Abstract

Rest apnea has progressed toward becoming in the rest issue that causes more prominent worry lately because of its dismalness and mortality, higher therapeutic consideration expenses and needy individuals personal satisfaction. A few recommendations have tended to rest apnea sickness in older individuals, yet they have still some specialized confinements. Therefore, this paper introduces a creative framework dependent on haze and distributed computing   advancements which in mix with IoT and enormous information stages offers new chances to manufacture novel and imaginative administrations for supporting the rest apnea and to conquer the current restrictions. Especially, the framework is based on a few low-control remote systems with heterogeneous keen gadgets (i.e, sensors and actuators). In the mist, an edge hub (Smart IoT Door) gives IoT association and interoperability and pre-handling IoT information to distinguish occasions progressively that may imperil the older's wellbeing and to act appropriately. In the cloud, a Generic Empowering Influence Context Broker oversees, stores and infuses information into the enormous information analyzer for further handling and investigating. The framework's presentation and emotional relevance are assessed utilizing more than 30 GB size datasets and a survey satisfied by medicals pro, individually. Results demonstrate that the framework information investigation improve the wellbeing experts' basic leadership to screen and guide rest apnea treatment, just as improving old individuals' personal satisfaction.

Keywords

IoT, big data, fog computing, cloud computing, sleep apnea,

Downloads

Download data is not yet available.

References

  1. M. Glasser, N. Bailey, A. McMillan, and E. Goff, “Sleep apnoea in older people,” Breathe, vol. 7, no. 3, pp. 249– 256, 2011.
  2. C.-G. J. Teran Santos, Jimenez-Gomez A, “The Association Between Sleep Apnea and the Risk of Traffic Accidents,” N Engl J Med, vol. 340, pp. 847–851, 1999.
  3. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Futur. Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013.
  4. A. Whitmore, A. Agarwal, and L. Da Xu, “The Internet of Things—A survey of topics and trends,” Inf. Syst. Front., vol. 17, no. 2, pp. 261–274, 2015.
  5. Fiware, “Home - FIWARE.” [Online]. Available: https://www.fiware.org/. [Accessed: 18-May-2018].
  6. C. Stergiou and K. E. Psannis, “Recent advances delivered by Mobile Cloud Computing and Internet of Things for Big Data applications: a survey,” Int. J. Netw. Manag., no. March, 2016.
  7. A. Sapountzi and K. E. Psannis, “Social networking data analysis tools & challenges,” Futur. Gener. Comput. Syst., Oct. 2016.
  8. S. M. R. Islam, D. Kwak, H. Kabir, M. Hossain, and K.-S. Kwak, “The Internet of Things for Health Care: A Comprehensive Survey,” Access, IEEE, vol. 3, pp. 678– 708, 2015.
  9. G. Sannino, I. De Falco, and G. De Pietro, “Monitoring Obstructive Sleep Apnea by means of a real-time mobile system based on the automatic extraction of sets of rules through Differential Evolution,” J. Biomed. Inform., vol. 49, pp. 84–100, 2014.
  10. M. Bsoul, H. Minn, and L. Tamil, “Apnea MedAssist: Real-time sleep apnea monitor using single-lead ECG,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 3, pp. 416–427, 2011.
  11. H. Nakano et al., “Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: Proof of concept,” J. Clin. Sleep Med., vol. 10, no. 1, pp. 73–78, 2014.
  12. X. Zhu, X. Zhou, W. Chen, K. I. Kitamura, and T. Nemoto, “Estimation of Sleep Quality of Residents in Nursing Homes Using an Internet-Based Automatic Monitoring System,” Proc. - 2014 IEEE Int. Conf. Ubiquitous Intell. Comput. 2014 IEEE Int. Conf. Auton. Trust.Comput. 2014 IEEE Int. Conf. Scalable Comput. Commun. Assoc. Sy, pp. 659–665, 2014.
  13. F. Manoel et al., Multimodal Low-Invasive System for Sleep Quality Monitoring and Improvement. Cham: Springer International Publishing, 2017.
  14. Y. Nam, Y. Kim, and J. Lee, “Sleep monitoring based on a tri-axial accelerometer and a pressure sensor,” Sensors (Switzerland), vol. 16, no. 5, pp. 1–14, 2016.