The Interoperability of Fog and IoT in Healthcare Domain: Architecture, Application, and Challenges

  • Karandeep Kaur
  • Harsh Kumar Verma
Part of the Signals and Communication Technology book series (SCT)


The great technological advances and rapid growth in the physical objects being connected to the Internet have led to the emergence of the term “Internet of Things” (IoT). IoT has an impact on almost all areas like construction, business, data analytics, e-commerce, agriculture, transportation, and healthcare. Maintenance of such a system can be done by the cloud computing but due to issues like long processing times, slow responses, and privacy issues, it is not preferred in real-time systems. IoT with its integration with fog computing can resolve problems like slow responses, delays, privacy, and security issues in healthcare systems. This chapter discusses the IoT and fog computing, their architecture, their application domains, and their integration and importance in healthcare. A literature survey involving all the works that include fog and IoT is discussed. Case studies involving fog and IoT in healthcare systems are also presented to provide light on how fog and IoT eliminate pressures on healthcare systems that require real-time processing.


Fog computing Healthcare Internet of things Wearable sensors Cloud computing Data processing Data privacy Data security 


  1. 1.
    Baker, S. B., Xiang, W., & Atkinson, I. (2017). Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access, 5, 26521–26544.Google Scholar
  2. 2.
    Ahmad, M., Amin, M. B., Hussain, S., Kang, B. H., Cheong, T., & Lee, S. (2016). Health fog: a novel framework for health and wellness applications. The Journal of Supercomputing, 72(10), 3677–3695.Google Scholar
  3. 3.
    Gope, P., & Hwang, T. (2016). BSN-Care: A secure IoT-based modern healthcare system using body sensor network. IEEE Sensors Journal, 16(5), 1368–1376.Google Scholar
  4. 4.
    Irawan, H. C., & Juhana, T. (2018). Heart rate monitoring using IoT wearable for ambulatory patient. In Proceeding of 2017 11th International Conference on Telecommunication Systems Services and Applications, TSSA 2017 (Vol. 2018, pp. 1–4).Google Scholar
  5. 5.
    Kaplan, S., Guvensan, M. A., Yavuz, A. G., & Karalurt, Y. (2015). Driver behavior analysis for safe driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3017–3032.Google Scholar
  6. 6.
    Jeong, Y. S., & Shin, S. S. (2016). An IoT healthcare service model of a vehicle using implantable devices. Cluster Computing, 21, 1.Google Scholar
  7. 7.
    Kartsch, V. J., Benatti, S., Schiavone, P. D., Rossi, D., & Benini, L. (2018). A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems. Information Fusion, 43, 66–76.Google Scholar
  8. 8.
    Li, G., & Chung, W. Y. (2018). Combined EEG-gyroscope-TDCS brain machine interface system for early management of driver drowsiness. IEEE Transactions on Human-Machine System, 48(1), 50–62.Google Scholar
  9. 9.
    Chowdhury, A., Shankaran, R., Kavakli, M., & Haque, M. M. (2018). Sensor applications and physiological features in drivers’ drowsiness detection: A review. IEEE Sensors Journal, 18(8), 3055–3067.Google Scholar
  10. 10.
    Choi, M., Koo, G., Seo, M., & Kim, S. W. (2018). Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE Transactions on Instrumentation and Measurement, 67(3), 634–645.Google Scholar
  11. 11.
    Abdi, L., & Meddeb, A. (2018). Driver information system: A combination of augmented reality, deep learning and vehicular Ad-hoc networks. Multimedia Tools and Applications, 77(12), 14673–14703.Google Scholar
  12. 12.
    Sun, W., Liu, J., & Zhang, H. (2017). When smart wearables meet intelligent vehicles: Challenges and future directions. IEEE Wireless Communications, 24(3), 58–65.Google Scholar
  13. 13.
    Ahmad, F., Kazim, M., Adnane, A., & Awad, A. (2015). Vehicular the cloud networks: Architecture, applications and security issues. In Proceedings - 2015 IEEE/ACM 8th International Conference on Utility and The cloud Computing, UCC (pp. 571–576).Google Scholar
  14. 14.
