Evolving requirements of Smart healthcare in Cloud
Computing and MIoT
Dipesh Singla1 , Sunil K. Singh1 , Harshit Dubey1 and Tejas Kumar1
1
Chandigarh College of Engineering and Technology, Sector-26, Chandigarh, 160019, India
Abstract
We are entering a new era of healthcare with MIoT advancements. New technology has been improved
in the field of healthcare. With the development of technology, the concept of smart healthcare has taken
hold. Using the latest technologies in the field of smart healthcare, we discuss the key importance of
cloud computing, data analysis, and artificial intelligence along with its subdomains. There have been
numerous advances in the field of smart healthcare as a result of these factors. Along with this data
analysis on the Role of Smart Healthcare in COVID-19 treatment in India is discussed. We are entering a
new era of healthcare with MIoT advancements. With the development of technology, the concept of
smart healthcare has taken hold. Smart healthcare uses the new and latest field of technologies such as
big data, cloud computing, machine learning, artificial intelligence, etc. In relation to smart healthcare
here, we discuss the key technologies and will also introduce several important fields. Using the latest
technologies in the field of smart healthcare, we discuss the key technologies as well as introduce several
important areas, such as big data, cloud computing, machine learning, and artificial intelligence. With
the continuous development of technology, the concept of smart healthcare has taken hold. Analyzing
data gathered from the IoT devices by using AI and Machine Learning can help to predict the upcoming
situation of diseases and help design a better environment to fight such diseases. Using the latest
technologies in the field of smart healthcare, we discuss the key technologies. It incorporates several
performers in a range of areas, and it is frequently aimed at the local community as well as children
and their families. This paper depicts the idea of solving health issues using the latest technology.
Furthermore, we also list the solutions and find the best one out of those in this paper. We also analyze
COVID-19 vaccination trends in India and discuss the role of smart healthcare and IoT in helping control
the transmission of COVID-19 as well as making it easier for citizens to get vaccinated with the help of
technology.
Keywords
MIoT, smart healthcare, AI in healthcare, COVID-19 treatment, Cloud Health care.
1. Introduction
With the advancement of technology, we are experiencing a transformation in smart healthcare,
which incorporates a new generation of information technology [20]. Community health
research is a complex field of research, due to the number of different possible factors concerning
one case[21]. Along with this, the internet has become one of the most essential components of
our life [22, 23]. There are many changes taking place which are mainly focused on the needs
of individuals, the efficiency of medical care, and future advancements in modern medicine and
International Conference on Smart Systems and Advanced Computing (Syscom-2021), December 25–26, 2021
$ tiwari.anupama@gmail.com (D. Singla); gupta.brij@gmail.com (S. K. Singh); dperakovic@fpz.unizg.hr
(H. Dubey); zhou1_zhili@163.com (T. Kumar)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
its domains. Health authorities directed their intervention toward both the environment and
the patients, as is typical of community health research. Today’s era is one of information [24,
25]. With the advancement in technology, the concept of smart healthcare is incorporating a
new generation of information technology [27, 28,29]. With the advancement of technology, we
are experiencing a transformation in smart healthcare, which incorporates a new generation
of information technology [30, 31]. In Section - 2 of this research paper we have discussed
Generations of Smart healthcare for MIoT. Similarly, Section 3 describes the importance of
cloud computing in the healthcare sector. How deep learning enhances the development of
healthcare is described in Section 4. Data is one of the most important things in smart health
care development. The role of IoT and technology in controlling the spread of COVID-19 in
India is also discussed with the help of a flowchart and diagram.
2. Generations of Smart healthcare for MIoT
Patient-centricity is at the heart of the next-generation healthcare system. Healthcare systems
are utilizing ICT effectively to improve the efficiency, speed, and accuracy of disease diagnosis,
monitoring, diagnosis, and treatment. Artificial intelligence and its subdomains are being used
in various ways such as disease prediction using machine learning, and robotic surgery [1].
It might use a cell phone to do image-based diagnostic and therapy recommendations. In the
past, health care systems acquired and stored patient data, enabled remote diagnosis of illnesses,
monitored and provided treatment without the use of Internet technology. This was known as
telemedicine or telehealth. E-Health is the combination of internet technology and telehealth. In
addition to cloud-based solutions, Blockchain technology, IoT medical devices, and so on, several
internet technologies have been integrated into the healthcare system [2]. Figure 2(a) presents
the first-generation model. In health care’s future, the goal is to enable disease prevention,
maintain high-quality medical care for a significant portion of the population, and manage
illnesses effectively at a low cost. In Figure 1, we show how the evolution of Smart Healthcare
and MIoT can be classified first based on Cloud Computing, which is a secure and cost-effective
means of storing data from patients and sharing it with recipients[11]. Next, we classify devices
that store data, such as body sensors worn by patients, and stationary devices like smart scales
and medication monitors.
