2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 241-251, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Adilakshmamma .T 1 * , Meharunnisa S. P. 2 , Anusha Sreeram 3 , Rajat Saini 4 , Maryanka 5 , Shikhar Gupta 6
Doi: https://doi.org/10.54216/JISIoT.140119
The Healthcare Internet of Things (HIoT) is driving a paradigm shift in the healthcare business by providing safe, fast, and networked healthcare solutions. We examined the advantages, disadvantages, and potential future of the Internet of Things (IoT) in the medical industry. Scalability, accuracy, real-time monitoring, data security, and interoperability were among the top priorities. The study employed strict assessment criteria to compare the proposed HIoT technology to existing approaches. This article begins with an overview of the IoT in healthcare. This study compares and contrasts the proposed HIoT strategy with more conventional approaches. We applied both methodologies in this study, each with its own benefits and drawbacks. We evaluated the responses using the F1-score, recall, accuracy, and precision. The inquiry uncovered an interesting story. The proposed HIoT method outperformed traditional techniques in all assessment parameters. In terms of accuracy, the recommended solution outperformed "Block chain Encryption" (8.4) and "Data Validation" (7.9). Additionally, it received an 8.9 for real-time monitoring and an 8.8 for interoperability. Another benefit of the strategy was a reduction in medical errors. The high data accuracy score of 9.1 demonstrates this. The findings illustrate the potential transformation of healthcare delivery through the Internet of Things. According to the study, the proposed strategy might increase healthcare's efficacy, efficiency, and patient-centeredness. The Internet of Things has opened up exciting new opportunities in healthcare. These options may transform medical care and patient outcomes.
AI , Data Security , Healthcare , HIoT , Interoperability , Machine Learning , Patient Care , Real-Time Monitoring , Scalability , Technology
[1] J. M. Kizza, "Internet of things (IoT): growth, challenges, and security," in Guide to Computer Network Security, pp. 517–531, Springer, Berlin, Germany, 2017.
[2] P. Gokhale, O. Bhat, and S. Bhat, "Introduction to IOT," International Advanced Research Journal in Science, Engineering and Technology, vol. 5, no. 1, pp. 41–44, 2018.
[3] M. A. J. Jamali, "IoT Architecture Towards the Internet of Things," Springer, Berlin, Germany, 2020.
[4] J. Wang, W. Chen, L. Wang, Y. Ren, and R. Simon Sherratt, "Blockchain-based data storage mechanism for industrial internet of things," Intelligent Automation & Soft Computing, vol. 26, no. 5, pp. 1157–1172, 2020.
[5] Anjali Raghav , Monika Gupta, Ensemble Learning for Facial Expression Recognition, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 31-41 (Doi : https://doi.org/10.54216/FPA.020104)
[6] S Hariharan , Monika Gupta, Improving Cloud-based ECG Monitoring, Detection and Classification using GAN, Fusion: Practice and Applications, Vol. 2 , No. 2 , (2020) : 42-49 (Doi : https://doi.org/10.54216/FPA.020201)
[7] N. M. Kumar and P. K. Mallick, "Blockchain technology for security issues and challenges in IoT," Procedia Computer Science, vol. 132, pp. 1815–1823, 2018.
[8] M. B. Hoy, "An introduction to the blockchain and its implications for libraries and medicine," Medical Reference Services Quarterly, vol. 36, no. 3, pp. 273–279, 2017.
[9] T. M. Fernández-Caramés and P. Fraga-Lamas, "A review on the use of blockchain for the internet of things," IEEE Access, vol. 6, pp. 32979–33001, 2018.
[10] Mohd Zainal Abidin Ab Kadir, Mhmed Algrnaodi , Ahmed N. Al-Masri, Optimal Algorithm for Shared Network Communication Bandwidth in IoT Applications, International Journal of Wireless and Ad Hoc Communication, Vol. 2 , No. 1 , (2021) : 33-48 (Doi : https://doi.org/10.54216/IJWAC.020103)
[11] Muhammad Edmerdash, Waleed khedr, Ehab Rushdy, An Overview of Cloud-Based Secure Services for Enterprise Drug–Drug Interaction Systems, International Journal of Wireless and Ad Hoc Communication, Vol. 2 , No. 2 , (2021) : 49-58 (Doi : https://doi.org/10.54216/IJWAC.020201)
[12] Z. Deng, Y. Ren, Y. Liu, X. Yin, Z. Shen, and H.-J. Kim, "Blockchain-based trusted electronic records preservation in cloud storage," Computers, Materials & Continua, vol. 58, no. 1, pp. 135–151, 2019.
