Survey Special Issues

Toward the Internet of Medical Things: Architecture, trends and challenges


  • Received: 23 August 2023 Revised: 15 October 2023 Accepted: 03 November 2023 Published: 19 December 2023
  • In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.

    Citation: Qinwang Niu, Haoyue Li, Yu Liu, Zhibo Qin, Li-bo Zhang, Junxin Chen, Zhihan Lyu. Toward the Internet of Medical Things: Architecture, trends and challenges[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 650-678. doi: 10.3934/mbe.2024028

    Related Papers:

  • In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.



    加载中


    [1] W. Hou, Z. Ning, L. Guo, X. Zhang, Temporal, functional and spatial big data computing framework for large-scale smart grid, IEEE Trans. Emerging Top. Comput., 7 (2019), 369–379. https://doi.org/10.1109/TETC.2017.2681113 doi: 10.1109/TETC.2017.2681113
    [2] B. V. Vishakh, M. K. Khwaja, Wearable device for hearing impaired individuals using ZigBee protocol, in 2015 9th Asia Modelling Symposium (AMS), (2015), 181–184. https://doi.org/10.1109/AMS.2015.37
    [3] A. I. Hussein, Wearable computing: Challenges of implementation and itsfuture, in 2015 12th Learning and Technology Conference, (2015), 14–19. https://doi.org/10.1109/LT.2015.7587224
    [4] L. M. Koonin, B. Hoots, C. A. Tsang, Z. Leroy, K. Farris, B. T. Jolly, et al., Trends in the use of telehealth during the emergence of the COVID-19 pandemic-United States, January–March 2020, Morb. Mortal. Wkly. Rep., 69 (2020), 1595–1599. https://doi.org/10.15585%2Fmmwr.mm6943a3
    [5] Y. Mehmood, F. Ahmad, I. Yaqoob, A. Adnane, M. Imran, S. Guizani, Internet-of-things-based smart cities: Recent advances and challenges, IEEE Commun. Mag., 55 (2017), 16–24. https://doi.org/10.1109/MCOM.2017.1600514 doi: 10.1109/MCOM.2017.1600514
    [6] O. AlShorman, B. AlShorman, M. Al-khassaweneh, F. Alkahtani, A review of internet of medical things (iomt)-based remote health monitoring through wearable sensors: a case study for diabetic patients, Indones. J. Electr. Eng. Comput. Sci., 20 (2020), 414–422. https://doi.org/10.11591/IJEECS.V20.I1.PP414-422 doi: 10.11591/IJEECS.V20.I1.PP414-422
    [7] M. A. U. Khalid, S. H. Chang, Flexible strain sensors for wearable applications fabricated using novel functional nanocomposites: A review, Compos. Struct., 284 (2022), 115214. https://doi.org/10.1016/j.compstruct.2022.115214 doi: 10.1016/j.compstruct.2022.115214
    [8] F. J. Tovar-Lopez, Recent progress in micro-and nanotechnology-enabled sensors for biomedical and environmental challenges, Sensors, 23 (2023), 5406. https://doi.org/10.3390/s23125406 doi: 10.3390/s23125406
    [9] F. Ju, Y. Wang, B. Yin, M. Zhao, Y. Zhang, Y. Gong, et al., Microfluidic wearable devices for sports applications, Micromachines, 14 (2023), 1792. https://doi.org/10.3390/mi14091792 doi: 10.3390/mi14091792
    [10] W. Wang, Fusion application of cloud computing technology in the field of artificial intelligence, in 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, (2021), 289–292. https://doi.org/10.1145/3495018.3495067
    [11] K. Ahmed, M. Gregory, Integrating wireless sensor networks with cloud computing, in 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks, (2011), 364–366. https://doi.org/10.1109/MSN.2011.86
    [12] M. Kumar, An incorporation of artificial intelligence capabilities in cloud computing, Int. J. Eng. Comput. Sci., 5 (2016), 19070–19073. https://doi.org/10.18535/ijecs/v5i11.63 doi: 10.18535/ijecs/v5i11.63
    [13] S. Razdan, S. Sharma, Internet of medical things (IoMT): Overview, emerging technologies, and case studies, IETE Tech. Rev., 39 (2022), 775-788. https://doi.org/10.1080/02564602.2021.1927863 doi: 10.1080/02564602.2021.1927863
    [14] X. Jin, B. W. Wah, X. Cheng, Y. Wang, Significance and challenges of big data research, Big Data Res., 2 (2015), 59–64. https://doi.org/10.1016/j.bdr.2015.01.006 doi: 10.1016/j.bdr.2015.01.006
    [15] A. Katal, M. Wazid, R. H. Goudar, Big data: issues, challenges, tools and good practices, in 2013 Sixth International Conference on Contemporary Computing (IC3), (2013), 404–409. https://doi.org/10.1109/IC3.2013.6612229
    [16] W. Hu, Big Data Management, Technologies, and Applications, IGI Global, Pennsylvania, 2013.
