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Progression and Challenges of IoT in Healthcare: A Short Review

by S.M.Atikur Rahman, Sifat Ibtisum, Priya Podder, S.M. Saokat Hossain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 37
Year of Publication: 2023
Authors: S.M.Atikur Rahman, Sifat Ibtisum, Priya Podder, S.M. Saokat Hossain
10.5120/ijca2023923168

S.M.Atikur Rahman, Sifat Ibtisum, Priya Podder, S.M. Saokat Hossain . Progression and Challenges of IoT in Healthcare: A Short Review. International Journal of Computer Applications. 185, 37 ( Oct 2023), 9-15. DOI=10.5120/ijca2023923168

@article{ 10.5120/ijca2023923168,
author = { S.M.Atikur Rahman, Sifat Ibtisum, Priya Podder, S.M. Saokat Hossain },
title = { Progression and Challenges of IoT in Healthcare: A Short Review },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2023 },
volume = { 185 },
number = { 37 },
month = { Oct },
year = { 2023 },
issn = { 0975-8887 },
pages = { 9-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number37/32929-2023923168/ },
doi = { 10.5120/ijca2023923168 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:00.556127+05:30
%A S.M.Atikur Rahman
%A Sifat Ibtisum
%A Priya Podder
%A S.M. Saokat Hossain
%T Progression and Challenges of IoT in Healthcare: A Short Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 37
%P 9-15
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.

