Volume 12 , Issue 2 , PP: 52-68, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Sudeshna chakraborty 1 * , Akanksha Singh 2
Doi: https://doi.org/10.54216/JCIM.120204
The Internet of Medical Things (IoMT) is a revolutionary technique for integrating the IT infrastructure of healthcare organisations with medical apps and equipment. Rapid advancements in this approach in recent years have resulted in game-changing improvements in the healthcare system, illness management, and patient care standards. Both achievements have been made possible by the Internet of Medical Things. People can use the IoMT to access a variety of cloud-based services, including file sharing, patient monitoring, data collection, information gathering, and hospital cleaning. Wireless sensor networks (WSNs), which collect and transmit data, are critical to system operation. In the healthcare system, patients’ privacy and security must be preserved at all costs. Wireless data transmission from these cutting-edge devices may have been intercepted and manipulated without consent. The hybrid and improved (Elliptic Curve Cryptography ECC) Energy-Efficient Routing Protocol (EERP) method, which is based on the elliptic curve encryption protocol, may provide enough protection for sensitive information. ECC-EERP uses pairs of public and private keys known only to each other to decode and encrypt data delivered across a network. As a result, the energy needed to sustain WSNs has dropped. To assess the efficacy of the recommended plan, we did an extensive study and compared our findings to the many other viable courses of action. We did the analysis while taking a variety of aspects into account. The study's findings and conclusion all point to the strategy's ability to significantly increase energy efficiency and security. ECC-EERP is a novel encryption method that increases data security while consuming less energy. Because of its efficacy in improving the whole healthcare system, this strategy has a lot of potential for the future of patient care, illness management, and healthcare delivery in general.
Data security , Electronic health records , Energy-Efficient Routing Protocol , Elliptic Curve Cryptography , Internet of Medical Things , Healthcare.
[1] Ali, A., Ming, Y., Chakraborty, S., & Iram, S. (2017). A comprehensive survey on real-time applications of WSN. Future Internet, 9(4), 77.
[2] Banđur, Đ., Jakšić, B., Banđur, M., & Jović, S. (2019). An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture. Computers and Electronics in Agriculture, 156, 500-507.
[3] Hezaveh, M., Shirmohammdi, Z., Rohbani, N., & Miremadi, S. G. (2015). A fault-tolerant and energy-aware mechanism for cluster-based routing algorithm of WSNs. In Integrated network management (IM), 2015 IFIP/IEEE international symposium (pp. 1-6).
[4] Natarajan, R., Lokesh, G.H., Flammini, F., Premkumar, A., Venkatesan, V.K., & Gupta, S.K. (2023). A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0. Infrastructures, 8, 22.
[5] Kashyap, R., Nair, R., Gangadharan, S. M., Botto-Tobar, M., Farooq, S., & Rizwan, A. (2022). Glaucoma detection and classification using improved U-Net Deep Learning Model. Healthcare, 10(12), 2497.
[6] Nair, R., Alhudhaif, A., Koundal, D., Doewes, R. I., & Sharma, P. (2021). Deep learning-based COVID-19 detection system using pulmonary CT scans. Turkish Journal of Electrical Engineering & Computer Sciences, 29(SI-1), 2716-2727.
[7] Uddin, M. I., Shah, S. A. A., & Al-Khasawneh, M. A. (2020). A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19. Journal of Sensors, 2020, Article ID 8856801, 1-15. doi: 10.1155/2020/8856801.
[8] Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189-199.
[9] Ahmed, G., Zou, J., Zhao, X., & Fareed, M. M. S. (2017). Markov chain model-based optimal cluster heads selection for wireless sensor networks. Sensors, 17(3), 440.
[10] Thakkar, A., & Kotecha, K. (2014). Cluster head election for energy and delay constraint applications of wireless sensor network. IEEE Sensors Journal, 14(8), 2658-2664.
[11] Wang, A., Yang, D., & Sun, D. (2012). A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks. Computers & Electrical Engineering, 38(3), 662-671.
[12] Mohanakurup, V., Parambil Gangadharan, S. M., Goel, P., Verma, D., Alshehri, S., Kashyap, R., & Malakhil, B. (2022). Breast cancer detection on histopathological images using a composite dilated Backbone Network. Computational Intelligence and Neuroscience, 2022, 1-10.