    Oliver, N., & Flores-mangas, F. (2006). HealthGear: A real-time wearable system for monitoring and analyzing physiological signals automatic detection of sleep apnea. IEEE Computer Society, C, 4–7.Google Scholar
  15. 15.
    Jin, Z., Oresko, J., Huang, S., & Cheng, A. C. (2009). HeartToGo: A personalized medicine technology for cardiovascular disease prevention and detection. In 2009 IEEE/NIH Life Science Systems and Applications Workshop (Vol. 80–83, p. 2009).Google Scholar
  16. 16.
    Leijdekkers, P., & Gay, V. (2008). A self-test to detect a heart attack using a mobile phone and wearable sensors. Proceedings of IEEE Symposium on Computer-Based Medical Systems, 2008, 93–98.Google Scholar
  17. 17.
    Pandian, P. S., Mohanavelu, K., Safeer, K. P., & Kotresh, T. M. (2008). Smart Vest: Wearable multi-parameter remote physiological monitoring system. Medical Engineering & Physics, 30(4), 466–477.Google Scholar
  18. 18.
    Balandong, R. P., Ahmad, R. F., Mohamad Saad, M. N., & Malik, A. S. (2018). A review on EEG-based automatic sleepiness detection systems for driver. IEEE Access, 6, 22908–22919.Google Scholar
  19. 19.
    Minerva, R., Biru, A., & Rotondi, D. (2015). Towards a definition of the internet of things (IoT). IEEE Internet of Things. Retrieved from
  20. 20.
    Perera, C., Liu, C. H., & Jayawardena, S. (2015). The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Transactions on Emerging Topics in Computing, 3(4), 585–598.Google Scholar
  21. 21.
    Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.zbMATHGoogle Scholar
  22. 22.
    Swan, M. (2012). Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1(3), 217–253.Google Scholar
  23. 23.
    Jing, Z. C., Wang, S., Wang, M., & Du, M. (2018). A low-cost collaborative location scheme with GNSS and RFID for the internet of things. ISPRS International Journal of Geo-Information, 7(5), 180.Google Scholar
  24. 24.
    Kraijak, S., & Tuwanut, P. (2015). A survey on IoT architectures, protocols, applications, security, privacy, real-world implementation and future trends. In 11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015) (pp. 1–6).Google Scholar
  25. 25.
    Dhanalaxmi, B., & Naidu, G. A. (2017). A survey on design and analysis of robust IoT architecture. In IEEE International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2017 – Proceedings, no. Icimia (pp. 375–378).Google Scholar
  26. 26.
    Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2013). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communication Surveys and Tutorials, 1(2), 78–95.Google Scholar
  27. 27.
    Shah, S. H., & Yaqoob, I. (2016). A survey: Internet of Things (IOT) technologies, applications and challenges. In 2016 4th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2016 (Vol. 1, pp. 381–385).Google Scholar
  28. 28.
    Beevi, M. J. (2016). A fair survey on Internet of Things (IoT). In 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 – Proceedings.Google Scholar
  29. 29.
    Sgouropoulos, D., Spyrou, E., Siantikos, G., & Giannakopoulos, T. (2015). Counting and tracking people in a smart room: An IoT approach. In Proceedings of the 10th International Workshop on Semantic and Social Media Adaptation and Personalization (pp. 7–12).Google Scholar
  30. 30.
    Datta, P., & Sharma, B. (2017). A survey on IoT architectures, protocols, security and smart city based applications. In 8th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2017.Google Scholar
  31. 31.
    TongKe, F. (2013). Smart agriculture based on the cloud computing and IOT. Journal of Convergence Information Technology, 8(2), 210–216.Google Scholar
  32. 32.
    Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., & Scorrano, F. (2014). Current trends in smart city initiatives: Some stylised facts. Cities, 38, 25–36.Google Scholar
  33. 33.
    Pyykonen, P., Laitinen, J., Viitanen, J., Eloranta, P., & Korhonen, T. (2013). IoT for intelligent traffic system. In Proceedings - 2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing, ICCP 2013 (pp. 175–179).Google Scholar
  34. 34.