    [17] N. Shakhovska, N. Boyko, Y. Zasoba, E. Benova, Big data processing technologies in distributed information systems, Proc. Comput. Sci., 160 (2019), 561–566. https://doi.org/10.1016/j.procs.2019.11.047 doi: 10.1016/j.procs.2019.11.047
    [18] V. Storey, I. Song, Big data technologies and management: What conceptual modeling can do, Data Knowl. Eng., 108 (2017), 50–67. https://doi.org/10.1016/j.datak.2017.01.001 doi: 10.1016/j.datak.2017.01.001
    [19] C. Yang, Q. Huang, Z. Li, K. Liu, F. Hu, Big data and cloud computing: Innovation opportunities and challenges, Int. J. Digital Earth, 10 (2017), 13–53. https://doi.org/10.1080/17538947.2016.1239771 doi: 10.1080/17538947.2016.1239771
    [20] M. Hajibaba, S. Gorgin, A review on modern distributed computing paradigms: Cloud computing, jungle computing and fog computing, J. Comput. Inf. Technol., 22 (2014), 69–84. https://doi.org/10.2498/cit.1002381 doi: 10.2498/cit.1002381
    [21] S. Goyal, Public vs private vs hybrid vs community-cloud computing: a critical review, Int. J. Comput. Network Inf. Secur., 6 (2014), 20–29. https://doi.org/10.5815/ijcnis.2014.03.03 doi: 10.5815/ijcnis.2014.03.03
    [22] X. He, G. Qi, Z. Zhu, Y. Li, B. Cong, L. Bai, Medical image segmentation method based on multi-feature interaction and fusion over cloud computing, Simul. Modell. Pract. Theory, 126 (2023), 102769. https://doi.org/10.1016/j.simpat.2023.102769 doi: 10.1016/j.simpat.2023.102769
    [23] M. N. O. Sadiku, S. M. Musa, O. D. Momoh, Cloud computing: opportunities and challenges, IEEE Potentials, 33 (2014), 34–36. https://doi.org/10.1109/MPOT.2013.2279684 doi: 10.1109/MPOT.2013.2279684
    [24] S. Zhang, H. Yan, X. Chen, Research on key technologies of cloud computing, Phys. Proc., 33 (2012), 1791–1797. https://doi.org/10.1016/j.phpro.2012.05.286 doi: 10.1016/j.phpro.2012.05.286
    [25] P. Kalagiakos, P. Karampelas, Cloud computing learning, in 2011 5th International Conference on Application of Information and Communication Technologies (AICT), (2011), 1–4. https://doi.org/10.1109/ICAICT.2011.6110925
    [26] T. Hu, H. Chen, L. Huang, X. Zhu, A survey of mass data mining based on cloud-computing, Anti-counterfeiting Secur. Identif., (2012), 1–4. https://doi.org/10.1109/ICASID.2012.6325353 doi: 10.1109/ICASID.2012.6325353
    [27] H. Nashaat, N. Ashry, R. Rizk, Smart elastic scheduling algorithm for virtual machine migration in cloud computing, J. Supercomput., 75 (2019), 3842–3865. https://doi.org/10.1007/s11227-019-02748-2 doi: 10.1007/s11227-019-02748-2
    [28] Statista, Amazon Maintains Lead in the Cloud Market, 2023. Available from: https://www.statista.com/chart/18819/worldwide-market-share-of-leading-cloud-infrastructure-service-providers/.
