An Energy-Efficient Based Secure IOT Data Transfer with Hashed Data Access Policy Using ACROT-DHSKECC and LSCRC32
Main Article Content
Keywords
Grasshopper Optimization Algorithm (GOA), Secure Data Transmission, IoT Healthcare, Energy-Efficient Communication, 5G Healthcare
Abstract
The rapid expansion of Internet of Things (IoT) applications in healthcare has amplified the need for secure, efficient, and scalable data transmission frameworks. This study proposes an energy-aware and secure IoT-based data transmission architecture tailored for remote healthcare monitoring systems. Unlike conventional encryption-centric models, the proposed framework integrates lightweight double encryption (ACROT-DHSKECC) and hashed access control (LSCRC32) with optimised network path selection using clustering and Grasshopper Optimisation Algorithm (GOA). This integration reduces latency, enhances node-level energy efficiency, and secures data transmission over distributed networks. The system’s design aligns with next-generation telecom infrastructure, making it well-suited for deployment in 5G-enabled smart healthcare environments. Simulation results demonstrate reduced hash generation time, lower memory usage, and enhanced encryption performance, affirming its viability for secure data delivery in modern digital health ecosystems.
References
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Deepalakshmi, P. C. P. (2021). HCAC EHR: Hybrid cryptographic access control for secure EHR retrieval in healthcare cloud. Journal of Ambient Intelligence and Humanized Computing, 13(2), 1–19. https://doi.org/10.1007/s12652-021-02942-2
Diffie, W., & Hellman, M. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654. https://doi.org/10.1109/TIT.1976.1055638
Dwivedi, R., Mehrotra, D., & Chandra, S. (2022). Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. Journal of Oral Biology and Craniofacial Research, 12(2), 302–318. https://doi.org/10.1016/j.jobcr.2021.11.010
Ghazal, T. M., Hasan, M. K., Alshurideh, M. T., Alzoubi, H. M., Ahmad, M., Akbar, S. S., Kurdi, B. A., & Akour, I. A. (2021). IoT for smart cities: Machine learning approaches in smart healthcare – A review. Future Internet, 13(8), 1–19. https://doi.org/10.3390/fi13080191
Ghazal, T. M., Saeed, R. A., Hasan, M. K., Pandey, B., Gohel, H., & Eshmawi, A. A. (2022). A review on security threats, vulnerabilities, and countermeasures of 5G-enabled Internet-of-Medical-Things. IET Communications, 16(5), 421–432. https://doi.org/10.1049/cmu2.12301
Haghi, M., Madanipour, M., Nikravan, M., Asghari, P., & Mahdipour, E. (2021). A systematic review of IoT in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications, 192, 103164. https://doi.org/10.1016/j.jnca.2021.103164
Heart Disease Dataset. (n.d.). UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Heart+Disease
Humayun, M., & Jhanjhi, N. Z. (2021). Secure healthcare data aggregation and transmission in IoT: A survey. IEEE Access, 9, 16849–16865. https://doi.org/10.1109/ACCESS.2021.3052850
Javaid, M., & Haleem, I. (2021). Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 pandemic. Journal of Oral Biology and Craniofacial Research, 11(2), 209–214. https://doi.org/10.1016/j.jobcr.2021.01.015
Kadiyala, B., & Kaur, H. (2021). Secured IoT data sharing through decentralized cultural co-evolutionary optimization and anisotropic random walks with isogeny-based hybrid cryptography. Journal of Science and Technology, 6(6), 231–245. https://doi.org/10.46243/jst.2021.v06.i06.pp231-245
Karunarathne, S. M., Saxena, N., Khan, M. K., & others. (2021). Security and privacy in IoT smart healthcare. IEEE Internet Computing, 25(4), 1–9. https://doi.org/10.1109/MIC.2021.3051675
Kathamuthu, N. D., Chinnamuthu, A., Iruthayanathan, N., Ramachandran, M., & Gandomi, A. H. (2022). Deep Q-learning-based neural network with privacy preservation method for secure data transmission in IoT healthcare application. Electronics, 11, 1–14. https://doi.org/10.3390/electronics11050745
Khadidos, A. O., Shitharth, S., Khadidos, A. O., Sangeetha, K., & Alyoubi, K. H. (2022). Healthcare data security using IoT sensors based on random hashing mechanism. Hindawi, 2022, 1–17. https://doi.org/10.1155/2022/8457116
Koblitz, N. (1987). Elliptic curve cryptosystems. Mathematics of Computation, 48(177), 203–209. https://doi.org/10.2307/2007884
Kondaka, L. S., Thenmozhi, M., Vijayakumar, K., & Kohli, R. (2021). An intensive healthcare monitoring paradigm by using IoT-based machine learning strategies. Multimedia Tools and Applications, 81(26), 1–15. https://doi.org/10.1007/s11042-021-11439-7
Kumar, P., Kumar, R., Gupta, G. P., Tripathi, R., Jolfaei, A., & Islam, A. K. M. N. (2023). A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system. Journal of Parallel and Distributed Computing, 172, 69–83. https://doi.org/10.1016/j.jpdc.2022.10.002
Li, H., Yu, K., Liu, B., Feng, C., Qin, Z., Srivastava, G., & Member, S. (2021). An efficient ciphertext-policy weighted attribute-based encryption for the Internet of Things. IEEE Journal of Biomedical and Health Informatics, 26(5), 1–12. https://doi.org/10.1109/JBHI.2021.3075995
