The Fault Tolerant Smart Cradle
Keywords:
Fault tolerance, Generative AI, Internet of Things, Monitoring system, Neural Networks, Qlearning algorithm.Abstract
Nowadays, artificial intelligence is present in all parts of our lives from work, to hospitals, to schools and even our home. Through this work, we will present a smart cradle for infant monitoring. This system uses AI algorithms and IOT tools to monitor the baby and detect its needs which in turn, would be a very important tool to help parents, babysitters and nurses in hospitals to monitor the baby and understand its needs using the cries that they make. In addition to that, our smart cradle is a robust system that can detect if one of the sensors used is unavailable or malfunctioning and proposes solutions to insure its service.
Our smart cradle incorporates various sensors, including temperature, humidity and audio sensors, along with a camera, to continuously monitor the infant's health and movements within the cradle environment. The microphone is the most important sensor in our system because it allows the detection of the baby cries and it sends the sound to our intelligent algorithm that analysis the sound and determines what the baby needs. The most important part in our smart cradle is the fault tolerance part that allows the overall system to work correctly and without breakdowns. As we know, sensors are electronic objects that can break down due to different causes such as power outages, so this module is responsible for handling any exceptions that may arise.
We use different techniques for fault tolerance mainly replication, exception handlers and learning algorithms to solve all detected faults. The fault tolerance part makes our smart cradle different from all proposed systems for baby monitoring.
Downloads
References
Ji, C., Mudiyanselage, T. B., Gao, Y., & Pan, Y. (2021). A review of infant cry analysis and classification. EURASIP Journal on Audio, Speech, and Music Processing, 2021(1), 8.
Jabbar, W. A., Shang, H. K., Hamid, S. N., Almohammedi, A. A., Ramli, R. M., & Ali, M. A. (2019). IoT-BBMS: Internet of Things-based baby monitoring system for smart cradle. IEEE Access, 7, 93791-93805.
Kannan, P., Devaraj, A., Rajan, B. P. T., Swathira, P. K., Jayaranim, S., & Shebna, V. (2021). IoT Based Smart Cradle Using PI. In Recent Trends in Intensive Computing (pp. 716-720). IOS Press.
Kumar, V. S., Pullagura, L., Kumari, N. V., Pooja Nayak, S., Devi, B. P., Alharbi, A., & Asakipaam, S. A. (2022). Internet of Things-Based Patient Cradle System with an Android App for Baby Monitoring with Machine Learning. Wireless Communications and Mobile Computing, 2022.
Rosita, Y. D., & Junaedi, H. (2016, October). Infant's cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network. In 2016 2nd International Conference on Science and Technology-Computer (ICST) (pp. 160-166). IEEE.
Yin, W., Kann, K., Yu, M., & Schütze, H. (2017). Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:1702.01923.
Zinemanas, P., Rocamora, M., Miron, M., Font, F., & Serra, X. (2021). An interpretable deep learning model for automatic sound classification. Electronics, 10(7), 850.
Gokhale, P., Bhat, O., & Bhat, S. (2018). Introduction to IOT. International Advanced Research Journal in Science, Engineering and Technology, 5(1), 41-44.
Khandpur, R. S. (2017). Telemedicine technology and applications (mHealth, TeleHealth and eHealth). PHI Learning Pvt. Ltd..
Ramyachitra, D., & Manikandan, P. (2014). Imbalanced dataset classification and solutions: a review. International Journal of Computing and Business Research (IJCBR), 5(4), 1-29.
Gia, T. N., Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2015, April). Fault tolerant and scalable IoT-based architecture for health monitoring. In 2015 IEEE Sensors Applications Symposium (SAS) (pp. 1-6). IEEE.
Casado-Vara, R., Vale, Z., Prieto, J., & Corchado, J. M. (2018). Fault-tolerant temperature control algorithm for IoT networks in smart buildings. Energies, 11(12), 3430.
Watkins, C. J. C. H. (1989). Learning from delayed rewards.
Sutton, R. S., & Barto, A. G. (1999). Reinforcement learning: An introduction. Robotica, 17(2), 229-235.
Tesauro, G., & Kephart, J. O. (2002). Pricing in agent economies using multi-agent Q-learning. Autonomous agents and multi-agent systems, 5, 289-304.
Lin, J. H., & Vitter, J. S. (1992, July). A theory for memory-based learning. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 103-115).
McCallum, A. K. (1996). Reinforcement learning with selective perception and hidden state. University of Rochester.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