References
  1. Belfiore, A., Cuccurullo, C., & Aria, M. (2022). IoT in healthcare: A scientometric analysis. Technological Forecasting and Social Change, 184, 122001.
  2. Khan, M. M., Alanazi, T. M., Albraikan, A. A., & Almalki, F. A. (2022). IoT-based health monitoring system development and analysis. Security and Communication Networks, 2022.
  3. Verdegem, P., De Marez, L., 2011. Rethinking determinants of ICT acceptance: towards an integrated and comprehensive overview. Technovation 31 (8), 411–423.
  4. Lu, Y., Papagiannidis, S., Alamanos, E., 2018. Internet of things: a systematic review of the business literature from the user and organisational perspectives. Technol. Forecast. Soc. Chang. 136, 285–297.
  5. Bharati, S., Mondal, M. R. H., Podder, P., & Kose, U. (2023). Explainable Artificial Intelligence (XAI) with IoHT for Smart Healthcare: A Review. Interpretable Cognitive Internet of Things for Healthcare, 1-24.
  6. Bharati, S. & Hossain Mondal, M. (2021). 12 Applications and challenges of AI-driven IoHT for combating pandemics: a review. In A. Khamparia, R. Hossain Mondal, P. Podder, B. Bhushan, V. Albuquerque & S. Kumar (Ed.), Computational Intelligence for Managing Pandemics (pp. 213-230). Berlin, Boston: De Gruyter. https://doi.org/10.1515/9783110712254-012.
  7. Dey, N., Ashour, A.S., Bhatt, C., 2017. Internet of things driven connected healthcare. In: Internet of Things And Big Data Technologies for Next Generation Healthcare. Springer, Cham, pp. 3–12.
  8. Papa, A., Mital, M., Pisano, P., Del Giudice, M., 2020. E-health and wellbeing monitoring using smart healthcare devices: an empirical investigation. Technol. Forecast. Soc. Chang. 153, 119226.
  9. Martínez-Caro, E., Cegarra-Navarro, J.G., García-P´erez, A., Fait, M., 2018. Healthcare service evolution towards the internet of things: an end-user perspective. Technol. Forecast. Soc. Chang. 136, 268–276.
  10. Islam, S.R., Kwak, D., Kabir, M.H., Hossain, M., Kwak, K.S., 2015. The internet of things for health care: a comprehensive survey. IEEE Access 3, 678–708.
  11. J. R. Gundala, S. S. Varsha Potluri, S. V. Damle and M. F. Hashmi, "IoT & ML-Based Healthcare Monitoring System-Review," 2022 IEEE International Symposium on Smart Electronic Systems (iSES), Warangal, India, 2022, pp. 623-626, doi: 10.1109/iSES54909.2022.00137.
  12. M. N. Bhuiyan, M. M. Rahman, M. M. Billah and D. Saha, "Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities," in IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10474-10498, 1 July1, 2021.
  13. F. Miao, Z.-D. Liu, J.-K. Liu, B. Wen, Q.-Y. He, and Y. Li, “Multisensor fusion approach for cuff-less blood pressure measurement,'' IEEE J. Biomed. Health Informat., vol. 24, no. 1, pp. 79 91, Jan. 2020.
  14. F. Yang, X. Zhao, W. Jiang, P. Gao, and G. Liu, “Multi-method fusion of cross-subject emotion recognition based on highdimensional EEG features,'' Frontiers Comput. Neurosci., vol. 13, p. 53, Aug. 2019. Accessed: Jul. 1, 2020. [Online].
  15. Q. Gu, S. Jiang, M. Lian, and C. Lu, “Health and safety situation awareness model and emergency management based on multi-sensor signal fusion,'' IEEE Access, vol. 7, pp. 958 968, 2019.
  16. M. Muzammal, R. Talat, A. H. Sodhro, and S. Pirbhulal, “A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks,'' Inf. Fusion, vol. 53, pp. 155 164, Jan. 2020.
  17. T. Van Steenkiste, D. Deschrijver, and T. Dhaene, “Sensor fusion using backward shortcut connections for sleep apnea detection in multi-modal data,'' 2019, arXiv:1912.06879.
  18. Y. Zhang, Y. Zhang, X. Zhao, Z. Zhang, and H. Chen, “Design and data analysis of sports information acquisition system based on Internet of medical things,'' IEEE Access, vol. 8, pp. 84792 84805, 2020.
  19. I. Chiuchisan, H.-N. Costin, and O. Geman, “Adopting the Internet of Things technologies in health care systems,'' in Proc. Int. Conf. Expo. Electr. Power Eng. (EPE), Iasi, Romania, Oct. 2014, pp. 532 535.
  20. A. Sharipudin and W. Ismail, “Internet of medical things (IoMT) for patient healthcare monitoring system,'' in Proc. IEEE 14th Malaysia Int. Conf. Commun. (MICC), Selangor, Malaysia, Dec. 2019, pp. 69 74.
  21. D. V. Dimitrov, “Medical Internet of Things and big data in healthcare,'' Healthcare Inform. Res., vol. 22, no. 3, pp. 156 163, 2016.
  22. A. Pazienza, R. Anglani, G. Mallardi, C. Fasciano, P. Noviello, C. Tatulli, and F. Vitulano, “Adaptive critical care intervention in the Internet of medical things,'' in Proc. IEEE Conf. Evolving Adapt. Intell. Syst. (EAIS), Bari, Italy, May 2020, pp. 1 8.
  23. S. Sanyal, D. Wu, and B. Nour, “A federated filtering framework for Internet of medical things,'' in Proc. IEEE Int. Conf. Commun. (ICC), Shanghai, China, May 2019, pp. 1 6.
  24. M. Luna-delRisco, M. G. Palacio, C. A. A. Orozco, S. V. Moncada, L. G. Palacio, J. J. Q. Montealegre, and I. Diaz-Forero, “Adoption of Internet of medical things (IoMT) as an opportunity for improving public health in Latin America,'' in Proc. 13th Iberian Conf. Inf. Syst. Technol. (CISTI), Caceres, Spain, Jun. 2018, pp. 1 5.
