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Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC

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Abstract

Healthcare (HC) is among the most promising sectors for assessing Internet of things (IoT) based technological advances, as patients can use wearable or injected medical sensors to monitor medical parameters any place and at any time. The data gathered by IoT devices can be sent to medical professionals, and doctors have real-time access to their patient's data. Nevertheless, the IoT devices within the network have limited resources and minimal computing capability, causing energy conservation issues. Even though clustering saves energy in network nodes, existing clustering methods could be more effective because of the higher Energy Consumption (EC), poorly balanced network load, and enhanced end-to-end delays. Furthermore, the integrity and security of medical information have become significant issues for HC applications. This paper proposes a secure and energy-efficient data transmission (DT) framework for IoT-based HC (IoT-HC) systems using enhanced mayfly clustering-based Q learner routing (EMCQLR) and exponential key-based elliptical curve cryptography (EKECC) techniques. The proposed work consists of the following steps. Double hash biometric-based authentication (DHABA) is initially used for IoT user authorization that prevents the HC network from unauthorized data access. The cluster head (CH) is then selected using the enhanced mayfly optimization algorithm (EMOA) to build clusters of IoT medical sensors and collect data from the nodes. Data is routed to the sink node by checking for data duplication. The path-weighted Q reinforcement learning (PWQRL) model can perform data routing. Finally, the EKECC algorithm encrypts the data packets received from the body sensors, providing security to the patient’s data when DT is performed over an unsecured wireless channel. The performance of the suggested system is evaluated using the energy, loss ratio, throughput, success rate, and delay. The simulation outcomes show that the proposed method outperforms existing methods. Furthermore, false data elimination check and reliability metrics show that our proposed scheme ensures information privacy, message authenticity, and integrity while requiring minimal communication overhead and computation.

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References

  1. Gulati, K., Boddu, R.S.K., Kapila, D., Bangare, S.L., Chandnani, N., Saravanan, G.: A review paper on wireless sensor network techniques in Internet of Things (IoT). Mater. Today: Proc. 51, 161–165 (2022)

    Google Scholar 

  2. Kadhim, K.T., Alsahlany, A.M., Wadi, S.M., Kadhum, H.T.: An overview of patient’s health status monitoring system based on Internet of Things (IoT). Wirel. Pers. Commun. 114(3), 2235–2262 (2020)

    Article  Google Scholar 

  3. Abirami, S., Chitra, P.: Energy-efficient edge based real-time healthcare support system. In: Advances in computers, vol. 117, pp. 339–368. Elsevier, Amsterdam (2020)

    Google Scholar 

  4. Birje, M.N., Hanji, S.S.: Internet of things based distributed healthcare systems: a review. J. Data Inf. Manage. 2(3), 149–165 (2020)

    Article  Google Scholar 

  5. Fouad, H., Hassanein, A.S., Soliman, A.M., Al-Feel, H.: Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction. Measurement 159, 107757 (2020)

    Article  Google Scholar 

  6. Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R., Vijayakumar, V.: A study on medical Internet of Things and big data in personalized healthcare system. Health Inf. Sci. Syst. 6(1), 1–20 (2018)

    Article  Google Scholar 

  7. Sodhro, A.H., Al-Rakhami, M.S., Wang, L., Magsi, H., Zahid, N., Pirbhulal, S., et al.: Decentralized energy efficient model for data transmission in IoT-based healthcare system. In: 2021 IEEE 93rd vehicular technology conference (VTC2021-Spring), pp. 1–5. IEEE (2021)

    Google Scholar 

  8. Kalkan, K.: SUTSEC: SDN utilized trust based secure clustering in IoT. Comput. Netw. 178, 107328 (2020)

    Article  Google Scholar 

  9. Shahraki, A., Taherkordi, A., Haugen, Ø., Eliassen, F.: A survey and future directions on clustering: from WSNs to IoT and modern networking paradigms. IEEE Trans. Netw. Serv. Manage. 18(2), 2242–2274 (2020)

    Article  Google Scholar 

  10. Fathy, Y., Barnaghi, P.: Quality-based and energy-efficient data communication for the internet of things networks. IEEE Internet Things J. 6(6), 10318–10331 (2019)

    Article  Google Scholar 

  11. Singh, S., Nandan, A.S., Sikka, G., Malik, A., Vidyarthi, A.: A secure energy-efficient routing protocol for disease data transmission using IoMT. Comput. Electr. Eng. 101, 108113 (2022)

    Article  Google Scholar 

  12. Akbari, Y., Tabatabaei, S.: A new method to find a high reliable route in IoT by using reinforcement learning and fuzzy logic. Wirel. Pers. Commun. 112(2), 967–983 (2020)

    Article  Google Scholar 

  13. Sarma, H.K.D., Bhuyan, B., Borah, S., Dutta, N.: Trends in communication, cloud, and big data. Springer, Singapore (2020)

    Book  Google Scholar 

  14. Haseeb, K., Islam, N., Almogren, A., Din, I.U.: Intrusion prevention framework for secure routing in WSN-based mobile Internet of Things. IEEE Access 7, 185496–185505 (2019)

    Article  Google Scholar 

  15. Xiu-Wu, Y.U., Hao, Y.U., Yong, L., Ren-rong, X.: A clustering routing algorithm based on wolf pack algorithm for heterogeneous wireless sensor networks. Comput. Netw. 167, 106994 (2020)

