati va nosozlik xavfini birgalikda hisobga olib, diagnostikani tejamkor, muvozanatli va kuchaytirilgan rejimlar orasida boshqarish yondashuvi ko‘rib chiqildi. 100 ta 30 kunlik Monte-Karlo tajribasida adaptiv usul doimiy muvozanatli rejimga nisbatan energiya sarfini 56,2 % kamaytirdi, nosozliklarni aniqlash darajasi esa 0,697 ni tashkil etdi.
References
1. Shi W., Cao J., Zhang Q., Li Y., Xu L. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal. 2016;3(5):637–646. https://doi.org/10.1109/JIOT.2016.2579198
2. Abadade Y. et al. A Comprehensive Survey on TinyML. IEEE Access. 2023;11:96892–96922. https://doi.org/10.1109/ACCESS.2023.3294111
3. Giordano M. et al. Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices. ENSsys 2023. pp. 65–71. https://doi.org/10.1145/3628353.3628545
4. Heo S., Mayer P., Magno M. Predictive Energy-Aware Adaptive Sampling with Deep Reinforcement Learning. IEEE ICECS 2022. https://doi.org/10.1109/ICECS202256217.2022.9971120
5. Ait Aoudia F., Gautier M., Berder O. RLMan: An Energy Manager Based on Reinforcement Learning for Energy Harvesting Wireless Sensor Networks. IEEE Transactions on Green Communications and Networking. 2018;2(2):408–417. https://doi.org/10.1109/TGCN.2018.2801725
6. Srbinovski B., Magno M., O’Flynn B., Pakrashi V., Popovici E. Energy Aware Adaptive Sampling Algorithm for Energy Harvesting Wireless Sensor Networks. IEEE SAS 2015. https://doi.org/10.1109/SAS.2015.7133582