The second generation of E-Health depicted in Figure 2(a) does not rely on medical devices to
store data, but rather on the cloud, which can be accessed remotely by clinicians. IoT and cloud
technologies are also included. With this generation of IoT medical devices, remote monitoring
and data collection are easier and more convenient. Providing real-time tracking of the patient
in a nonstop manner 24 hours a day, seven days a week, lowering the risk of developing a
serious condition. Whereas Figure 2(b) shows the second generation E-Health care system
which mainly adds the technology of cloud storage to the preexisting architecture. As part of
the third generation of E-Health, Blockchain technology has been used to address privacy and
security concerns. Figure 3(a) illustrates the third generation of E-Health, which is built on top
of the second generation. According to a predictive analysis based on Artificial Intelligence (AI)
agents, figure 3(b) illustrates the fourth generation[8] of the E-Health system. Medical services
and patient-centric services were at the center of the fourth generation of E-Health. Artificial
Figure 1: Classification of Smart healthcare and MIoT
Figure 2: (a) The first-generation E-Health care system, (b) The second-generation E-Health care system.
intelligence agents are software entities that can be run on a cloud service to enable continuous
health monitoring.
3. Need of Cloud in Healthcare
To adequately address the necessities of business and patients, well-informed services, ex-
perts are turning towards distributed computing for every one of its advantages. Distributed
computing, with its on-request accessibility, web-based administrations, and high-information
accessibility, has changed the whole medical services area and changed it into HealthTech [3, 4].
• Coordinated effort: In spreading information, collaboration is essential. With distributed
Figure 3: (a) The third-generation E-Health care system, (b) The fourth-generation E-Health care system
computing, the process has become more straightforward and less complex.
• Security: Health-care information should be kept private. This space’s richness of knowl-
edge makes it a meeting place.
• Cost: The cloud can store a considerable amount of data at a very cheap cost.
• Adaptability and Flexibility: Healthcare, as an industry, operates in a particular setting.
4. Role of Smart Healthcare and IoT in COVID-19 treatment in
India: Data Analysis
A Smart Healthcare Initiative launched by the Government of India is Aarogya Setu[5] which
facilitates digital storage of registered user’s personal health information and the places they
visit. This technology has helped in contact tracing and minimizing the transmission of COVID-
19. Another Smart Healthcare Initiative launched by the Government is the CoWin portal [6],
which allows users to register for COVID vaccination online from the comfort of their homes.
It has allowed people to get the COVID-19 vaccine without any hassle and also has made it
possible for India to successfully cross 1 billion doses [7]. After that, we examine the vaccination
trends in India and analyze how government initiatives and the digital revolution have increased
vaccination rates and helped control the spread of COVID-19.
Figure 4 shows the weekly vaccination trend in India. From this graph, we can see that
with the launch of Smart Healthcare Initiatives, the number of vaccinations has risen with the
increase in awareness.
Figure 5 (a) shows the age-wise vaccination trend. As the 18-44 age group is the more tech-
savvy one, COVID-19’s awareness has increased due to the Smart Healthcare Initiatives by the
means of information through digital media. Figure 5 (b) shows the trend of vaccination in
urban and rural areas of India. The Digital Revolution has enabled the rural areas of India to
enjoy the benefits of the digital world and the graph shows that the Smart Healthcare Initiatives
have increased awareness about COVID-19 in rural areas as well[18, 26]. Intelligent monitoring
of infected cases allowed rapid intervention in emergency cases and in limiting the spread of the
Figure 4: Vaccination Trend in India
Figure 5: (a) Age-wise Vaccination Trend in India (b) Vaccination Trend in Rural and Urban Areas in
India.
virus. Analyzing data gathered from the IoT medical devices by using AI and Machine Learning
models have helped to predict the future situation of the virus and has helped to design a better
environment to fight it[32].
5. Conclusion
A smart healthcare field is vast and can afford better health management for individual users.
