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All issues Volume 477 (2024) E3S Web Conf., 477 (2024) 00016 References
Open Access
Issue
E3S Web Conf.
Volume 477, 2024
International Conference on Smart Technologies and Applied Research (STAR'2023)
Article Number 00016
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202447700016
Published online 16 January 2024
  1. Y. Liu, H.-N. Dai, Q. Wang, M. K. Shukla, and M. Imran, “Unmanned aerial vehicle for internet of everything: Opportunities and challenges,” Comput. Commun. , vol. 155, pp. 66–83, Apr. 2020, doi: 10.1016/j.comcom.2020.03.017. [CrossRef] [Google Scholar]
  2. F. Al-Turjman, M. Abujubbeh, A. Malekloo, and L. Mostarda, “UAVs assessment in software-defined IoT networks: An overview,” Comput. Commun. , vol. 150, no. July 2019, pp. 519–536, 2020. [CrossRef] [Google Scholar]
  3. Slimani, K., Khoulji, S., Mortreau, A., & Kerkeb, M. L. (2024). From tradition to innovation: The telecommunications metamorphosis with AI and advanced technologies. Journal of Autonomous Intelligence, 7(1). [Google Scholar]
  4. S. Duangsuwan, A. Chusongsang, and S. Promwong, “Performance Analysis of Power Outage Probability for Drone based IoT Connectivity Network,” Proc. - 2019 Int. Symp. Intell. Signal Process. Commun. Syst. ISPACS 2019 , pp. 5–6, 2019, [Google Scholar]
  5. T. Addabbo et al., “An Automatic Battery Recharge and Condition Monitoring System for Autonomous Drones,” in 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, Jun. 2020, pp. 1–5. [Google Scholar]
  6. T. M. Mostafa, A. Muharam, and R. Hattori, “Wireless battery charging system for drones via capacitive power transfer,” in 2017 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW), May 2017, pp. 1–6. doi: 10.1109/WoW.2017.7959357. [Google Scholar]
  7. G. Faraci, A. Raciti, S. A. Rizzo, and G. Schembra, “Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry,” Appl. Energy , vol. 259, p. 114204, Feb. 2020, doi: 10.1016/j.apenergy.2019.114204. [CrossRef] [Google Scholar]
  8. S. Kouroshnezhad, A. Peiravi, M. S. Haghighi, and A. Jolfaei, “Energy-efficient drone trajectory planning for the localization of 6g-enabled iot devices,” IEEE Internet Things J. , vol. 8, no. 7, pp. 5202–5210, 2021, doi: 10.11 09/JIOT.2020.3032347. [CrossRef] [Google Scholar]
  9. C. Y. Lee, “Cooperative Drone Positioning Measuring in Internet-of-Drones,” 2020 IEEE 17th Annu. Consum. Commun. Netw. Conf. CCNC 2020, pp. 2020–2022, 2020, doi: 10.1109/CCNC46108.2020.9045111. [Google Scholar]
  10. Slimani, K., Ruichek, Y., & Messoussi, R. (2022). Compound facial emotional expression recognition using CNN deep features. Engineering Letters, 30(4). [Google Scholar]
  11. V. Delafontaine, F. Schiano, G. Cocco, A. Rusu, and D. Floreano, “Drone-aided Localization in LoRa IoT Networks,” Proc. - IEEE Int. Conf. Robot. Autom. , pp. 286–292, 2020, doi: 10.1109/ICRA40945.2020.9196869. [Google Scholar]
  12. Ossamah, “Blockchain as a solution to Drone Cybersecurity,” IEEE World Forum Internet Things, WF-IoT 2020 - Symp. Proc. , pp. 1–9, 2020, doi: 10.1109/WF-IoT48130.2020.9221466. [Google Scholar]
  13. Bera, S. Saha, A. K. Das, N. Kumar, P. Lorenz, and M. Alazab, “Blockchain-Envisioned Secure Data Delivery and Collection Scheme for 5G-Based IoT-Enabled Internet of Drones Environment,” IEEE Trans. Veh. Technol. , vol. 69, no. 8, pp. 9097–9111, 2020, doi: 10.1109/TVT.2020.3000576. [CrossRef] [Google Scholar]
  14. Yazdinejad, R. M. Parizi, A. Dehghantanha, H. Karimipour, G. Srivastava, and M. Aledhari, “Enabling Drones in the Internet of Things with Decentralized Blockchain-Based Security,” IEEE Internet Things J. , vol. 8, no. 8, pp. 6406–6415, 2021, doi: 10.1109/JIOT.2020.3015382. [CrossRef] [Google Scholar]
  15. V. Pal, B. S. Acharya, S. Shrivastav, S. Saha, A. Joglekar, and B. Amrutur, “PUF Based Secure Framework for Hardware and Software Security of Drones,” Proc. 2020 Asian Hardw. Oriented Secur. Trust Symp. AsianHOST 2020 , pp. 0–5, 2020, doi: 10.1109/AsianHOST51057.2020.9358264. [Google Scholar]