[13] Nirmal Kumar, K. Premika, L. Bhagyalakshmi and Sanjay Kumar Suman, “Smart Traffic Rescuer using IOT”, Journal of Pharmaceutical Science and Research, special issue 8, pp. 215-219, 2017
[14] Sujeetha Devi, Bhagyalakshmi L and Sanjay Kumar Suman, “Enhancing the Performance of Wireless Sensor Networks through Clustering and Joint Routing with Mobile Sink”, International Journal of Engineering and Advanced Technology, vol. 8, issue 6, pp. 323-327, 2019
[15] V. Mohanakurup et al., "Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network," Computational Intelligence and Neuroscience, vol. 2022, Article ID 8517706, pp. 1–10, 2022. [Online]. Available: https://doi.org/10.1155/2022/8517706.
[16] Hanan Ahmed, Red Palm Weevil Detection Methods: A Survey, Journal of Cybersecurity and Information Management, Vol. 1 , No. 1 , (2020) : 17-20 (Doi : https://doi.org/10.54216/JCIM.010103)
[17] Abdelrahim Koura , Hany S. Elnashar, Data Mining Algorithms for Kidney Disease Stages Prediction, Journal of Cybersecurity and Information Management, Vol. 1 , No. 1 , (2020) : 21-29 (Doi : https://doi.org/10.54216/JCIM.010104).
[18] I.-C. Lin and T.-C. Liao, "A survey of blockchain security issues and challenges," IJ Network Security, vol. 19, no. 5, pp. 653–659, 2017.
[19] V. Roy. "Breast cancer Classification with Multi-Fusion Technique and Correlation Analysis" Fusion: Practice & Applications, Vol. 9, No. 2, 2023 ,PP. 48-61.
[20] R. Kashyap, "Stochastic Dilated Residual Ghost Model for Breast Cancer Detection," J Digit Imaging, vol. 36, pp. 562–573, 2023. [Online]. Available: https://doi.org/10.1007/s10278-022-00739-z
[21] Z. Zheng, "An overview of blockchain technology: architecture, consensus, and future trends," in Proceedings of the 2017 IEEE International Congress on Big Data (BigData Congress), IEEE, Boston, MA, USA, December 2017.
[22] D. Pathak, R. Kashyap, and S. Rahamatkar, "A study of deep learning approach for the classification of Electroencephalogram (EEG) brain signals," in Artificial Intelligence and Machine Learning for EDGE Computing, pp. 133–144, 2022. [Online]. Available: https://doi.org/10.1016/b978-0-12-824054-0.00009-5
[23] D. Pathak and R. Kashyap, "Electroencephalogram-based deep learning framework for the proposed solution of e-learning challenges and limitations," International Journal of Intelligent Information and Database Systems, vol. 15, no. 3, p. 295, 2022. [Online]. Available: https://doi.org/10.1504/ijiids.2022.124081
[24] D. M. Bavkar, R. Kashyap, and V. Khairnar, "Multimodal sarcasm detection via hybrid classifier with optimistic logic," Journal of Telecommunications and Information Technology, vol. 3, pp. 97–114, 2022. [Online]. Available: https://doi.org/10.26636/jtit.2022.161622
[25] E. Ramirez-Asis, R. P. Bolivar, L. A. Gonzales, S. Chaudhury, R. Kashyap, W. F. Alsanie, and G. K. Viju, "A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer," Computational Intelligence and Neuroscience, vol. 2022, Article ID 9325452, pp. 1–10, 2022. [Online]. Available: https://doi.org/10.1155/2022/9325452.
[26] Reem Atassi, Aditi Sharma, Intelligent Traffic Management using IoT and Machine Learning, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 08-19 (Doi : https://doi.org/10.54216/JISIoT.080201)
[27] Khder Alakkari, Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin, Irina Potoroko, Mostafa Abotaleb, El-Sayed M El-kenawy, Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 20-33 (Doi : https://doi.org/10.54216/JISIoT.080202).
[28] M. Amadeo, "Named data networking for IoT: an architectural perspective," in Proceedings of the 2014 European Conference on Networks and Communications (EuCNC), IEEE, Bologna, Italy, June 2014.
[29] V. Roy and S. Shukla, "Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis," Wireless Pers Commun, vol. 97, pp. 6441–6451, 2017. https://doi.org/10.1007/s11277-017-4846-3.
[30] P.K. Shukla, V. Roy, P.K. Shukla, A.K. Chaturvedi, A.K. Saxena, M. Maheshwari, P.R. Pal, "An Advanced EEG Motion Artifacts Eradication Algorithm," The Computer Journal, pp. bxab170, 2021. https://doi.org/10.1093/comjnl/bxab170.