Besides analyzing the latest innovations in smart healthcare and MIoT, the study also discusses
how deep learning and data analysis prove their importance in this developing field. The
interventions in community health research are often aimed at the local community, as well as
the children and their families. It is often the case that community health research is conducted
using vertically partitioned datasets, which may perpetuate ethical concerns due to the selection
of contributors. The data-centric workflow modeling approach shows promise for dealing
with such complexity in community health research. By advancing smart healthcare, we can
eliminate the status quo of medical resource inequality, promote the process of medical reform,
and lower the social medical costs as well. A major concern for the development of smart
healthcare and MIoT is cyber threats and data security. This is an appropriate approach for
modeling complex analyses of data in community health research. IoT medical devices enable
real-time and remote monitoring of COVID patients. IoT applications also help in contact
tracing to find potential carriers of the COVID virus and help prevent further transmission.
Government initiatives like the CoWin portal have helped people to register for vaccinations at
the comfort of their homes and enabled India to cross 1 Billion vaccination doses.
References
[1] J. Karamacoski, L. Gavrilovska, Framework for next generation of digital healthcare sys-
tems, in International Conference on Future Access Enablers of Ubiquitous and Intelligent
Infrastructures, (Springer, Cham, 2019), pp. 12–24
[2] C. Chakraborty, B. Gupta, S.K. Ghosh, A review on telemedicine-based WBAN framework
for patient monitoring. Int J. Telemed. E-Health, Mary Ann Liebert, Inc. 19(8), 619–626
(2013. ISSN: 1530-5627). https://doi.org/10.1089/tmj.2012.0215.
[3] M. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang, C.-H. Youn, Wearable 2.0: Enabling human-cloud
integration in next-generation healthcare systems. IEEE Commun. Mag. 55(1), 54–61 (2017).
[4] SK Singh, K Kaur, A Aggarwal, D Verma, “Achieving High-Performance Distributed Sys-
tem: Using Grid Cluster and Cloud Computing”, Int. Journal of Engineering Research and
Applications (IJERA), 5(2), pp 59-67, 2015.
[5] Aarogya Setu 2021, National Informatics Center, Government of India, accessed 11 Novem-
ber 2021,
[6] Co-WIN Portal 2021, Government of India, accessed 11 November 2021,
.
[7] Aggarwal, K., Singh, S. K., Chopra, M., Kumar, S. (2022). Role of Social Media in the COVID-
19 Pandemic: A Literature Review. In B. Gupta, D. Peraković, A. Abd El-Latif, D. Gupta
(Ed.), Data Mining Approaches for Big Data and Sentiment Analysis in Social Media (pp.
91-115). IGI Global. http://doi:10.4018/978-1-7998-8413-2.ch004.
[8] Sunil Sharma, Sunil Singh, and Subhash Panja, “Human Factors of Vehicle Automation”, in
Autonomous Driving and Advanced Driver-Assistance Systems (ADAS), Taylor Francis
Group (CRC Press), Chapter 15, 2021.
[9] Sunil Kr. Singh, R. K. Singh, M.P.S. Bhatia, SP Singh, “CAD for Delay optimization of
Symmetrical FPGA Architecture through Hybrid LUTs/PLAs”, ACIT, Vol. 178, Page 581-591,
Springer, 2012
[10] S. K. Singh, R. K. Singh and M. Bhatia, "Design flow of reconfigurable embedded sys-
tem architecture using LUTs/PLAs," 2012 2nd IEEE International Conference on Parallel,
Distributed and Grid Computing, 2012, pp. 385-390, doi: 10.1109/PDGC.2012.6449851
[11] Sudhakar Kumar, Sunil Kr Singh, Naveen Aggarwal, Kriti Aggarwal, “Evaluation of auto-
matic parallelization algorithms to minimize speculative parallelism overheads: An experi-
ment”, pp 1517-1528, 2021 Journal of Discrete Mathematical Sciences and Cryptography,
Volume 24, Issue 5 , Taylor Francis, (2021)
[12] R Madan, SK Singh, N Jain, Signal filtering using discrete wavelet transform,International
journal of recent trends in engineering 2 (3), 96, 2009
[13] SK Singh, RK Singh, MPS Bhatia, “Performance evaluation of hybrid reconfigurable com-
puting architecture over symmetrical FPGAs” , International Journal of Embedded Systems
and Applications 2 (3), 107-116, 2012 .
[14] SK Singh, RK Singh, MPS Bhatia, “System level architectural synthesis compilation
technique in reconfigurable computing system”, International Conference on Embedded
Systems and Applications (ESA10) WORK COMP-2010, pp 109-115, July 12-15, 2010.