  16. T. Kobayashi, T. Yokogawa, N. Igawa, Y. Sato, S. Fujii, and K. Arimoto, “A Compact Low Power AI Module Mounted on Drone for Plant Monitor System,” Proc. - 2019 8th Int. Congr. Adv. Appl. Informatics, IIAI-AAI 2019 , pp. 1081–1082, 2019, doi: 10.1109/IIAI-AAI.2019.00236. [Google Scholar]
  17. N. Kitpo and M. Inoue, “Early rice disease detection and position mapping system using drone and IoT architecture,” Proc. - 12th SEATUC Symp. SEATUC 2018 , pp. 0–4, 2018, doi: 10.1109/SEATUC.2018.8788863. [Google Scholar]
  18. N. Siriphun, “Distinguishing Drone Types Based on Acoustic Wave by IoT Device,” 2018 22nd Int. Comput. Sci. Eng. Conf., pp. 1–4, 2018. [Google Scholar]
  19. L. Shan, R. Miura, T. Kagawa, F. Ono, H. B. Li, and F. Kojima, “Machine Learning-Based Field Data Analysis and Modeling for Drone Communications,” IEEE Access , vol. 7, pp. 79127–79135, 2019, doi: 10.1109/ACCESS.2019.2922544. [CrossRef] [Google Scholar]
  20. M. H. Abu Bakar, A. U. Shamsudin, and R. A. Rahim, “Simulation of drone controller using reinforcement learning AI with hyperparameter optimization,” 2020 IEEE 10th Int. Conf. Syst. Eng. Technol. ICSET 2020 - Proc., no. November, pp. 167–172, 2020, doi: 10.1109/ICSET51301.2020.9265381. [Google Scholar]
  21. J. Su et al., “Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring,” IEEE Trans. Ind. Informatics , vol. 17, no. 3, pp. 2242–2249, Mar. 2021, doi: 10.1109/TII.2020.2979237. [CrossRef] [Google Scholar]
  22. J. A. Paredes, J. Gonzalez, C. Saito, and A. Flores, “Multispectral imaging system with UAV integration capabilities for crop analysis,” in 2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE), Jun. 2017, pp. 1–4. doi: 10.1109/GRSS-CHILE.2017.7996009. [Google Scholar]
  23. P. B. Balaji, S. Kowshik Chennupati, S. Radha Krishna Chilakalapudi, R. Katuri, K. Mareedu, and R. Scholars, “Design of UAV (Drone) for Crop, Weather Monitoring and for Spraying Fertilizers and Pesticides,” Int. J. Res. Trends Innov. , vol. 3, no. March 2018, p. 42, 2018, [Online]. Available: www.ijrti.org [Google Scholar]
  24. Alkouz, B. Shahzaad, and A. Bouguettaya, “Service-Based Drone Delivery,” Proc. - 2021 IEEE 7th Int. Conf. Collab. Internet Comput. CIC 2021 , no. Cic, pp. 68–76, 2021, doi: 10.1109/CIC52973.2021.00019. [Google Scholar]
  25. J. T. Zou and V. G. Rajvee, “Drone-based solar panel inspection with 5G and AI Technologies,” Proc. 2022 8th Int. Conf. Appl. Syst. Innov. ICASI 2022 , pp. 174–178, 2022, doi: 10.1109/ICASI55125.2022.9774462. [Google Scholar]
  26. Mukherjee, P. Mukherjee, D. De, and N. Dey, “QoS-aware 6G-enabled ultra low latency edge- assisted Internet of Drone Things for real-time stride analysis,” Comput. Electr. Eng. , vol. 95, no. September 2020, p. 107438, 2021, doi: 10.1016/j.compeleceng.2021.107438. [CrossRef] [Google Scholar]
  27. P. Khopkar, “Mixed-Initiative Flexible Autonomy in Drone Swarms for COVID-19 Applications,” Int. Symp. Technol. Soc. Proc. , vol. 2020-Novem, pp. 457–461, 2020, doi: 10.1109/ISTAS50296.2020.9462196. [Google Scholar]
  28. Kumar, A. S. Yadav, S. S. Gill, H. Pervaiz, Q. Ni, and R. Buyya, “A Secure Drone-to-Drone Communication and Software Defined Drone Network-Enabled Traffic Monitoring System,” Simul. Model. Pract. Theory , vol. 120, no. December 2021, p. 102621, 2022, doi: 10.1016/j.simpat.2022.102621. [CrossRef] [Google Scholar]
  29. Chen, T., Zhang, X. P., Wang, J., Li, J., Wu, C., Hu, M., & Bian, H. (2020). A review on electric vehicle charging infrastructure development in the UK. Journal of Modern Power Systems and Clean Energy, 8(2), 193-205. [CrossRef] [Google Scholar]
  30. Shibl, M., Ismail, L., & Massoud, A. (2021). Electric vehicles charging management using machine learning considering fast charging and vehicle-to-grid operation. Energies, 14(19), 6199. [CrossRef] [Google Scholar]
  31. S. H. Alsamhi, O. Ma, M. S. Ansari, and F. A. Almalki, “Survey on collaborative smart drones and internet of things for improving smartness of smart cities,” IEEE Access, vol. 7, pp. 128125–128152, 2019, doi: 10.1109/ACCESS.2019.2934998. [CrossRef] [Google Scholar]

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