[13] Kashyap, R. (2021). Breast cancer histopathological image classification using stochastic dilated residual ghost model. International Journal of Information Retrieval Research, 12(1), 1-24.
[14] Dishongh, T. J., McGrath, M., & Kuris, B. (2014). Wireless sensor networks for healthcare applications. Artech House.
[15] Al-Khasawneh, M. A., Shamsuddin, S. M., Hasan, S., & Bakar, A. A. (2018). An Improved Chaotic Image Encryption Algorithm. In Proceedings of the 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) (pp. 1-8). Shah Alam, Malaysia. doi: 10.1109/ICSCEE.2018.8538373.
[16] Suciu, G., Suciu, V., Martian, A., Craciunescu, R., Vulpe, A., Marcu, I., et al. (2015). Big data, internet of things and cloud convergence – an architecture for secure E-Health applications. Journal of Medical Systems, 39(11), 141.
[17] Van Dam, K., Pitchers, S., & Barnard, M. (2001). Body area networks: towards a wearable future. Proceedings of WWRF Kick off Meeting, Munich, Germany.
[18] Sun, Y., Lo, F. P. W., & Lo, B. (2019). Security and privacy for the internet of medical things enabled healthcare systems: a survey. IEEE Access, 7. doi:10.1109/access.2019.2960617.183339
[19] Verma, G., & Prakash, S. (2021). Internet of Things for healthcare: research challenges and future prospects. In Advances in Communication and Computational Technology. Singapore: Springer.
[20] Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. A. (2015). Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376. doi:10.1109/comst.2015.2444095.
[21] Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. (2017). Internet of medical things (IoMT): applications, benefits and future challenges in healthcare domain. Journal of Communication, 12(4), 240-247. doi:10.12720/jcm.12.4.240-247.
[22] Quwaider, M., & Biswas, S. (2009). On-body packet routing algorithms for body sensor networks. In Proceedings of the 2009 First International Conference on Networks & Communications (pp. 171-177). Chennai, India: IEEE.
[23] Wei, W., & Qi, Y. (2011). Information potential fields navigation in wireless Ad-Hoc sensor networks. Sensors, 11(5), 4794-4807. doi:10.3390/s110504794.
[24] Rehman, A., Saba, T., Haseeb, K., Larabi Marie-Sainte, S., & Lloret, J. (2021). Energy-efficient IoT e-health using artificial intelligence model with homomorphic secret sharing. Energies, 14(19), 6414. doi:10.3390/en14196414.
[25] Rghioui, A., Lloret, J., Harane, M., & Oumnad, A. (2020). A smart glucose monitoring system for diabetic patient. Electronics, 9(4), 678. doi:10.3390/electronics9040678.
[26] Kashyap, R. (2020). Machine learning for internet of things. In Research Anthology on Artificial Intelligence Applications in Security (pp. 976-1002).
[27] Khan, Z. A., Feng, Z., Uddin, M. I., Mast, N., Shah, S. A. A., Imtiaz, M., Al-Khasawneh, M. A., & Mahmoud, M. (2020). Optimal Policy Learning for Disease Prevention Using Reinforcement Learning. Scientific Programming, 2020, Article ID 7627290, 1-13. doi: 10.1155/2020/7627290.
[28] Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system. EAI Endorsed Transactions on Security and Safety, 3(9), e2. doi:10.4108/eai.3-12-2015.2262516.
[29] Mohamed Shakeel, P., Baskar, S., Sarma Dhulipala, V. R., Mishra, S., & Jaber, M. M. (2018). Retracted article: maintaining security and privacy in health care system using learning based deep-Q-networks. Journal of Medical Systems, 42(10), 186. doi:10.1007/s10916-018-1045-z.
[30] Ramirez-Asis, E., Bolivar, R. P., Gonzales, L. A., Chaudhury, S., Kashyap, R., Alsanie, W. F., & Viju, G. K. (2022). A lightweight hybrid dilated ghost model-based approach for the prognosis of breast cancer. Computational Intelligence and Neuroscience, 2022, 1-10.
[31] Al-Khasawneh, M. A., Uddin, I., Shah, S. A. A., et al. (2022). An Improved Chaotic Image Encryption Algorithm using Hadoop-based MapReduce framework for massive remote sensed images in parallel IoT applications. Cluster Computing, 25(2), 999-1013. doi: 10.1007/s10586-021-03466-2.
