Open Access
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01010
Number of page(s) 7
Published online 14 March 2022
  1. P. Asghari, A. M. Rahmani, and H. H. S. Javadi, “Internet of things applications: A systematic review,” Computer Networks, vol. 148, pp. 241–261, 2019. [CrossRef] [Google Scholar]
  2. D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of wireless sensor networks: an up-to- date survey,” Applied System Innovation, vol. 3, no. 1, p. 14, 2020. [CrossRef] [Google Scholar]
  3. S. Fu, Y. Zhang, Y. Jiang, C. Hu, C.-Y. Shih, and P. J. Marroń, “Experimental study for multi-layer parameter configuration of wsn links,” in 2015 IEEE 35 th International Conference on Distributed Computing Systems. IEEE, 2015, pp. 369-378. [Google Scholar]
  4. T. Huang, L. Lan, X. Fang, P. An, J. Min, and F. Wang, “Promises and challenges of big data computing in health sciences,” Big Data Research, vol. 2, no. 1, pp. 2-11, 2015. [CrossRef] [Google Scholar]
  5. R. Vinayakumar, M. Alazab, K. Soman, P. Poornachandran, A. AlNemrat, and S. Venkatraman, “Deep learning approach for intelligent intrusion detection system,” IEEE Access, vol. 7, pp. 41 525-41 550, 2019. [Google Scholar]
  6. S. Mahfouz, F. Mourad-Chehade, P. Honeine, J. Farah, and H. Snoussi, “Kernel-based machine learning using radio-fingerprints for localization in wsns,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 2, pp. 1324-1336, 2015. [CrossRef] [Google Scholar]
  7. Z. Noshad, N. Javaid, T. Saba, Z. Wadud, M. Q. Saleem, M. E. Alzahrani, and O. E. Sheta, “Fault detection in wireless sensor networks through the random forest classifier,” Sensors, vol. 19, no. 7, p. 1568, 2019. [CrossRef] [Google Scholar]
  8. X. Ma, T. Yao, M. Hu, Y. Dong, W. Liu, F. Wang, and J. Liu, “A survey on deep learning empowered iot applications,” IEEE Access, vol. 7, pp. 181 721181 732, 2019. [Google Scholar]
  9. M.-P. Hosseini, S. Lu, K. Kamaraj, A. Slowikowski, and H. C. Venkatesh, “Deep learning architectures,” in Deep learning: concepts and architectures. Springer, 2020, pp. 1-24. [Google Scholar]
  10. A. Parvat, J. Chavan, S. Kadam, S. Dev, and V. Pathak, “A survey of deep-learning frameworks,” in 2017 International Conference on Inventive Systems and Control (ICISC). IEEE, 2017, pp. 1-7. [Google Scholar]
  11. J. D. Day and H. Zimmermann, “The osi reference model,” Proceedings of the IEEE, vol. 71, no. 12, pp. 1334-1340, 1983. [CrossRef] [Google Scholar]
  12. Q. Wang and I. Balasingham, “Wireless sensor networks-an introduction,” Wireless sensor networks: application-centric design, pp. 1-14, 2010. [Google Scholar]
  13. H. M. A. Fahmy, “Protocol stack of wsns,” in Concepts, Applications, Experimentation and Analysis of Wireless Sensor Networks. Springer, 2021, pp. 53-66. [CrossRef] [Google Scholar]
  14. A. Kumar, M. Zhao, K.-J. Wong, Y. L. Guan, and P. H. J. Chong, “A comprehensive study of iot and wsn mac protocols: Research issues, challenges and opportunities,” IEEE Access, vol. 6, pp. 76 228-76 262, 2018. [Google Scholar]
  15. M. Pundir, J. K. Sandhu, and A. Kumar, “Quality- of-service prediction techniques for wireless sensor networks,” in Journal of Physics: Conference Series, vol. 1950, no. 1. IOP Publishing, 2021, p. 012082. [CrossRef] [Google Scholar]
  16. A. Akbas, H. U. Yildiz, A. M. Ozbayoglu, and B. Tavli, “Neural network based instant parameter prediction for wireless sensor network optimization models,” Wireless Networks, vol. 25, no. 6, pp. 3405-3418, 2019. [CrossRef] [Google Scholar]