    Amendola, S., Lodato, R., Manzari, S., Occhiuzzi, C., & Marrocco, G. (2014). RFID technology for IoT-based personal healthcare in smart spaces. IEEE Internet of Things Journal, 1(2), 144–152.Google Scholar
  35. 35.
    Catarinucci, L., De Donno, D., Mainetti, L., Palano, L., & Patrono, L. (2015). An IoT-Aware architecture for smart healthcare systems. IEEE Internet of Things Journal, 2(6), 515–526.Google Scholar
  36. 36.
    Pasluosta, C. F., Gassner, H., Winkler, J., Klucken, J., & Eskofier, B. M. (2015). An emerging era in the management of Parkinson’s disease: Wearable technologies and the internet of things. IEEE Journal of Biomedical and Health Informatics, 19(6), 1873–1881.Google Scholar
  37. 37.
    Hiremath, S., Yang, G., & Mankodiya, K. (2015). Wearable Internet of Things: Concept, architectural components and promises for person-centered healthcare. In 4th International Conference on Wireless Mobile Communication and Healthcare – Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (pp. 304–307).Google Scholar
  38. 38.
    Howcroft, J., Kofman, J., & Lemaire, E. D. (2017). Prospective fall-risk prediction models for older adults based on wearable sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1812–1820.Google Scholar
  39. 39.
    Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation, 2012, 1–17.Google Scholar
  40. 40.
    Prioleau, T., Ii, E. M., Member, S., & Paper, R. (2017). Automatic dietary monitoring. IEEE Transactions on Biomedical Engineering, 64(9), 2075–2089.Google Scholar
  41. 41.
    Chandra Mukhopadhyay, S. (2015). Wearable sensors for human activity monitoring: A review. IEEE Sensors Journal, 15(3), 1321–1330.Google Scholar
  42. 42.
    Liang, T., & Yuan, Y. J. (2016). Wearable medical monitoring systems based on wireless networks: A review. IEEE Sensors Journal, 16(23), 8186–8199.Google Scholar
  43. 43.
    Andreu-Perez, J., Leff, D. R., Ip, H. M. D., & Yang, G.-Z. (2015). From wearable sensors to smart implants—Toward pervasive and personalized healthcare javier. IEEE Transactions on Biomedical Engineering, 62(12), 2750–2762.Google Scholar
  44. 44.
    Vora, J., DevMurari, P., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2018). Blind signatures based secured e-healthcare system. In 2018 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1–5).Google Scholar
  45. 45.
    Tanwar, S., Patel, P., Patel, K., Tyagi, S., Kumar, N., & Obaidat, M. S. (2017). An advanced Internet of Thing based security alert system for smart home. In 2017 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 25–29).Google Scholar
  46. 46.
    Vora, J., et al. (2018). BHEEM: A blockchain-based framework for securing electronic health records. In 2018 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6).Google Scholar
  47. 47.
    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.Google Scholar
  48. 48.
    Tanwar, S., Ramani, T., & Tyagi, S. (2017). Dimensionality reduction using pca and svd in big data: A comparative case study. In International Conference on Future Internet Technologies and Trends (pp. 116–125).Google Scholar
  49. 49.
    Tanwar, S., Tyagi, S., & Kumar, N. (2019). Multimedia big data computing for IoT applications: Concepts, paradigms and solutions (Vol. 163). New York, NY: Springer.Google Scholar
  50. 50.
    Verma, J. P., Tanwar, S., Garg, S., Gandhi, I., & Bachani, N. H. (2019). Evaluation of pattern based customized approach for stock market trend prediction with big data and machine learning techniques. International Journal of Business Analysis, 6(3), 1–15.Google Scholar
  51. 51.
    Shankar, K., Lakshmanaprabu, S. K., Khanna, A., Tanwar, S., Rodrigues, J. J. P. C., & Roy, N. R. (2019). Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier. Computers and Electrical Engineering, 77, 230–243.Google Scholar
  52. 52.
    Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Maasberg, M., & Choo, K.-K. R. (2018). Multimedia big data computing and Internet of Things applications: A taxonomy and process model. Journal of Network and Computer Applications, 124, 169–195.Google Scholar
  53. 53.