    [29] R. Hammad, M. Barhoush, B. H. Abed-Alguni, A semantic-based approach for managing healthcare big data: A survey, J. Healthcare Eng., 2020 (2020), 8865808. https://doi.org/10.1155/2020/8865808 doi: 10.1155/2020/8865808
    [30] R. Lin, Z. Ye, H. Wang, B. Wu, Chronic diseases and health monitoring big data: A survey, IEEE Rev. Biomed. Eng., 11 (2018), 275–288. https://doi.org/10.1109/RBME.2018.2829704 doi: 10.1109/RBME.2018.2829704
    [31] L. Sun, X. Jiang, H. Ren, Y. Guo, Edge-cloud computing and artificial intelligence in internet of medical things: architecture, technology and application, IEEE Access, 8 (2020), 101079–101092. https://doi.org/10.1109/ACCESS.2020.2997831 doi: 10.1109/ACCESS.2020.2997831
    [32] H. V. Jagadish, J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, et al., Big data and its technical challenges, Commun. ACM, 57 (2014), 86–94. https://doi.org/10.1145/2611567 doi: 10.1145/2611567
    [33] C. H. Lee, H. Yoon, Medical big data: promise and challenges, Kidney Res. Clin. Pract., 36 (2017), 3–11. https://doi.org/10.23876/j.krcp.2017.36.1.3 doi: 10.23876/j.krcp.2017.36.1.3
    [34] P. Langkafel, Big Data in Medical Science and Healthcare Management: Diagnosis, Therapy, Side Effects, De Gruyter, Boston, 2016. https://doi.org/10.1515/9783110445749
    [35] X. Xu, C. Li, X. Lan, X. Fan, X. Lv, X. Ye, et al., A lightweight and robust framework for circulating genetically abnormal cells (CACs) identification using 4-color fluorescence in situ hybridization (FISH) image and deep refined learning, J. Digit. Imaging, 36 (2023), 1687–1700. https://doi.org/10.1007/s10278-023-00843-8 doi: 10.1007/s10278-023-00843-8
    [36] X. Xu, C. Li, X. Fan, X. Lan, X. Lu, X. Ye, et al., Attention mask r-cnn with edge refinement algorithm for identifying circulating genetically abnormal cells, Cytom. Part A, 103 (2023), 227–239. https://doi.org/10.1002/cyto.a.24682 doi: 10.1002/cyto.a.24682
    [37] W. Wang, J. Chen, J. Wang, J. Chen, Z. Gong, Geography-aware inductive matrix completion for personalized point of interest recommendation in smart cities, IEEE Internet Things J., 7 (2020), 4361–4370. https://doi.org/10.1109/JIOT.2019.2950418 doi: 10.1109/JIOT.2019.2950418
    [38] W. Wang, J. Chen, J. Wang, J. Chen, J. Liu, Z. Gong, Trust-enhanced collaborative filtering for personalized point of interests recommendation, IEEE Trans. Ind. Inf., 16 (2020), 6124–6132. https://doi.org/10.1109/TII.2019.2958696 doi: 10.1109/TII.2019.2958696
    [39] W. Wang, N. Kumar, J. Chen, Z. Gong, X. Kong, W. Wei, et al., Realizing the potential of the internet of things for smart tourism with 5G and AI, IEEE Network, 34 (2020), 295–301. https://doi.org/10.1109/MNET.011.2000250 doi: 10.1109/MNET.011.2000250
    [40] W. Wang, X. Yu, B. Fang, Y. Zhao, Y. Chen, W. Wei, et al., Cross-modality LGE-CMR segmentation using image-to-image translation based data augmentation, IEEE/ACM Trans. Comput. Biol. Bioinf., 20 (2023), 2367–2375. https://doi.org/10.1109/TCBB.2022.3140306 doi: 10.1109/TCBB.2022.3140306
    [41] J. Chen, Z. Guo, X. Xu, L. Zhang, Y. Teng, Y. Chen, et al., A robust deep learning framework based on spectrograms for heart sound classification, IEEE/ACM Trans. Comput. Biol. Bioinf., (2023), 1–12. https://doi.org/10.1109/TCBB.2023.3247433 doi: 10.1109/TCBB.2023.3247433
    [42] P. Manickam, S. A. Mariappan, S. M. Murugesan, S. Hansda, A. Kaushik, R. Shinde, et al., Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare, Biosensors, 12 (2022), 562. https://doi.org/10.3390/bios12080562 doi: 10.3390/bios12080562
    [43] Z. Ning, P. Dong, X. Wang, J. J. Rodrigues, F. Xia, Deep reinforcement learning for vehicular edge computing: An intelligent offloading system, ACM Trans. Intell. Syst. Technol., 10 (2019), 1–24. https://doi.org/10.1145/3317572 doi: 10.1145/3317572