  25. Krishnamoorthy, S., Dua, A. & Gupta, S. Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: a survey, current challenges and future directions. J Ambient Intell Human Comput 14, 361–407 (2023).
  26. Khamparia, A., Bharati, S., Podder, P., Gupta, D., Khanna, A., Phung, T. K., & Thanh, D. N. (2021). Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimensional systems and signal processing, 32, 747-765.
  27. A.H.M Shahariar Parvez, Bipasha Sarker, “Role of IoT and Cloud Computing in Digital Healthcare”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 11, Issue 4, April 2022.
  28. Yin Y, Zeng Y, Chen X, Fan Y (2016) The internet of things in healthcare: AN overview. Ind Inf Integr 1:3–13.
  29. Gardasevic G, Veletić M, Maletić N, Vasiljević D, Radusinović I, Tomović S, Radonjić M (2016) The IOT architectural framework, design issues and application domains. Wirel Pers Commun 92:127–148.
  30. Nauman A, Qadri YA, Amjad M, Zikria YB, Afzal MK, Kim SW (2020) Multimedia internet of things: a comprehensive survey. IEEE Access 8:8202–8250.
  31. Sarker, B., Sharif, N. B., Rahman, M. A. & Parvez, A. S. (2023). AI, IoMT and Blockchain in Healthcare. Journal of Trends in Computer Science and Smart Technology, 5(1), 30-50. doi:10.36548/jtcsst.2023.1.003.
  32. Bharati, S., & Podder, P. (2022). Machine and deep learning for iot security and privacy: applications, challenges, and future directions. Security and Communication Networks, 2022, 1-41.
  33. Podder, P., Mondal, M., Bharati, S., & Paul, P. K. (2021). Review on the security threats of internet of things. arXiv preprint arXiv:2101.05614.
  34. Ahmed, R., & Deng, H. (2023). Ground Moving Target Detection Using Multi-Features under Antenna Array Crabbing. arXiv preprint arXiv:2304.08716.
  35. Ibtisum, S. (2020). A Comparative Study on Different Big Data Tools.
  36. Aguzzi S., Bradshaw D., Canning M., et al. Definition of a research and innovation policy leveraging cloud computing and IoT combination. Final Report, European Commission, SMART . 2013;37
  37. Siarry P., Jabbar M. A., Aluvalu R., Abraham A., Madureira A. The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care . Berlin, Germany: Springer; 2021.
  38. Saba Raoof S, Durai MAS. A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies. Contrast Media Mol Imaging. 2022 Sep 29;2022:4822235.
  39. Spanhol F. A., Oliveira L. S., Petitjean C., Heutte L. Breast cancer histopathological image classification using convolutional neural networks. Proceedings of the international joint conference on neural networks (IJCNN); July 2016; Vancouver, British Columbia, Canada.
  40. Ertosun M. G., Rubin D. L. Probabilistic visual search for masses within mammography images using deep learning. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM); July 2015; Washington, DC, USA.
  41. Albayrak A., Bilgin G. Mitosis detection using convolutional neural network based features. Proceedings of the IEEE 17th International symposium on computational intelligence and informatics (CINTI); November 2016; Budapest, Hungary.
  42. Chen H., Qi X., Yu L., Heng P.-A. DCAN: deep contour-aware networks for accurate gland segmentation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition; June 2016; Las Vegas, Nevada, USA.
  43. Xu B., Wang N., Chen T., Li M. Empirical evaluation of rectified activations in convolutional network. 2015. https://arxiv.org/abs/1505.00853.
  44. Wichakam I., Vateekul P. Combining deep convolutional networks and SVMs for mass detection on digital mammograms. Proceedings of the international conference on knowledge and smart technology (KST); January 2016; Chon buri, Thailand.
  45. Allugunti V. R. Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. International Journal of Engineering in Computer Science . 2022;4(1):49–56.
  46. Kim D. H., Kim S. T., Ro Y. M. Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis. Proceedings of the IEEE international conference on acoustics, speech and signal processing (ICASSP); March 2016; Shanghai, China.
  47. Yu K., Tan L., Lin L., Cheng X., Yi Z., Sato T. Deep-learning-empowered breast cancer auxiliary diagnosis for 5GB remote E-health. IEEE Wireless Communications, 2021; 28(3):54–61.
  48. D. Sisodia and D. S. Sisodia, “Prediction of diabetes using classification algorithms,” Procedia Computer Science, vol. 132, pp. 1578–1585, 2018.
  49. Polat K., Güneş S. An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing . 2007;17(4):702–710.
  50. He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; June 2016; Las Vegas, Nevada, USA.
  51. El_Jerjawi N. S., Abu-Naser S. S. Diabetes prediction using artificial neural network. International Journal of Advanced Science and Technology . 2018;121.
  52. Carrera E. V., González A., Carrera R. Automated detection of diabetic retinopathy using SVM. Proceedings of the IEEE XXIV international conference on electronics, electrical engineering and computing (INTERCON); June 2017; Cusco, Peru.