    ALzubi, J. A., Bharathikannan, B., Tanwar, S., Manikandan, R., Khanna, A., & Thaventhiran, C. (2019). Boosted neural network ensemble classification for lung cancer disease diagnosis. Applied Soft Computing, 80, 579–591.Google Scholar
  54. 54.
    Patel, D., Narmawala, Z., Tanwar, S., & Singh, P. K. (2019). A systematic review on scheduling public transport using IoT as tool. In Smart innovations in communication and computational sciences (pp. 39–48). New York, NY: Springer.Google Scholar
  55. 55.
    Mittal, M., Tanwar, S., Aggarwal, B., & Goyal, L. M. (2019). Energy conservation for IoT devices: concepts, paradigms and solutions. New York, NY: Springer.Google Scholar
  56. 56.
    Hathaliya, J. J., Tanwar, S., Tyagi, S., & Kumar, N. (2019). Securing electronics healthcare records in Healthcare 4.0: A biometric-based approach. Computers and Electrical Engineering, 76, 398–410.Google Scholar
  57. 57.
    Kaneriya, S., et al. (2018). A range-based approach for long-term forecast of weather using probabilistic Markov model. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops) (pp. 1–6).Google Scholar
  58. 58.
    Tanwar, S., Vora, J., Kaneriya, S., & Tyagi, S. (2017). Fog-based enhanced safety management system for miners. In 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) (Fall) (pp. 1–6).Google Scholar
  59. 59.
    Gupta, R., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Sadoun, B. (2019). HaBiTs: Blockchain-based telesurgery framework for Healthcare 4.0. In 2019 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1–5).Google Scholar
  60. 60.
    Kaneriya, S., Chudasama, M., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. P. C. (2019). Markov decision-based recommender system for sleep apnea patients. In ICC 2019–2019 IEEE International Conference on Communications (ICC) (pp. 1–6).Google Scholar
  61. 61.
    Tanwar, S., Obaidat, M. S., Tyagi, S., & Kumar, N. (2019). Online signature-based biometric recognition. In Biometric-based physical and cybersecurity systems (pp. 255–285). New York, NY: Springer.Google Scholar
  62. 62.
    Kabra, N., Bhattacharya, P., Tanwar, S., & Tyagi, S. (2020). MudraChain: Blockchain-based framework for automated cheque clearance in financial institutions. Future Generation Computer Systems, 102, 574–587.Google Scholar
  63. 63.
    Prasad, V. K., Bhavsar, M. D., & Tanwar, S. (2019). Influence of monitoring: Fog and edge computing. Scalable Computing: Practice and Experience, 20(2), 365–376.Google Scholar
  64. 64.
    Tanwar, S., Thakkar, K., Thakor, R., & Singh, P. K. (2018). M-Tesla-based security assessment in wireless sensor network. Procedia Computing Science, 132, 1154–1162.Google Scholar
  65. 65.
    Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N. (2018). Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Computers and Electrical Engineering, 72, 1–13.Google Scholar
  66. 66.
    Mistry, I., Tanwar, S., Tyagi, S., & Kumar, N. (2020). Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenges. Mechanical Systems and Signal Processing, 135, 106382.Google Scholar
  67. 67.
    Bodkhe, U., Bhattacharya, P., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2019). BloHosT: Blockchain enabled smart tourism and hospitality management. In International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1–5).Google Scholar
  68. 68.
    Tanwar, S., et al. (2019). Human arthritis analysis in fog computing environment using Bayesian Network Classifier and Thread Protocol. IEEE Consumer Electronics Magazine, 9(1), 88–94.Google Scholar
  69. 69.
    Vora, J., et al. (2018). Ensuring privacy and security in E-health records. In 2018 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1–5).Google Scholar
  70. 70.
    Vora, J., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. P. C. (2017). FAAL: Fog computing-based patient monitoring system for ambient assisted living. In 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services (Healthcom) (pp. 1–6).Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Karandeep Kaur
    • 1
  • Harsh Kumar Verma
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyJalandharIndia

Personalised recommendations