    [44] Z. Ning, P. Dong, X. Wang, M. S. Obaidat, X. Hu, L. Guo, et al., When deep reinforcement learning meets 5G-enabled vehicular networks: A distributed offloading framework for traffic big data, IEEE Trans. Ind. Inf., 16 (2020), 1352–1361. https://doi.org/10.1109/TII.2019.2937079 doi: 10.1109/TII.2019.2937079
    [45] M. N. Hossen, V. Panneerselvam, D. Koundal, K. Ahmed, F. M. Bui, S. M. Ibrahim, Federated machine learning for detection of skin diseases and enhancement of internet of medical things (IoMT) security, IEEE J. Biomed. Health. Inf., 27 (2022), 835–841. https://doi.org/10.1109/JBHI.2022.3149288 doi: 10.1109/JBHI.2022.3149288
    [46] A. Cuevas-Chávez, Y. Hernández, J. Ortiz-Hernandez, E. Sánchez-Jiménez, G. Ochoa-Ruiz, J. Pérez, et al., A systematic review of machine learning and IoT applied to the prediction and monitoring of cardiovascular diseases, Healthcare, 11 (2023), 2240. https://doi.org/10.3390/healthcare11162240 doi: 10.3390/healthcare11162240
    [47] A. E. Hassanien, A. Khamparia, D. Gupta, K. Shankar, A. Slowik, Cognitive Internet of Medical Things for Smart Healthcare, Springer, Cham, 2021. https://doi.org/10.1007/978-3-030-55833-8_9
    [48] A. L. N. Al-Hajjar, A. K. M. Al-Qurabat, An overview of machine learning methods in enabling iomt-based epileptic seizure detection, J. Supercomput., 79 (2023), 16017–16064. https://doi.org/10.1007/s11227-023-05299-9 doi: 10.1007/s11227-023-05299-9
    [49] W. Zhao, Y. Wang, X. Sun, S. Zhang, X. Li, IoMT-based seizure detection system leveraging edge machine learning, IEEE Sens. J., 23 (2023), 21474–21483. https://doi.org/10.1109/JSEN.2023.3300743 doi: 10.1109/JSEN.2023.3300743
    [50] T. M. Ghazal, S. Abbas, M. Ahmad, S. Aftab, An IoMT based ensemble classification framework to predict treatment response in hepatitis C patients, in 2022 International Conference on Business Analytics for Technology and Security (ICBATS), (2022), 1–4. https://doi.org/10.1109/ICBATS54253.2022.9759059
    [51] A. S. Rajawat, S. Goyal, P. Bedi, T. Jan, M. Whaiduzzaman, M. Prasad, Quantum machine learning for security assessment in the internet of medical things (IoMT), Future Internet, 15 (2023), 271. https://doi.org/10.3390/fi15080271 doi: 10.3390/fi15080271
    [52] A. Si-Ahmed, M. A. Al-Garadi, N. Boustia, Survey of machine learning based intrusion detection methods for internet of medical things, Appl. Soft Comput., 140 (2023), 110227. https://doi.org/10.1016/j.asoc.2023.110227 doi: 10.1016/j.asoc.2023.110227
    [53] V. Chang, J. Bailey, Q. A. Xu, Z. Sun, Pima indians diabetes mellitus classification based on machine learning (ML) algorithms, Neural Comput. Appl., 35 (2023), 16157–16173. https://doi.org/10.1007/s00521-022-07049-z doi: 10.1007/s00521-022-07049-z
    [54] C. Iwendi, S. Khan, J. H. Anajemba, A. K. Bashir, F. Noor, Realizing an efficient iomt-assisted patient diet recommendation system through machine learning model, IEEE Access, 8 (2020), 28462–28474. https://doi.org/10.1109/ACCESS.2020.2968537 doi: 10.1109/ACCESS.2020.2968537
    [55] T. Mishra, M. Wang, A. A. Metwally, G. K. Bogu, A. W. Brooks, A. Bahmani, et al., Pre-symptomatic detection of COVID-19 from smartwatch data, Nat. Biomed. Eng., 4 (2020), 1208–1220. https://doi.org/10.1038/s41551-020-00640-6 doi: 10.1038/s41551-020-00640-6
    [56] F. Li, M. Valero, H. Shahriar, R. A. Khan, S. I. Ahamed, Wi-COVID: A COVID-19 symptom detection and patient monitoring framework using WiFi, Smart Health, 19 (2021), 100147. https://doi.org/10.1016/j.smhl.2020.100147 doi: 10.1016/j.smhl.2020.100147
    [57] M. Otoom, N. Otoum, M. A. Alzubaidi, Y. Etoom, R. Banihani, An IoT-based framework for early identification and monitoring of COVID-19 cases, Biomed. Signal Process. Control, 62 (2020), 102149. https://doi.org/10.1016/j.bspc.2020.102149 doi: 10.1016/j.bspc.2020.102149
    [58] S. Venkatasubramanian, Ambulatory monitoring of maternal and fetal using deep convolution generative adversarial network for smart health care IoT system, Int. J. Adv. Comput. Sci. Appl., 13 (2022), 214–222. https://doi.org/10.14569/IJACSA.2022.0130126 doi: 10.14569/IJACSA.2022.0130126
