A secured and energy-efficient system for patient e-healthcare monitoring using the Internet of Medical Things (IoMT)
DOI:
https://doi.org/10.56294/dm2024368Keywords:
Internet of Medical Things, Energy Efficiency, Security, Healthcare MonitoringAbstract
Introduction: the Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge.
Methods: this article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.
Result and Discussion: in consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.
Conclusion: in terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience
References
1. Alam S, Shuaib M, Ahmad S, Jayakody DNK, Muthanna A, Bharany S, and Elgendy IA. Blockchain-based solutions supporting reliable healthcare for fog computing and Internet of medical things (IoMT) integration. Sustainability, 14(22), pp. 1-17. https://doi.org/10.3390/su142215312.
2. Kapoor B, Nagpal B, and Alharbi M. Secured healthcare monitoring for remote patient using energy-efficient IoT sensors. Computers and Electrical Engineering, 106, pp.108585. https://doi.org/10.1016/j.compeleceng.2023.108585.
3. Zala K, Thakkar HK, Jadeja R, Singh P, Kotecha K, and Shukla M. PRMS: design and development of patients’ E-healthcare records management system for privacy preservation in third party cloud platforms. IEEE Access, 10, pp. 85777-85791. https://doi.org/10.1109/ACCESS.2022.3198094.
4. Saba T, Haseeb K, Ahmed I, and Rehman A. Secure and energy-efficient framework using Internet of Medical Things for e-healthcare. Journal of Infection and Public Health, 13(10), pp. 1567-1575. https://doi.org/10.1016/j.jiph.2020.06.027.
5. Hireche R, Mansouri H, and Pathan ASK. Security and privacy management in Internet of Medical Things (IoMT): a synthesis. Journal of Cybersecurity and Privacy, 2(3), pp.640-661. https://doi.org/10.3390/jcp2030033.
6. Kapoor B, Nagpal B, and Alharbi M. Secured healthcare monitoring for remote patient using energy-efficient IoT sensors. Computers and Electrical Engineering, 106, pp.108585. https://doi.org/10.1016/j.compeleceng.2023.108585.
7. Bharathi R, Abirami T, Dhanasekaran S, Gupta D, Khanna A, Elhoseny M, and Shankar K. Energy efficient clustering with disease diagnosis model for IoT based sustainable healthcare systems. Sustainable Computing: Informatics and Systems, 28, pp. 100453. https://doi.org/10.1016/j.suscom.2020.100453.
8. Yaacoub JPA, Noura M, Noura HN, Salman O, Yaacoub E, Couturier R, and Chehab A. Securing internet of medical things systems: Limitations, issues and recommendations. Future Generation Computer Systems, 105, pp. 581-606. https://doi.org/10.1016/j.future.2019.12.028.
9. Hireche R, Mansouri H, and Pathan ASK. Security and privacy management in Internet of Medical Things (IoMT): a synthesis. Journal of Cybersecurity and Privacy, 2(3), pp. 640-661. https://doi.org/10.3390/jcp2030033.
10. Kapoor B, Nagpal B, and Alharbi M. Secured healthcare monitoring for remote patient using energy-efficient IoT sensors. Computers and Electrical Engineering, 106, pp. 108585. https://doi.org/10.1016/j.compeleceng.2023.108585.
11. Sodhro AH, Al-Rakhami MS, Wang L, Magsi H, Zahid N, Pirbhulal S, Nisar K, and Ahmad A. Decentralized energy efficient model for data transmission in IoT-based healthcare system. In IEEE 93rd vehicular technology conference (VTC2021-Spring), pp. 1-5. https://doi.org/10.1109/VTC2021-Spring51267.2021.9448886.
12. Ray PP, Dash D, and Kumar N. Sensors for internet of medical things: State-of-the-art, security and privacy issues, challenges and future directions. Computer Communications, 160, pp. 111-131. https://doi.org/10.1016/j.comcom.2020.05.029.
13. Vora J, DevMurari P, Tanwar S, Tyagi S, Kumar N, and Obaidat MS. Blind signatures based secured e-healthcare system. In International conference on computer, information and telecommunication systems (CITS), pp. 1-5. https://doi.org/10.1109/CITS.2018.8440186.
14. Sun Y, Lo FPW, and Lo B. Security and privacy for the internet of medical things enabled healthcare systems: A survey. IEEE Access, 7, pp. 183339-183355. https://doi.org/10.1109/ACCESS.2019.2960617.
15. Deebak BD, and Al-Turjman F. Smart mutual authentication protocol for cloud based medical healthcare systems using internet of medical things. IEEE Journal on Selected Areas in Communications, 39(2), pp. 346-360. https://doi.org/10.1109/JSAC.2020.3020599.
16. Ullah A, Azeem M, Ashraf H, Alaboudi AA, Humayun M, and Jhanjhi NZ. Secure healthcare data aggregation and transmission in IoT—A survey. IEEE Access, 9, pp. 16849-16865. https://doi.org/10.1109/ACCESS.2021.3052850.
17. Abbas A, Alroobaea R, Krichen M, Rubaiee S, Vimal S, and Almansour FM. Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal and ubiquitous computing, 28(1), pp. 59-72. https://doi.org/10.1007/s00779-021-01583-8.
18. Liu M, Yu FR, Teng Y, Leung VC, and Song M. Performance optimization for blockchain-enabled industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 15(6), pp. 3559-3570. https://doi.org/10.1109/TII.2019.2897805.
19. Patel WD, Pandya S, Koyuncu B, Ramani B, Bhaskar S, and Ghayvat H. NXTGeUH: LoRaWAN based NEXT generation ubiquitous healthcare system for vital signs monitoring & falls detection. In IEEE Punecon, pp. 1-8. https://doi.org/10.1109/PUNECON.2018.8745431.
20. Alotaibi M, and Alotaibi SS. Optimal disease diagnosis in internet of things (IoT) based healthcare system using energy efficient clustering. Applied Sciences, 12(8), pp. 1-16. https://doi.org/10.3390/app12083804.
21. Singla R, Kaur N, Koundal D, and Bharadwaj A. Challenges and developments in secure routing protocols for healthcare in WBAN: A comparative analysis. Wireless Personal Communications, pp.1-40. https://doi.org/10.1007/s11277-021-08969-0.
22. Haseeb K, Ahmad I, Awan II, Lloret J, and Bosch I. A machine learning SDN-enabled big data model for IoMT systems. Electronics, 10(18), pp. 1-13. https://doi.org/10.3390/electronics10182228.
Published
Issue
Section
License
Copyright (c) 2024 Veera V Rama Rao M, Kiran Sree Pokkuluri, N. Raghava Rao, Sureshkumar S, Balakrishnan S, Shankar A (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.