Masud, M., Gaba, G. S., & Choudhary, K. (2021). Lightweight and anonymity-preserving user authentication scheme for IoT-based healthcare. IEEE Internet of Things Journal, 9(4), 1–8. https://doi.org/10.1109/JIOT.2021.3080461
Miller, V. S. (1985). Use of elliptic curves in cryptography. In A. M. Odlyzko (Ed.), Advances in Cryptology – CRYPTO ’85 Proceedings (pp. 417–426). Springer. https://doi.org/10.1007/3-540-39799-X_31
Natarajan, D. R., Peddi, S., Valivarthi, D. T., Narla, S., Kethu, S. S., & Kurniadi, D. (2024). Secure user authentication and data sharing for mobile cloud computing using BLAKE2 and Diffie-Hellman key exchange. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1–8). IEEE. https://doi.org/10.1109/ICERCS63125.2024.10895363
Natarajan, R., Lokesh, G. H., Flammini, F., & Premkumar, A. (2023). A novel framework on security and energy enhancement based on Internet of Medical Things for healthcare 5.0. Infrastructures, 8, 1–18. https://doi.org/10.3390/infrastructures8010018
Obesity Levels Dataset. (n.d.). Kaggle. https://www.kaggle.com/datasets/mansoordaku/obesity-levels
Oliveira, M. T. de, Verginadis, Y., Reis, L. H. A., Psarra, E., Patiniotakis, I., & Olabarriaga, S. D. (2023). AC-ABAC: Attribute-based access control for electronic medical records during acute care. Expert Systems With Applications, 213, 119271. https://doi.org/10.1016/j.eswa.2022.119271
PCOS Dataset. (n.d.). Kaggle. https://www.kaggle.com/datasets/jainilcoder/pcos-dataset
Peterson, W. W., & Brown, D. T. (1961). Cyclic codes for error detection. Proceedings of the IRE, 49(1), 228–235. https://doi.org/10.1109/JRPROC.1961.287814
Pima Indians Diabetes Database. (n.d.). UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/diabetes
Pradhan, B., Bhattacharyya, S., & Pal, K. (2021). IoT-based applications in healthcare devices. Hindawi, 2021, 1–18. https://doi.org/10.1155/2021/6632599
Ratta, P., Kaur, A., Sharma, S., Shabaz, M., & Dhiman, G. (2021). Application of blockchain and Internet of Things in healthcare and medical sector: Applications, challenges, and future perspectives. Hindawi, 2021, 1–20. https://doi.org/10.1155/2021/7608296
Refaee, E., Parveen, S., Mohamed, K., Begum, J., Parveen, F., Raja, M. C., Gupta, S. K., & Krishnan, S. (2022). Secure and scalable healthcare data transmission in IoT based on optimized routing protocols for mobile computing applications. Hindawi, 2022, 1–12. https://doi.org/10.1155/2022/5665408
Rejeb, A., Rejeb, K., Treiblmaier, H., Appolloni, A., Alghamdi, S., Alhasawi, Y., & Iranmanesh, M. (2023). The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet of Things, 22, 100721. https://doi.org/10.1016/j.iot.2023.100721
Srinivasan, K., Chauhan, G. S., Jadon, R., Budda, R., Gollapalli, V. S. T., & Prema, R. (2025). Secure and privacy-preserving cloud computing through MLP-LSTM based enhanced homomorphic encryption technique. International Journal of Humanities Social Science and Management (IJHSSM), 5(2), 285–290. https://www.ijhssm.org
Sun, Y., Liu, J., & Yu, K. (2021). PMRSS: Privacy-preserving medical record searching scheme for intelligent diagnosis in IoT. IEEE Transactions on Industrial Informatics, 18(3), 1–10. https://doi.org/10.1109/TII.2021.3070544
Ullah, F., Khan, M. Z., Faisal, M., Rehman, U., Abbas, S., Mubarek, F. S. (2021). An Energy Efficient and Reliable Routing Scheme to enhance the stability period in Wireless Body Area Networks. Computer Communications, 165, 20–32. https://doi.org/10.1016/j.comcom.2020.10.017
Valivarthi, D. T., & Kurniadi, D. (2024). A hybrid consensus method for energy-efficient and secure IoT data sharing in fog computing, integrating delegated proof of stake and whale optimization techniques. Journal of IoT in Social, Mobile, Analytics, and Cloud, 6(4), 308–326. https://doi.org/10.36548/jismac.2024.4.002
Valivarthi, D. T., Peddi, S., Narla, S., Kethu, S. S., & Natarajan, D. R. (2023). Fog computing-based optimized and secured IoT data sharing using CMA-ES and firefly algorithm with DAG protocols and federated Byzantine agreement. International Journal of Engineering & Science Research, 13(1), 117–132. https://www.ijesr.org
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2025-10-15
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Anjikumar, T., & Chakravarthy, A. (2025). An Energy-Efficient Based Secure IOT Data Transfer with Hashed Data Access Policy Using ACROT-DHSKECC and LSCRC32. Journal of Telecommunications and the Digital Economy, 13(3), 145-175. https://doi.org/10.18080/jtde.v13n3.1301
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How to Cite
Anjikumar, T., & Chakravarthy, A. (2025). An Energy-Efficient Based Secure IOT Data Transfer with Hashed Data Access Policy Using ACROT-DHSKECC and LSCRC32. Journal of Telecommunications and the Digital Economy, 13(3), 145-175. https://doi.org/10.18080/jtde.v13n3.1301