[15] Sunil Kr. Singh, Ajay Kumar, Siddharth Gupta, Ratnakar Madan, “Architectural Perfor-
mance of WiMAX over WiFi with Reliable QoS over Wireless Communication” in Interna-
tional Journal Advanced Networking and Applications (IJANA) [EISSN: 0975-0282], Page
1016-1023, Vol. 03, Issue 01, July 2011.
[16] SK Singh, K Kaur, A Aggarwal, “Emerging Trends and Limitations in Technology and
System of Ubiquitous Computing”, International Journal of Advanced Research in Computer
Science (IJARCS), 5 (7), pp 174-178, 2014.
[17] Chopra, M., Singh, S. K., Aggarwal, K., Gupta, A. (2022). Predicting Catastrophic Events Us-
ing Machine Learning Models for Natural Language Processing. In B. Gupta, D. Peraković, A.
Abd El-Latif, D. Gupta (Ed.), Data Mining Approaches for Big Data and Sentiment Analysis
in Social Media (pp. 223-243). IGI Global. http://doi:10.4018/978-1-7998-8413-2.ch010.
[18] CoWin Vaccination Statistics dataset, accessed 11 November 2021. URL:
https://dashboard.cowin.gov.in/
[19] Gou, Z., Yamaguchi, S., Gupta, B. B. (2017). Analysis of various security issues and
challenges in cloud computing environment: a survey. In Identity Theft: Breakthroughs in
Research and Practice (pp. 221-247). IGI global.
[20] Al-Ayyoub, M., Al-Mnayyis, N., Alsmirat, M. A., Alawneh, K., Jararweh, Y., Gupta, B. B.
(2018). SIFT based ROI extraction for lumbar disk herniation CAD system from MRI axial
scans. Journal of Ambient Intelligence and Humanized Computing, 1-9.
[21] Al-Ayyoub, M., AlZu’bi, S., Jararweh, Y., Shehab, M. A., Gupta, B. B. (2018). Accelerating
3D medical volume segmentation using GPUs. Multimedia Tools and Applications, 77(4),
4939-4958
[22] AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., Gupta, B. (2020). Parallel imple-
mentation for 3d medical volume fuzzy segmentation. Pattern Recognition Letters, 130,
312-318.
[23] Amrita (2021), Game Theory for Cyber Security during COVID-19 Pandemic: A Holistic
Approach, Insights2Techinfo, pp.1
[24] Sandeep Kumar (2021) Artificial Intelligence and Machine learning for Smart and Secure
Healthcare System, Insights2Techinfo, pp.1
[25] A. Khan, F. G. Penalvo (2021), Blockchain Technology and Associated Challenges in Smart
Healthcare Systems, Insights2Techinfo, pp.1
[26] Sedik, A., Hammad, M., Abd El-Samie, F. E., et al. (2021). Efficient deep learning approach
for augmented detection of Coronavirus disease. Neural Computing and Applications, 1-18
[27] Gupta, B. B., Joshi, R. C., Misra, M. (2009). Defending against distributed denial of service
attacks: issues and challenges. Information Security Journal: A Global Perspective, 18(5),
224-247.
[28] Masud, M., Gaba, G. S., Alqahtani, S., et al. (2020). A lightweight and robust secure key
establishment protocol for internet of medical things in COVID-19 patients care. IEEE
Internet of Things Journal.
[29] Singh, K. S. (2021). Linux Yourself (1st ed.). Routledge."
[30] Sudhakar Kumar, Sunil Kr Singh, Naveen Aggarwal, Kriti Aggarwal, “Evaluation of auto-
matic parallelization algorithms to minimize speculative parallelism overheads: An experi-
ment”, pp 1517-1528, 2021 Journal of Discrete Mathematical Sciences and Cryptography,
Volume 24, Issue 5 , Taylor Francis, (2021).
[31] Sunil Kr. Sharma, Sunil Kr. Singh, and Subhash Panja, “Human Factors of Vehicle Au-
tomation”, in Autonomous Driving and Advanced Driver-Assistance Systems (ADAS):
Applications, Development, Legal Issues, and Testing (1st ed.). CRC Press, Chapter 15, pp
335-358, 2021. https://doi.org/10.1201/9781003048381
[32] Sudhakar Kumar, Sunil K. Singh (2021), Brain-Computer Interaction (BCI): A Way to
Interact with Brain Waves. Insights2Techinfo, pp. 1