EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 564 (2024) E3S Web Conf., 564 (2024) 01006 References
Open Access
Issue
E3S Web Conf.
Volume 564, 2024
International Conference on Power Generation and Renewable Energy Sources (ICPGRES-2024)
Article Number 01006
Number of page(s) 12
Section Renewable Energy Sources
DOI https://doi.org/10.1051/e3sconf/202456401006
Published online 06 September 2024
  1. Y.- H. Lin, Z.- T. Chou, C.- W. Yu, and R.- H. Jan, “Ideal and maximizedconfigurable power saving conventions for crown based remote sensornetworks,” IEEE Trans. Versatile Comput., to be published. [Google Scholar]
  2. F. Farahnakian et al., “Utilizing subterranean insect state framework to unite VMs forgreen distributed computing,” IEEE Trans. Administrations Comput., vol. 8, no. 2,pp. 187-198, Blemish./Apr. 2015. [CrossRef] [Google Scholar]
  3. C.- H. Chang, R. Y. Chang, and H.- Y. Hsieh, “High-fidelity energy-efficient machine-to-machine correspondence,” in Proc. IEEE 25th Annu.Int. Symp. Pers., Indoor, Portable Radio Commun., Sep. 2014, pp. 91-96. [Google Scholar]
  4. J. Shuja et al., “Review of strategies and models for designingenergy-efficient server farms,” IEEE Syst. J., to be published. [Google Scholar]
  5. A. Alamri, W. S. Ansari, M. M. Hassan, M. S. Hossain, A. Alelaiwi, and M. A. Hossain, “A review on sensor-cloud: Design, applications,and approaches,” Int. J. Distrib. Sensor Netw., vol. 2013, Nov. 2013, Art. ID 917923. [Google Scholar]
  6. S. Misra, S. Chatterjee, and M. S. Obaidat, “On hypothetical modelingof sensor cloud: A change in outlook from remote sensor organization,” IEEESyst. J., to be published. [Google Scholar]
  7. P. Sathyamoorthy, E. C.- H. Ngai, X. Hu, and V. C. M. Leung, “Energyefficiency as an organization administration for portable Web of Things,” inProc. seventh IEEE Int. Conf. Cloud Comput. Technol. Sci., Nov./Dec. 2015,pp. 1-8. [Google Scholar]
  8. A. Juels, “RFID security and protection: An examination overview,” IEEE J. Sel.Areas Commun., vol. 24, no. 2, pp. 381-394, Feb. 2006. [CrossRef] [Google Scholar]
  9. C. Zhu, L. Shu, T. Hara, L. Wang, S. Nishio, and L. T. Yang, “A study oncommunication and information the board issues in versatile sensor networks,”Wireless Commun. Versatile Comput., vol. 14, no. 1, pp. 19-36, Jan. 2014. [Google Scholar]
  10. J. A. Gutierrez, M. Naeve, E. Callaway, M. Common, V. Mitter, and B. Heile, “IEEE 802.15.4: A creating standard for low-power low-costwireless individual region organizations,” IEEE Netw., vol. 15, no. 5, pp. 12-19,Sep./Oct. 2001. [CrossRef] [Google Scholar]
  11. S.- H. Han and S. K. Park, “Execution examination of remote bodyarea network in indoor off-body correspondence,” IEEE Trans. Consum.Electron., vol. 57, no. 2, pp. 335-338, May 2011. [CrossRef] [Google Scholar]
  12. Alsharif, M.H.; Nordin, R.; Ismail, M. Characterization, Late Advances and Exploration Difficulties in Energy Productive Cell Organizations. Wirel. Pers. Commun. 2014. [Google Scholar]
  13. Miorandi, D.; Sicari, S.; De Pellegrini, F.; Chlamtac, I. Web of things: Vision, applications and examination challenges. Promotion. Hoc. Netw. 2012. [Google Scholar]
  14. Baliga, J.; Ayre, R.W.; Hinton, K.; Exhaust, R.S. Green distributed computing: Adjusting energy in handling, stockpiling, and transport. Proc. IEEE 2011. [Google Scholar]
  15. Shaikh, F.K.; Zeadally, S. Energy collecting in remote sensor organizations: An exhaustive survey. Recharge. Maintain. Energy Fire up. 2016. [Google Scholar]
  16. Akkaya, K.; Guvenc, I.; Aygun, R.; Pala, N.; Kadri, A. IoT-based inhabitance observing methods for energy-productive savvy structures. In Procedures of the 2015. [Google Scholar]
  17. Höller, J.; Boyle, D.; Karnouskos, S.; Avesand, S.; Mulligan, C.; Tsiatsis, V. From Machine-to-Machine to the Web of Things; Elsevier: Amsterdam, The Netherlands, 2014. [Google Scholar]
  18. Gulati, K.; Boddu, R.S.K.; Kapila, D.; Bangare, S.L.; Chandnani, N.; Saravanan, G. A survey paper on remote sensor network procedures in Web of Things (IoT). Mater. Today Proc. 2021. [Google Scholar]
  19. Al-Turjman, F.; Kamal, A.; Rehmani, M.H.; Radwan, A.; Pathan, A.- S.K. The green web of things (g-iot). Hindawi 2019. [Google Scholar]
  20. Wang, X.; Magno, M.; Cavigelli, L.; Benini, L. FANN-on-MCU: An Open-Source Tool stash for Energy-Effective Brain Organization Induction at the Edge of the Web of Things. IEEE Web Things J. 2020. [Google Scholar]
  21. Azar, J.; Makhoul, A.; Barhamgi, M.; Couturier, R. An energy proficient IoT information pressure approach for edge AI. Futur. Gener. Comput. Syst. 2019. [Google Scholar]
  22. Saranya K. ‘A Novel Hyperparameter Tuned Deep Learning Model for Power Quality Disturbance Prediction in Microgrids with Attention Based Feature Learning Mechanism’. Journal of Intelligent & Fuzzy Systems, PP: 2911 – 2927, Jan. 2024. [Google Scholar]
  23. Saranya K, Lakshmanan N, Logeshwaran M, Mathivanan S, “Deep Learning Based Algorithm For Detection of Diabetic Retinopathy”, International Journal Of Progressive Research In Engineering Management And Science, Vol. 03, Issue 03, March 2023, pp: 65-69. [Google Scholar]
  24. K.Saranya, K.Premalatha,” Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography”, Intelligent Automation & Soft Computing, Vol. 35, Issue 02, 2023 DOI: 10.32604/iasc.2023.027949, 2022. [Google Scholar]
  25. K.Saranya, S.S.Rajasekar, “Performance Model for Media Streaming Bandwidth Allocation using p2p”, International Journal of Recent Technology and Engineering, Vol. 9 Issue-1, 2020. Page No.:2072-2077. [Google Scholar]
  26. Pragathi, B., and P. Ramu. “Authentication Technique for Safeguarding Privacy in Smart Grid Settings.” E3S Web of Conferences. Vol. 540. EDP Sciences, 2024. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.