International Journal of Wireless Security and Networks Review Article

A Literature Review on Internet of Medical Things

  1. Shalini Sachdeva Department of Computer Science and Applications, Ram Sukh Das College Ferozepur, Firozpur
  2. Rajesh Sachdeva Department of Computer Science, Dev Samaj College for Women, Firozpur

Abstract

Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment planning, and patient management through data-driven insights and automation. The Internet of Medical Things (IoMT) represents a significant shift in modern healthcare, enabling real-time patient monitoring, data-driven decision-making, and enhanced medical outcomes. This literature review explores the architecture of IoMT, including perception layer, network layer, transport layer and application layer. It also thoroughly explores key challenges like ensuring data security, meeting regulatory requirements, and achieving seamless system interoperability. By analyzing recent studies and emerging trends, this review provides a comprehensive understanding of IoMT’s role in transforming healthcare delivery. It examines key technologies, including wearable sensors, remote monitoring systems, and AI-driven analytics, while also addressing critical concerns such as cybersecurity, interoperability, and data privacy. Moreover, this study emphasizes recent research on how the Internet of Medical Things (IoMT) influences patient health outcomes, enhances healthcare efficiency, and contributes to lowering costs.

Keywords

References (20)

  1. Fotouhi H, Causevic A, Lundqvist K, Bjorkman M. Communication and Security in Health Monitoring Systems -- A Review. 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). 2016:545-554. doi:10.1109/compsac.2016.8
  2. Satyajit Sinha. (2024 Sep 3). State of IOT 2022: Number of connected IOT devices growing 18% to 14.4 billion globally. [Online]. IoT Analytics. Retrieved February 20, 2023, from https://iot-analytics.com/number-connected-iot-devices/
  3. Cogniteq. (2022 Jan 20). Internet of medical things (IOMT): Innovative Future for Healthcare Industry. [Online]. Cogniteq. Retrieved February 20, 2023, from http://www.cogniteq.com/blog/ internet-medical-things-iomt-innovative-future-healthcare-industry
  4. Newman LH. (2022 Mar 8). Critical bugs expose hundreds of thousands of medical devices and atms. [Online]. Wired. Retrieved February 20, 2023, from https://www.wired.com/story/access7-iot- vulnerabilities-medical-devices-atms/
  5. Ravi V, Alazab M, Selvaganapathy S, Chaganti R. A Multi-View attention-based deep learning framework for malware detection in smart healthcare systems. Computer Communications. 2022;195:73-81. doi:10.1016/j.comcom.2022.08.015
  6. ICC 2021 - IEEE International Conference on Communications. 2021. doi:10.1109/icc42927.2021
  7. Ghubaish A, Salman T, Zolanvari M, Unal D, Al-Ali A, Jain R. Recent Advances in the Internet-of-Medical-Things (IoMT) Systems Security. IEEE Internet of Things Journal. 2021;8(11):8707-8718. doi:10.1109/jiot.2020.3045653
  8. Hady AA, Ghubaish A, Salman T, Unal D, Jain R. Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study. IEEE Access. 2020;8:106576-106584. doi:10.1109/access.2020.3000421
  9. Clifton L, Clifton DA, Pimentel MAF, Watkinson PJ, Tarassenko L. Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors. IEEE Journal of Biomedical and Health Informatics. 2014;18(3):722-730. doi:10.1109/jbhi.2013.2293059
  10. Rani AAV, Baburaj E. Secure and intelligent architecture for cloud-based healthcare applications in wireless body sensor networks. International Journal of Biomedical Engineering and Technology. 2019;29(2):186. doi:10.1504/ijbet.2019.097305
  11. Chakraborty S, Aich S, Kim HC. A Secure Healthcare System Design Framework using Blockchain Technology. 2019 21st International Conference on Advanced Communication Technology (ICACT). 2019:260-264. doi:10.23919/icact.2019.8701983
  12. Alabdulatif A, Khalil I, Forkan AR, Atiquzzaman M. Real-time secure health surveillance for Smarter Health Communities. IEEE Commun Mag. 2019; 57(1): 122–129. https://doi.org/10.1109/ mcom.2017.1700547
  13. Tao H, Bhuiyan MZA, Abdalla AN, Hassan MM, Zain JM, Hayajneh T. Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare. IEEE Internet of Things Journal. 2019;6(1):410-420. doi:10.1109/jiot.2018.2854714
  14. Jiong Zhang, Zulkernine M, Haque A. Random-forests-based network intrusion detection systems. IEEE Trans Syst Man Cybern Part C (Appl Rev). 2008; 38(5): 649–659. https://doi.org/10.1109/ tsmcc.2008.923876
  15. Rao BB, Swathi K, Computer Center, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, India, Computer Center, Acharya Nagarjuna University, Guntur - 522510, Andhra Pradesh, India. Fast kNN Classifiers for Network Intrusion Detection System. Indian Journal of Science and Technology. 2017;10(14):1-10. doi:10.17485/ijst/2017/v10i14/93690
  16. Shapoorifard H, Shamsinejad P. Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN. International Journal of Computer Applications. 2017;173(1):5-9. doi:10.5120/ijca2017914340
  17. Rathore H, Al-Ali AK, Mohamed A, Du X, Guizani M. A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants. IEEE Access. 2019;7:24154-24164. doi:10.1109/access.2019.2899558
  18. Yaacoub JPA, Noura M, Noura HN, Salman O, Yaacoub E, Couturier R, et al. Securing internet of medical things systems: Limitations, issues and recommendations. Future Generation Computer Systems. 2020;105:581-606. doi:10.1016/j.future.2019.12.028
  19. Saba T. Intrusion Detection in Smart City Hospitals using Ensemble Classifiers. 2020 13th International Conference on Developments in eSystems Engineering (DeSE). 2020:418-422. doi:10.1109/dese51703.2020.9450247
  20. Kumar P, Gupta GP, Tripathi R. An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Computer Communications. 2021;166:110-124. doi:10.1016/j.comcom.2020.12.003