  17. S. Peng, H. Jiang, H. Wang, H. Alwageed, and Y.- D. Yao, “Modulation classification using convolutional neural network based deep learning model,” in 2017 26th Wireless and Optical Communication Conference (WOCC). IEEE, 2017, pp. 1-5. [Google Scholar]
  18. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, pp. 1097-1105, 2012. [Google Scholar]
  19. T. O’shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, 2017. [CrossRef] [Google Scholar]
  20. P. H. Isolani, M. Claeys, C. Donato, L. Z. Granville, and S. Latré, “A survey on the programmability of wireless mac protocols,” IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1064-1092, 2018. [Google Scholar]
  21. R. Mennes, M. Camelo, M. Claeys, and S. Latre, “A neural-network based mf-tdma mac scheduler for collaborative wireless networks,” in 2018 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2018, pp. 1-6. [Google Scholar]
  22. R. Mennes, M. Claeys, F. A. De Figueiredo, I. Jabandzĭ c, I. Moer-man, and S. Latre, “Deep learning-based spectrum prediction collision avoidance for hybrid wireless environments,” IEEE Access, vol. 7, pp. 45 818-45 830, 2019. [Google Scholar]
  23. Y. Zhang, J. Hou, V. Towhidlou, and M. R. Shikh-Bahaei, “A neural network prediction-based adaptive mode selection scheme in full-duplex cognitive networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 540-553, 2019. [CrossRef] [Google Scholar]
  24. D. Cavalcanti, S. Das, J. Wang, and K. Challapali, “Cognitive radio based wireless sensor networks,” in 2008 Proceedings of 17th International Conference on Computer Communications and Networks. IEEE, 2008, pp. 1-6. [Google Scholar]
  25. H. Alhazmi, A. Almarhabi, A. Samarkandi, M. Alymani, M. H. Alhazmi, Z. Sheng, and Y.-D. Yao, “Classification of qpsk signals with different phase noise levels using deep learning,” in 2020 29th Wireless and Optical Communications Conference (WOCC). IEEE, 2020, pp. 1-5. [Google Scholar]
  26. N. Shabbir and S. R. Hassan, “Routing protocols for wireless sensor networks (wsns),” Wireless Sensor Networks-Insights and Innovations, 2017. [Google Scholar]
  27. W. Guo and W. Zhang, “A survey on intelligent routing protocols in wireless sensor networks,” Journal of Network and Computer Applications, vol. 38, pp. 185-201, 2014. [CrossRef] [Google Scholar]
  28. R. Sinde, F. Begum, K. Njau, and S. Kaijage, “Refining network lifetime of wireless sensor network using energy-efficient clustering and drlbased sleep scheduling,” Sensors, vol. 20, no. 5, p. 1540, 2020. [CrossRef] [Google Scholar]
  29. M. Ateeq, F. Ishmanov, M. K. Afzal, and M. Naeem, “Multi-parametric analysis of reliability and energy consumption in iot: A deep learning approach,” Sensors, vol. 19, no. 2, p. 309, 2019. [CrossRef] [Google Scholar]
  30. M. W. Gardner and S. Dorling, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,” Atmospheric environment, vol. 32, no. 14-15, pp. 2627-2636, 1998. [CrossRef] [Google Scholar]
  31. M. Ateeq, F. Ishmanov, M. K. Afzal, and M. Naeem, “Predicting delay in iot using deep learning: a multiparametric approach,” IEEE Access, vol. 7, pp. 62 022-62 031, 2019. [Google Scholar]
  32. M. Ateeq, M. K. Afzal, M. Naeem, M. Shafiq, and J.-G. Choi, “Deep learning-based multiparametric predictions for iot,” Sustainability, vol. 12, no. 18, p. 7752, 2020. [CrossRef] [Google Scholar]
  33. Chen Lei. Deep reinforcement learning. In Deep Learning and Practice with MindSpore, pages 217243. Springer, 2021. [Google Scholar]

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