    [59] P. K. Vemuri, A. Kunta, R. Challagulla, S. Bodiga, S. Veeravilli, V. L. Bodiga, et al., Artificial intelligence and internet of medical things based health-care system for real-time maternal stress-strategies to reduce maternal mortality rate, Drug Invent. Today, 13 (2020), 1126–1129. http://dx.doi.org/10.6084/m9.figshare.13213631 doi: 10.6084/m9.figshare.13213631
    [60] X. Li, Y. Lu, X. Fu, Y. Qi, Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing, Future Gener. Comput. Syst., 118 (2021), 282–296. https://doi.org/10.1016/j.future.2021.01.016 doi: 10.1016/j.future.2021.01.016
    [61] Y. Hao, R. Foster, Wireless body sensor networks for health-monitoring applications, Physiol. Meas., 29 (2008), 27. https://doi.org/10.1088/0967-3334/29/11/R01 doi: 10.1088/0967-3334/29/11/R01
    [62] S. Li, B. Zhang, P. Fei, P. M. Shakeel, R. D. J. Samuel, WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT, Aggress Violent Behav., (2020), 101541. https://doi.org/10.1016/j.avb.2020.101541 doi: 10.1016/j.avb.2020.101541
    [63] X. Shi, Z. Huang, Wearable device monitoring exercise energy consumption based on Internet of things, Complexity, 2021 (2021), 8836723. https://doi.org/10.1155/2021/8836723 doi: 10.1155/2021/8836723
    [64] J. Chen, S. Sun, L. Zhang, B. Yang, W. Wang, Compressed sensing framework for heart sound acquisition in internet of medical things, IEEE Trans. Ind. Inf., 18 (2022), 2000–2009. https://doi.org/10.1109/TII.2021.3088465 doi: 10.1109/TII.2021.3088465
    [65] Y. Yao, H. Wu, L. Shu, C. Lu, Developing a multifunctional heating pad based on fuzzy-edge computations and IoMT approach, J. Internet Technol., 23 (2022), 1519–1525. https://doi.org/10.53106/160792642022122307007 doi: 10.53106/160792642022122307007
    [66] A. N. Trunov, I. M. Dronyuk, V. S. Martynenko, S. I. Maltsev, I. V. Skopenko, M. Y. Skoroid, Formation of a recurrent neural network for the description of IoMT processes in restorative medicine for post-stroke patients, in AI Models for Blockchain-Based Intelligent Networks in IoT Systems, 6 (2023), 185–202. https://doi.org/10.1007/978-3-031-31952-5_9
    [67] S. Shaji, R. Sankaran, R. Guntha, R. K. Pathinarupothi, A real-time IoMT enabled remote cardiac rehabilitation framework, in 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS), (2023), 153–158. https://doi.org/10.1109/COMSNETS56262.2023.10041272
    [68] A. Buzachis, G. M. Bernava, M. Busa, G. Pioggia, M. Villari, Towards the basic principles of osmotic computing: a closed-loop gamified cognitive rehabilitation flow model, in 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), (2018), 446–452. https://doi.org/10.1109/CIC.2018.00067
    [69] N. Yadav, F. Keshtkar, C. Schweikert, G. Crocetti, Cradle: An IoMT psychophysiological analytics platform, in Proceedings of the Workshop on Human-Habitat for Health (H3): Human-Habitat Multimodal Interaction for Promoting Health and Well-Being in the Internet of Things Era, (2018), 1–7. https://doi.org/10.1145/3279963.3279970
    [70] N. Yadav, Y. Jin, L. J. Stevano, AR-IoMT mental health rehabilitation applications for smart cities, in 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT), (2019), 166–170. https://doi.org/10.1109/HONET.2019.8907997
    [71] J. Chen, L. Chen, Y. Zhou, Cryptanalysis of a dna-based image encryption scheme, Inf. Sci., 520 (2020), 130–141. https://doi.org/10.1016/j.ins.2020.02.024 doi: 10.1016/j.ins.2020.02.024
    [72] J. Chen, Z. Zhu, L. Zhang, Y. Zhang, B. Yang, Exploiting self-adaptive permutation-diffusion and DNA random encoding for secure and efficient image encryption, Signal Process., 142 (2018), 340–353. https://doi.org/10.1016/j.sigpro.2017.07.034 doi: 10.1016/j.sigpro.2017.07.034
