Skip to main content
SpringerLink
Log in

Markovian with Federated Deep Recurrent Neural Network for Edge—IoMT to Improve Healthcare in Smart Cities

  • Research
  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

The architectural design of smart cities should prioritize the provision of critical medical services. This involves establishing improved connectivity and leveraging supercomputing capabilities to enhance the quality of services (QoS) offered to residents. Edge computing is vital in healthcare applications by enabling low network latencies necessary for real-time data processing. By implementing edge computing, smart cities can benefit from reduced latency, increased bandwidth, and improved power consumption efficiency. In the context of Mobile Edge Computing (MEC), the study proposes a novel approach called the Markovian Decision Process with Federated Deep Recurrent Neural Network (MDP-FDRNN) as the primary algorithm for managing resource allocation. MEC focuses on utilizing edge computing capabilities to process data and perform computations at the network's edges. The conducted tests demonstrate that the MDP-FDRNN algorithm is superior and well-suited for effectively resolving high-processing traffic at the network's edges. It significantly reduces processing time, particularly crucial for healthcare operations related to patients' health problems. By employing the MDP-FDRNN algorithm in resource allocation management, smart cities can efficiently utilize their edge computing infrastructure to handle complex processing tasks. The superior performance of this algorithm in reducing processing time showcases its potential to support critical healthcare operations within smart cities, thereby enhancing the overall quality of healthcare services provided to residents. This article underscores the significance of implementing appropriate technology, including edge computing and the IoM, in developing prosperous smart cities. It also highlights the effectiveness of the MDP-FDRNN algorithm in managing resource allocation and addressing processing challenges at the network's edges, particularly in healthcare operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Alrazgan, M.: Internet of medical things and edge computing for improving healthcare in smart cities. Math. Prob. Eng. 2022, (2022)

  2. Gera, S., Mridul, M., Sharma, S.: IoT based automated health care monitoring system for smart city. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 364–368). IEEE (2021)

  3. Jaiswal, K., Anand, V.: A survey on IoT-based healthcare system: potential applications, issues, and challenges. In Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018, pp. 459–471. Springer Singapore (2021)

  4. Alfakih, T., Hassan, M.M., Gumaei, A., Savaglio, C., Fortino, G.: Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA. IEEE Access 8, 54074–54084 (2020)

    Article  Google Scholar 

  5. Rathi, V.K., Rajput, N.K., Mishra, S., Grover, B.A., Tiwari, P., Jaiswal, A.K., Hossain, M.S.: An edge AI-enabled IoT healthcare monitoring system for smart cities. Comput. Electr. Eng. 96, 107524 (2021)

    Article  Google Scholar 

  6. Song, X., Tong, W., Lei, C., Huang, J., Fan, X., Zhai, G.,..., Zhou, H.: A clinical decision model based on machine learning for ptosis. BMC Ophthalmol. 21(1), 169 (2021)

  7. Zeng, Q., Bie, B., Guo, Q., Yuan, Y., Han, Q., Han, X.,..., Zhou, X.: Hyperpolarized Xe NMR signal advancement by metal-organic framework entrapment in aqueous solution. Proc. Natl. Acad. Sci. 117(30), 17558–17563 (2020)

  8. Pradhan, B., Bhattacharyya, S., Pal, K.: IoT-based applications in healthcare devices. J. Healthc. Eng. 2021, 1–18 (2021)

    Google Scholar 

  9. Liu, Y., Wang, K., Liu, L., Lan, H., Lin, L.: Tcgl: Temporal contrastive graph for self-supervised video representation learning. IEEE Trans. Image Process. 31, 1978–1993 (2022)

    Article  ADS  PubMed  Google Scholar 

  10. Zhou, X., Zhang, L.: SA-FPN: An effective feature pyramid network for crowded human detection. Appl. Intell. 52(11), 12556–12568 (2022)

  11. Cheng, L., Yin, F., Theodoridis, S., Chatzis, S., & Chang, T.: Rethinking bayesian learning for data analysis: the art of prior and inference in sparsity-aware modeling. IEEE Signal Process. Mag. 39(6) (2022)