    Article  Google Scholar 

  16. Selvaraj, S., Sundaravaradhan, S.: Challenges and opportunities in IoT healthcare systems: a systematic review. SN Appl. Sci. 2(1), 1–8 (2020)

    Article  Google Scholar 

  17. Shi, Q., Qin, L., Ding, Y., Xie, B., Zheng, J., Song, L.: Information-aware secure routing in wireless sensor networks. Sensors 20(1), 165 (2019)

    Article  Google Scholar 

  18. Gopika, D., Panjanathan, R.: WITHDRAWN: Energy efficient routing protocols for WSN based IoT applications: A review (2020)

  19. Magsi, H., Sodhro, A.H., Al-Rakhami, M.S., Zahid, N., Pirbhulal, S., Wang, L.: A novel adaptive battery-aware algorithm for data transmission in IoT-based healthcare applications. Electronics 10(4), 367 (2021)

    Article  Google Scholar 

  20. Majumdar, A., Laskar, N.M., Biswas, A., Sood, S.K., Baishnab, K.L.: Energy efficient e-healthcare framework using HWPSO-based clustering approach. J. Intell. Fuzzy Syst. 36(5), 3957–3969 (2019)

    Article  Google Scholar 

  21. Ahad, A., Tahir, M., Sheikh, M.A., Ahmed, K.I., Mughees, A.: An intelligent clustering-based routing protocol (CRP-GR) for 5G-based smart healthcare using game theory and reinforcement learning. Appl. Sci. 11(21), 9993 (2021)

    Article  Google Scholar 

  22. Almalki, F.A., Soufiene, B.O.: EPPDA: an efficient and privacy-preserving data aggregation scheme with authentication and authorization for IoT-based healthcare applications. Wirel. Commun. Mob. Comput. (2021). https://doi.org/10.1155/2021/5594159

    Article  Google Scholar 

  23. Al-Turjman, F., Deebak, B.D.: Privacy-aware energy-efficient framework using the internet of medical things for COVID-19. IEEE Internet of Things Mag. 3(3), 64–68 (2020)

    Article  Google Scholar 

  24. Saba, T., Haseeb, K., Ahmed, I., Rehman, A.: Secure and energy-efficient framework using Internet of Medical Things for e-healthcare. J. Infect. Public Health 13(10), 1567–1575 (2020)

    Article  Google Scholar 

  25. Zhang, J., Liu, H., Ni, L.: A secure energy-saving communication and encrypted storage model based on RC4 for EHR. IEEE Access 8, 38995–39012 (2020)

    Article  Google Scholar 

  26. Xu, G.: IoT-assisted ECG monitoring framework with secure data transmission for health care applications. IEEE Access 8, 74586–74594 (2020)

    Article  Google Scholar 

  27. Prasanalakshmi, B., Murugan, K., Srinivasan, K., Shridevi, S., Shamsudheen, S., Hu, Y.C.: Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography. J. Supercomput. 78(1), 361–378 (2022)

    Article  Google Scholar 

  28. Kumar, P.M., Gandhi, U.D.: Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application. J. Supercomput. 76(6), 3963–3983 (2020)

    Article  Google Scholar 

  29. Refaee, E., Parveen, S., Begum, K.M.J., Parveen, F., Raja, M.C., Gupta, S.K., Krishnan, S.: Secure and scalable healthcare data transmission in IoT based on optimized routing protocols for mobile computing applications. Wirel. Commun. Mob. Comput. (2022). https://doi.org/10.1155/2022/5665408

    Article  Google Scholar 

  30. Elhoseny, M., Ramírez-González, G., Abu-Elnasr, O.M., Shawkat, S.A., Arunkumar, N., Farouk, A.: Secure medical data transmission model for IoT-based healthcare systems. IEEE Access 6, 20596–20608 (2018)

    Article  Google Scholar 

  31. Hasan, M., Islam, M.M., Zarif, M.I.I., Hashem, M.M.A.: Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things 7, 100059 (2019)

    Article  Google Scholar 

  32. Luo, C., Tan, Z., Min, G., Gan, J., Shi, W., Tian, Z.: A novel web attack detection system for internet of things via ensemble classification. IEEE Trans. Ind. Inform. 17(8), 5810–5818 (2020)

    Article  Google Scholar 

  33. Xiao, L., Wan, X., Lu, X., Zhang, Y., Wu, D.: IoT security techniques based on machine learning: how do IoT devices use AI to enhance security? IEEE Signal Process. Mag. 35(5), 41–49 (2018)

    Article  Google Scholar 

  34. Tian, Z., Luo, C., Qiu, J., Du, X., Guizani, M.: A distributed deep learning system for web attack detection on edge devices. IEEE Trans. Ind. Inform. 16(3), 1963–1971 (2019)

    Article  Google Scholar 

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DB wrote the paper. TDR, SD and BSM collected the data and performed the analysis. All authors reviewed the manuscript.

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Correspondence to D. Balakrishnan.

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Balakrishnan, D., Rajkumar, T.D., Dhanasekaran, S. et al. Secure and energy-efficient data transmission framework for IoT-based healthcare applications using EMCQLR and EKECC. Cluster Comput 27, 2999–3016 (2024). https://doi.org/10.1007/s10586-023-04130-7

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