    [73] L. Liu, B. Xu, Research on information security technology based on blockchain, in 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), (2018), 380–384. https://doi.org/10.1109/ICCCBDA.2018.8386546
    [74] C. Zhang, C. Wu, X. Wang, Overview of blockchain consensus mechanism, in Proceedings of the 2020 2nd International Conference on Big Data Engineering, (2020), 7–12. https://doi.org/10.1145/3404512.3404522
    [75] S. Kaur, S. Chaturvedi, A. Sharma, J. Kar, A research survey on applications of consensus protocols in blockchain, Secur. Commun. Networks, 2021 (2021), 6693731. https://doi.org/10.1155/2021/6693731 doi: 10.1155/2021/6693731
    [76] P. Chinnasamy, P. Deepalakshmi, V. Praveena, K. Rajakumari, P. Hamsagayathri, Blockchain technology: A step towards sustainable development, Int. J. Innovative Technol. Explor. Eng., 9 (2019), 1034–1040. https://doi.org/10.35940/ijitee.b1109.1292s219 doi: 10.35940/ijitee.b1109.1292s219
    [77] Y. Cui, B. Pan, Y. Sun, A survey of privacy-preserving techniques for blockchain, in International Conference on Artificial Intelligence and Security, 11635 (2019), 225–234. https://doi.org/10.1007/978-3-030-24268-8_21
    [78] A. Ghosh, S. Gupta, A. Dua, N. Kumar, Security of cryptocurrencies in blockchain technology: State-of-art, challenges and future prospects, J. Network Comput. Appl., 163 (2020), 102635. https://doi.org/10.1016/j.jnca.2020.102635 doi: 10.1016/j.jnca.2020.102635
    [79] B. A. Tama, B. J. Kweka, Y. Park, K. Rhee, A critical review of blockchain and its current applications, in 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), (2017), 109–113. https://doi.org/10.1109/ICECOS.2017.8167115
    [80] P. Ratta, A. Kaur, S. Sharma, M. Shabaz, G. Dhiman, Application of blockchain and internet of things in healthcare and medical sector: Applications, challenges, and future perspectives, J. Food Qual., 2021 (2021), 7608296. https://doi.org/10.1155/2021/7608296 doi: 10.1155/2021/7608296
    [81] I. Yaqoob, K. Salah, R. Jayaraman, Y. Al-Hammadi, Blockchain for healthcare data management: Opportunities, challenges, and future recommendations, Neural Comput. Appl., 34 (2022), 11475–11490. https://doi.org/10.1007/s00521-020-05519-w doi: 10.1007/s00521-020-05519-w
    [82] Z. Zhang, L. Zhao, A design of digital rights management mechanism based on blockchain technology, in International Conference on Blockchain, 10974 (2018), 32–46. https://doi.org/10.1007/978-3-319-94478-4_3
    [83] W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, A. Ahmed, Edge computing: A survey, Future Gener. Comput. Syst., 97 (2019), 219–235. https://doi.org/10.1016/j.future.2019.02.050 doi: 10.1016/j.future.2019.02.050
    [84] W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu, Edge computing: Vision and challenges, IEEE Internet Things J., 3 (2016), 637–646. https://doi.org/10.1109/JIOT.2016.2579198 doi: 10.1109/JIOT.2016.2579198
    [85] S. Wang, Edge computing: Applications, state-of-the-art and challenges, Adv. Networks, 7 (2019), 8–15. https://doi.org/10.11648/j.net.20190701.12 doi: 10.11648/j.net.20190701.12
    [86] W. Shi, S. Dustdar, The promise of edge computing, Computer, 49 (2016), 78–81. https://doi.org/10.1109/MC.2016.145 doi: 10.1109/MC.2016.145
    [87] A. A. Abdellatif, A. Mohamed, C. F. Chiasserini, M. Tlili, A. Erbad, Edge computing for smart health: Context-aware approaches, opportunities, and challenges, IEEE Networks, 33 (2019), 196–203. https://doi.org/10.1109/MNET.2019.1800083 doi: 10.1109/MNET.2019.1800083
    [88] P. P. Ray, D. Dash, D. De, Edge computing for internet of things: A survey, e-healthcare case study and future direction, J. Network Comput. Appl., 140 (2019), 1–22. https://doi.org/10.1016/j.jnca.2019.05.005 doi: 10.1016/j.jnca.2019.05.005
    [89] S. M. Kumar, D. Majumder, Healthcare solution based on machine learning applications in IoT and edge computing, Int. J. Pure Appl. Math., 119 (2018), 1473–1484.