  53. Girard F., Kavalec C., Cheriet F. Joint segmentation and classification of retinal arteries/veins from fundus images. Artificial Intelligence in Medicine . 2019;94:96–109. doi: 10.1016/j.artmed.2019.02.004.
  54. Pratt H., Coenen F., Broadbent D. M., Harding S. P., Zheng Y. Convolutional neural networks for diabetic retinopathy. Procedia Computer Science . 2016;90:200–205.
  55. Zhang D., Bu W., Wu X. Diabetic retinopathy classification using deeply supervised ResNet. Proceedings of the 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing; August 2017; San Francisco, CA, USA. pp. 1–6.
  56. Sirajul Islam, M., Rouf, M. A., Shahariar Parvez, A. H. M., & Podder, P. (2022). Machine Learning-Driven Algorithms for Network Anomaly Detection. In Inventive Computation and Information Technologies: Proceedings of ICICIT 2021 (pp. 493-507). Singapore: Springer Nature Singapore.
  57. Paul, P., Bharati, S., Podder, P. & Hossain Mondal, M. (2021). 10 The role of IoMT during pandemics. In A. Khamparia, R. Hossain Mondal, P. Podder, B. Bhushan, V. Albuquerque & S. Kumar (Ed.), Computational Intelligence for Managing Pandemics (pp. 169-186). Berlin, Boston: De Gruyter.
  58. Robel, M. R. A., Bharati, S., Podder, P., Raihan-Al-Masud, M., & Mandal, S. (2021). Fault tolerance in cloud computing-an algorithmic approach. In Innovations in Bio-Inspired Computing and Applications: Proceedings of the 10th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2019) held in Gunupur, Odisha, India during December 16-18, 2019 10 (pp. 307-316). Springer.
  59. Sarker, B., Sarker, B., Podder, P., & Robel, M. R. A. (2020). Progression of Internet Banking System in Bangladesh and its Challenges. International Journal of Computer Applications, 177(29), 11-15.
  60. Wazid, M., Das, A. K., Rodrigues, J. J., Shetty, S., & Park, Y. (2019). IoMT malware detection approaches: analysis and research challenges. IEEE access, 7, 182459-182476.
  61. P. Yan and Z. Yan, “A survey on dynamic mobile malware detection,'' Softw. Qual. J., vol. 26, no. 3, pp. 891919, 2018.
  62. H. Takase, R. Kobayashi, M. Kato, and R. Ohmura, “A prototype implementation and evaluation of the malware detection mechanism for IoT devices using the processor information,'' Int. J. Inf. Secur., 2019.
  63. A. Azmoodeh, A. Dehghantanha, and K.-K. R. Choo, “Robust malware detection for Internet of (battleeld) things devices using deep Eigenspace learning,'' IEEE Trans. Sustain. Comput., vol. 4, no. 1, pp. 8895, Jan./Mar. 2019.
  64. E. M. Rudd, A. Rozsa, M. Günther, and T. E. Boult, “A survey of stealth malware attacks, mitigation measures, and steps toward autonomous open world solutions,'' IEEE Commun. Surveys Tuts., vol. 19, no. 2, pp. 1145-1172, 2nd Quart., 2017.
  65. Kamal, Tamanna, Fabiha Islam, and Mobasshira Zaman. "Designing a Warehouse with RFID and Firebase Based Android Application." Journal of Industrial Mechanics 4.1 (2019): 11-19.
  66. Parvez, Md Shohel, et al. "Anthropomorphic investigation into improved furniture fabrication and fitting for students in a Bangladeshi university." Journal of The Institution of Engineers (India): Series C 103.4 (2022): 613-622.
  67. S. Vishnu, S. R. J. Ramson and R. Jegan, "Internet of Medical Things (IoMT) - An overview," 2020 5th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India, 2020, pp. 101-104, doi: 10.1109/ICDCS48716.2020.243558.
  68. Parvez, M. S., et al. "Are library furniture dimensions appropriate for anthropometric measurements of university students?." Journal of Industrial and Production Engineering 39.5 (2022): 365-380.
  69. Hossain, Md Zakir, et al. "Evaluating the Effectiveness of a Portable Wind Generator that Produces Electricity using Wind Flow from Moving Vehicles." Journal of Industrial Mechanics 8.2 (2023): 44-53.
  70. Mondal, M. Rubaiyat Hossain, et al. "Data analytics for novel coronavirus disease." informatics in medicine unlocked 20 (2020): 100374.
  71. Bharati, S., Mondal, M., Podder, P., & Prasath, V. B. (2022). Deep learning for medical image registration: A comprehensive review. arXiv preprint arXiv:2204.11341.
  72. S M Atikur Rahman, Sifat Ibtisum, Ehsan Bazgir, Tumpa Barai, “The Significance of Machine Learning in Clinical Disease Diagnosis: A Review”, International Journal of Computer Applications (0975 – 8887), October 2023 (Accepted).
  73. S. Y. Wong, M. Y. Soh and J. M. Wong, "Internet of Medical Things:Brief Overview and the Future," 2021 IEEE 19th Student Conference on Research and Development (SCOReD), Kota Kinabalu, Malaysia, 2021, pp. 427-432.
Index Terms

Computer Science
Information Sciences

Keywords

Internet of Things (IoT) Internet of Medical Things (IoMT) cloud computing medical signals malware threats smart health care artificial intelligence machine learning (ML).

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