  12. Umair, M., Cheema, M.A., Cheema, O., Li, H., Lu, H.: Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Sensors 21(11), 3838 (2021)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Muthu, B., Sivaparthipan, C.B., Manogaran, G., Sundarasekar, R., Kadry, S., Shanthini, A., Dasel, A.: IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector. Peer-to-peer Netw. Appl. 13, 2123–2134 (2020)

    Article  Google Scholar 

  14. Shamayleh, A., Awad, M., Farhat, J.: IoT based predictive maintenance management of medical equipment. J. Med. Syst. 44, 1–12 (2020)

    Article  Google Scholar 

  15. Li, Q., Lin, H., Tan, X., & Du, S.: Consensus for multiagent-based supply chain systems under switching topology and uncertain demands. IEEE Trans. Syst. Man Cybern. Syst. 50(12), 4905–4918 (2020)

  16. Alfakih, T., Hassan, M.M., Al-Razgan, M.: Multi-objective accelerated particle swarm optimization with dynamic programing technique for resource allocation in mobile edge computing. IEEE Access 9, 167503–167520 (2021)

    Article  Google Scholar 

  17. Majeed, U., Khan, L.U., Yaqoob, I., Kazmi, S.A., Salah, K., Hong, C.S.: Blockchain for IoT-based smart cities: recent advances, requirements, and future challenges. J. Netw. Comput. Appl. 181, 103007 (2021)

    Article  Google Scholar 

  18. Yang, S., Li, Q., Li, W., Li, X., Liu, A.: Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Trans. Circuits Syst. Video Technol. 32(11), 8037–8050 (2022)

    Article  Google Scholar 

  19. Liu, A., Zhai, Y., Xu, N., Nie, W., Li, W.,..., Zhang, Y.: Region-aware image captioning via interaction learning. IEEE Trans. Circ. Syst. Video Technol. 32(6), 3685–3696 (2022)

  20. Nie, W., Bao, Y., Zhao, Y., Liu, A.: Long dialogue emotion detection based on commonsense knowledge graph guidance. IEEE Trans. Multimed. (2023)

  21. Singh, S.K., Cha, J., Kim, T.W., Park, J.H.: Machine learning based distributed big data analysis framework for next generation web in IoT. Comput. Sci. Inf. Syst. 18(2), 597–618 (2021)

    Article  Google Scholar 

  22. Singh, S.K., Azzaoui, A.E., Kim, T.W., Pan, Y., Park, J.H.: DeepBlockScheme: a deep learning-based blockchain driven scheme for secure smart city. HCIS 11(12), 1–13 (2021)

    Google Scholar 

  23. Ullo, S.L., Sinha, G.R.: Advances in smart environment monitoring systems using IoT and sensors. Sensors 20(11), 3113 (2020)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yan, L., Shi, Y., Wei, M., Wu, Y.: Multi-feature fusing local directional ternary pattern for facial expressions signal recognition based on video communication system. Alex. Eng. J. 63, 307–320 (2023)

    Article  Google Scholar 

  25. Cao, K., Wang, B., Ding, H., Lv, L., Tian, J., Hu, H.,..., Gong, F. Achieving Reliable and Secure Communications in Wireless-Powered NOMA Systems. IEEE Trans. Veh. Technol. 70(2), 1978–1983 (2021)

  26. Wang, S., Hu, X., Sun, J., Liu, J.: Hyperspectral anomaly detection using ensemble and robust collaborative representation. Inf. Sci. 624, 748–760 (2023)

    Article  Google Scholar 

  27. Jiang, H., Dai, X., Xiao, Z., Iyengar, A. K.: Joint task offloading and resource allocation for energy-constrained mobile edge computing. IEEE Trans. Mobile Comput. (2022)

  28. Dai, X., Xiao, Z., Jiang, H., Lui, J. C. S. UAV-Assisted Task Offloading in Vehicular Edge Computing Networks. IEEE Trans. Mobile Comput. (2023)

  29. Wang, Y., Han, X., Jin, S.: MAP based modeling method and performance study of a task offloading scheme with time-correlated traffic and VM repair in MEC systems. Wireless Netw. (2022)

  30. Li, J., Deng, Y., Sun, W., Li, W., Li, R., Li, Q.,..., Liu, Z.: Resource orchestration of cloud-edge–based smart grid fault detection. ACM Trans. Sen. Netw. 18(3) (2022)

  31. Liu, J., Prabuwono, A.S., Abulfaraj, A.W., Miniaoui, S., Taheri, N.: Cognitive cloud framework for waste dumping analysis using deep learning vision computing in healthy environment. Comput. Electr. Eng. 110, 108814 (2023)

    Article  Google Scholar 

  32. Rababah, M., Maydanchi, M., Pouya, S., Basiri, M., Azad, A.N., Haji, F., Aminjarahi, M.: Data Visualization of Traffic Violations in Maryland, US. arXiv preprint arXiv:2208.10543, (2022)

  33. Nasrin, S., Shylendra, A., Darabi, N., Tulabandhula, T., Gomes, W., Chakrabarty, A., Trivedi, A.R.: Enos: Energy-aware network operator search in deep neural networks. IEEE Access 10, 81447–81457 (2022)

    Article  Google Scholar 

  34. Kosarirad, H., Ghasempour Nejati, M., Saffari, A., Khishe, M., Mohammadi, M.: Feature selection and training multilayer perceptron neural networks using grasshopper optimization algorithm for design optimal classifier of big data sonar. J. Sens. (2022)

  35. Liu, H., Barekatain, M., Roy, A., Liu, S., Cao, Y., Tang, Y., Shkel, A., Kim, E.S.: MEMS piezoelectric resonant microphone array for lung sound classification. J. Micromech. Microeng. 33(4), 044003 (2023)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  36. Jiang, H., Xiao, Z., Li, Z., Xu, J., Zeng, F.,..., Wang, D.: Energy-efficient framework for internet of things underlaying heterogeneous small cell networks. IEEE Trans. Mobile Comput. 21(1), 31–43 (2022)

  37. Luan, D., Liu, A., Wang, X., Xie, Y., Wu, Z.,..., Zhang, W.: Robust two-stage location allocation for emergency temporary blood supply in postdisaster. Discrete Dyn. Nat. Soci. (2022)

  38. Shan, Y., Wang, H., Yang, Y., Wang, J., Zhao, W., Huang, Y.,..., Zhao, G.: Evidence of a large current of transcranial alternating current stimulation directly to deep brain regions. Mol. Psychatry (2023)

  39. Cheng, B., Wang, M., Zhao, S., Zhai, Z., Zhu, D.,..., Chen, J.: Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Trans. Network. 25(4), 2082–2095 (2017)

  40. Zhuang, Y., Jiang, N., Xu, Y., Xiangjie, K., Kong, X.: Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks. Wirel. Commun. Mobile Comput. (2022)

Download references

Funding

No funding was obtained for this study.

Author information

Authors and Affiliations

  1. School of International, Jilin University of Finance and Economics, Changchun, 130117, Jilin, China

    Yuliang Gai

  2. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Yuxin Liu

  3. The Third Bethune School of Clinical Medicine, Jilin University, Changchun, 130021, Jilin, China

    Minghao Li

  4. School of International Economics and Trade, Jilin University of Finance and Economics, Changchun, 130117, Jilin, China

    Shengcheng Yang

Authors
  1. Yuliang Gai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minghao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shengcheng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yuliang Gai: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Yuxin Liu: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Minghao Li: Writing—original draft, Writing—review & editing.

Shengcheng Yang: Data Curation, Validation, Resources, Writing—review & editing.

Corresponding author

Correspondence to Yuliang Gai.

Ethics declarations

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gai, Y., Liu, Y., Li, M. et al. Markovian with Federated Deep Recurrent Neural Network for Edge—IoMT to Improve Healthcare in Smart Cities. J Grid Computing 22, 1 (2024). https://doi.org/10.1007/s10723-023-09709-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-023-09709-3

Keywords

Search

Navigation