    [90] K. Subramanian, Digital twin for drug discovery and development-The virtual liver, J. Indian Inst. Sci., 100 (2020), 653–662. https://doi.org/10.1007/s41745-020-00185-2 doi: 10.1007/s41745-020-00185-2
    [91] B. Björnsson, C. Borrebaeck, N. Elander, T. Gasslander, D. R. Gawel, M. Gustafsson, et al., Digital twins to personalize medicine, Genome Med., 12 (2020). https://doi.org/10.1186/s13073-019-0701-3 doi: 10.1186/s13073-019-0701-3
    [92] Y. Tai, L. Zhang, Q. Li, C. Zhu, V. Chang, J. J. P. C. Rodrigues, et al., Digital-twin-enabled IoMT system for surgical simulation using rAC-GAN, IEEE Internet Things J., 9 (2022), 20918–20931. https://doi.org/10.1109/JIOT.2022.3176300 doi: 10.1109/JIOT.2022.3176300
    [93] Q. Qu, H. Sun, Y. Chen, Light-weight real-time senior safety monitoring using digital twins, in Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, (2023), 450–451. https://doi.org/10.1145/3576842.3589163
    [94] O. Moztarzadeh, M. Jamshidi, S. Sargolzaei, A. Jamshidi, N. Baghalipour, M. M. Moghani, et al., Metaverse and healthcare: Machine learning-enabled digital twins of cancer, Bioengineering, 10 (2023), 455. https://doi.org/10.3390/bioengineering10040455 doi: 10.3390/bioengineering10040455
    [95] Z. Qu, Y. Li, B. Liu, D. Gupta, P. Tiwari, Dtqfl: A digital twin-assisted quantum federated learning algorithm for intelligent diagnosis in 5G mobile network, IEEE J. Biomed. Health Inf., (2023), 1–10. https://doi.org/10.1109/JBHI.2023.3303401 doi: 10.1109/JBHI.2023.3303401
    [96] Y. Liu, L. Zhang, Y. Yang, L. Zhou, L. Ren, F. Wang, et al., A novel cloud-based framework for the elderly healthcare services using digital twin, IEEE Access, 7 (2019), 49088–49101. https://doi.org/10.1109/ACCESS.2019.2909828 doi: 10.1109/ACCESS.2019.2909828
    [97] Z. Lou, L. Wang, K. Jiang, Z. Wei, G. Shen, Reviews of wearable healthcare systems: Materials, devices and system integration, Mater. Sci. Eng. R Rep., 140 (2020), 100523. https://doi.org/10.1016/j.mser.2019.100523 doi: 10.1016/j.mser.2019.100523
    [98] G. Medic, M. Wille, M. E. Hemels, Short-and long-term health consequences of sleep disruption, Nat. Sci. Sleep, 9 (2017), 151–161. https://doi.org/10.2147/NSS.S134864 doi: 10.2147/NSS.S134864
    [99] V. P. Tran, A. A. Al-Jumaily, S. M. S. Islam, Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review, Big Data Cognit. Comput., 3 (2019), 3. https://doi.org/10.3390/bdcc3010003 doi: 10.3390/bdcc3010003
    [100] L. Ismail, R. Buyya, Artificial intelligence applications and self-learning 6G networks for smart cities digital ecosystems: Taxonomy, challenges, and future directions, Sensors, 22 (2022), 5750. https://doi.org/10.3390/s22155750 doi: 10.3390/s22155750
    [101] X. Lin, J. Wu, A. K. Bashir, W. Yang, A. Singh, A. A. AlZubi, Fairhealth: Long-term proportional fairness-driven 5G edge healthcare in internet of medical things, IEEE Trans. Ind. Inf., 18 (2022), 8905–8915. https://doi.org/10.1109/TII.2022.3183000 doi: 10.1109/TII.2022.3183000
    [102] L. Kouhalvandi, L. Matekovits, I. Peter, Magic of 5G technology and optimization methods applied to biomedical devices: A survey, Appl. Sci., 12 (2022), 7096. https://doi.org/10.3390/app12147096 doi: 10.3390/app12147096
    [103] M. Malik, S. K. Garg, Towards 6G: Network evolution beyond 5G & indian scenario, in 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), (2022), 123–127. https://doi.org/10.1109/ICIPTM54933.2022.9753847
    [104] S. T. Ahmed, V. V. Kumar, K. K. Singh, A. Singh, V. Muthukumaran, D. Gupta, 6G enabled federated learning for secure IoMT resource recommendation and propagation analysis, Comput. Electr. Eng., 102 (2022), 108210. https://doi.org/10.1016/j.compeleceng.2022.108210 doi: 10.1016/j.compeleceng.2022.108210
    [105] P. N. Srinivasu, M. F. Ijaz, J. Shafi, M.Woźniak, R. Sujatha, 6G driven fast computational networking framework for healthcare applications, IEEE Access, 10 (2022), 94235–94248. https://doi.org/10.1109/ACCESS.2022.3203061 doi: 10.1109/ACCESS.2022.3203061
    [106] A. Koren, R. Prasad, IoT health data in electronic health records (EHR): Security and privacy issues in era of 6G, J. ICT Stand., 10 (2022), 63–84. https://doi.org/10.13052/jicts2245-800X.1014 doi: 10.13052/jicts2245-800X.1014
    [107] I. U. Din, M. Guizani, S. Hassan, B. Kim, M. K. Khan, M. Atiquzzaman, et al., The Internet of Things: A review of enabled technologies and future challenges, IEEE Access, 7 (2018), 7606–7640. https://doi.org/10.1109/ACCESS.2018.2886601 doi: 10.1109/ACCESS.2018.2886601
    [108] S. Nasiri, F. Sadoughi, M. H. Tadayon, A. Dehnad, Security requirements of internet of things-based healthcare system: A survey study, Acta Inf. Med., 27 (2019), 253–258. https://doi.org/10.5455/aim.2019.27.253-258 doi: 10.5455/aim.2019.27.253-258
    [109] J. Granjal, E. Monteiro, J. S. Silva, Security for the internet of things: A survey of existing protocols and open research issues, IEEE Commun. Surv. Tutorials, 17 (2015), 1294–1312. https://doi.org/10.1109/COMST.2015.2388550 doi: 10.1109/COMST.2015.2388550
    [110] S. Alasmari, M. Anwar, Security & privacy challenges in IoT-based health cloud, in 2016 International Conference on Computational Science and Computational Intelligence (CSCI), (2016), 198–201. https://doi.org/10.1109/CSCI.2016.0044
    [111] S. Agrawal, K. Sharma, Software defined millimeter wave 5th generation communications system, Appl. Theory Comput. Technol., 2 (2017), 46–56.
    [112] T. Lin, C. Hsu, T. Le, C. Lu, B. Huang, A smartcard-based user-controlled single sign-on for privacy preservation in 5G-IoT telemedicine systems, Sensors, 21 (2021), 2880. https://doi.org/10.3390/s21082880 doi: 10.3390/s21082880
    [113] S. H. Alsamhi, B. Lee, Blockchain-empowered multi-robot collaboration to fight COVID-19 and future pandemics, IEEE Access, 9 (2020), 44173–44197. https://doi.org/10.1109/ACCESS.2020.3032450 doi: 10.1109/ACCESS.2020.3032450
    [114] T. Zhang, J. Zhao, L. An, D. Liu, Energy efficiency of base station deployment in ultra dense HetNets: A stochastic geometry analysis, IEEE Wireless Commun. Lett., 5 (2016), 184–187. https://doi.org/10.1109/LWC.2016.2516010 doi: 10.1109/LWC.2016.2516010
    [115] A. P. C. Da Silva, M. Meo, M. A. Marsan, Energy-performance trade-off in dense WLANs: A queuing study, Comput. Networks, 56 (2012), 2522–2537. https://doi.org/10.1016/j.comnet.2012.03.017 doi: 10.1016/j.comnet.2012.03.017
    [116] P. K. Sadhu, V. P. Yanambaka, A. Abdelgawad, Physical unclonable function and machine learning based group authentication and data masking for in-hospital segments, Electronics, 11 (2022), 4155. https://doi.org/10.3390/electronics11244155 doi: 10.3390/electronics11244155
    [117] P. K. Sadhu, A. Baul, V. P. Yanambaka, A. Abdelgawad, Machine learning and puf based authentication framework for internet of medical things, in 2022 International Conference on Microelectronics (ICM), (2022), 160–163. https://doi.org/10.1109/ICM56065.2022.10005380
    [118] A. Darwish, A. E. Hassanien, M. Elhoseny, A. K. Sangaiah, K. Muhammad, The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems, J. Ambient Intell. Human. Comput., 10 (2019), 4151–4166. https://doi.org/10.1007/s12652-017-0659-1 doi: 10.1007/s12652-017-0659-1
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1845) PDF downloads(146) Cited by(2)

Article outline

Figures and Tables

Figures(7)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog