Sustainable Healthcare Through Iot And Pervasive Computing: A Reinforcement Learning Approach
DOI:
https://doi.org/10.63682/jns.v14i10S.2852Keywords:
Sustainable Healthcare, IoT in Healthcare, Pervasive Computing, Reinforcement Learning, Smart Healthcare SystemsAbstract
Sustainable healthcare has become a critical global concern, necessitating intelligent and adaptive solutions to improve patient care, optimize resources, and enhance real-time decision-making. The integration of the Internet of Things and pervasive computing offers a transformative approach by enabling seamless connectivity, continuous monitoring, and data-driven insights. This paper explores a reinforcement learning-based framework that leverages IoT-enabled healthcare devices and pervasive computing environments to optimize healthcare processes dynamically. The proposed model enhances personalized patient care, predictive diagnostics, and resource allocation by utilizing real-time data analytics and adaptive learning mechanisms. This paper proposes a Dispersed and Elastic Computing Model to facilitate robust and adaptive communication for users of IoT-based wearable healthcare devices. The model incorporates Recurrent Reinforcement Learning to dynamically analyze and optimize resource allocation based on user demands and system constraints.Additionally, the study highlights the role of reinforcement learning in automating decision-making, reducing latency, and improving operational efficiency in healthcare systems. Through simulations and case studies, the framework demonstrates significant improvements in patient outcomes, energy efficiency, and healthcare sustainability. The findings emphasize the potential of AI-driven IoT healthcare systems in ensuring proactive, cost-effective, and scalable solutions for future digital healthcare ecosystems.Future research can focus on scalability improvements, adaptive energy-efficient resource management, and interoperability between diverse healthcare ecosystems.
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M. M. Baig, S. Afifi, H. GholamHosseini, and F. Mirza, "A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults—A Focus on Ageing Population and Independent Living," Journal of Medical Systems, vol. 43, no. 8, p. 233, 2019.
Z. Yang, Q. Zhou, L. Lei, and W. Xiang, "An IoT-Cloud Based Wearable ECG Monitoring System for Smart Healthcare," Journal of Medical Systems, vol. 40, no. 12, p. 286, 2016.
A. H. T. Eranga De Silva, W. H. Peshan Sampath, N. H. Lakshitha Sameera, Y. W. Ranjith Amarasinghe, and A. Mitani, "Development of a Wearable Tele-Monitoring System with IoT for Bio-Medical Applications," in 2016 IEEE 5th Global Conference on Consumer Electronics (GCCE), Kyoto, Japan, 2016, pp. 1-2.
A. Sabban, "Compact Wearable Metamaterials Antennas for Energy Harvesting Systems, Medical and IoT Systems," Electronics, vol. 8, no. 11, p. 1340, 2019.
S. H. Abbas, "Revolutionizing UAV: Experimental Evaluation of IoT Enabled Unmanned Aerial Vehicle Based Agricultural Field Monitoring Using Remote Sensing Strategy," Remote Sensing in Earth Systems Sciences, Springer, 2024.
S. H. Abbas, "Exploring Subcellular Location Anomalies: A Novel Quantum Bioimaging Paradigm," Optical and Quantum Electronics, Springer, 2024.
S. H. Abbas, "A Fuzzy-Based Proposed Forecast Model for Brain Disease," International Neurourology Journal, SCOPUS Q2, 2023.
S. H. Abbas, "Deep Learning Framework for Analysis of Health Factors in Internet-of-Medical Things," Radioelectronics and Communications Systems, Springer, 2023.
S. H. Abbas, "Artificial Intelligence and Deep Learning Based Agri & Food Quality and Safety Detection System," International Journal of Intelligent Systems and Applications in Engineering, Elsevier, 2024.
S. H. Abbas, "IoTWP: Design and Development of Internet of Things Assisted Weather Prediction Scheme with Advance Remote Tracking Norms," 1st IEEE International Conference on Multidisciplinary Research in Technology and Management (MRTM 23), 2023.
R. W. Heath, N. Gonzalez-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed, "An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems," IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 436-453, 2016.
M. A. Ferrer, M. Diaz, and M. Zapata, "Challenges in IoT-enabled Healthcare Systems for Chronic Disease Management," IEEE Transactions on Consumer Electronics, vol. 63, no. 4, pp. 441-449, 2017.
M. Stankovic, "Research Directions in IoT Security and Privacy: Challenges, Attacks, and Solutions," Future Generation Computer Systems, vol. 92, pp. 947-964, 2019.
F. A. Silva, R. Queiroz, and J. P. Gomes, "Fog Computing for the Internet of Things: A Comprehensive Survey," IEEE Access, vol. 7, pp. 119779-119809, 2019.
M. H. Miraz and M. Ali, "Internet of Nano-Things, Things, and Everything: Future Visions, Applications, and Research Challenges," IEEE Access, vol. 5, pp. 9423-9436, 2017.
M. Al-Turjman, "Intelligence and Security in Big 5G-Oriented IoT," IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3452-3465, 2021.
B. Boero, S. Marchese, D. Muratore, and R. Zunino, "Smart IoT for Health: A Wearable Sensor Node for Real-Time Monitoring and Feedback," IEEE Internet of Things Journal, vol. 8, no. 1, pp. 485-493, 2021.
Y. Shi, X. Chen, and X. Zhang, "Energy-Efficient Data Transmission in IoT-Based Wearable Healthcare Monitoring Systems," IEEE Sensors Journal, vol. 18, no. 16, pp. 6844-6851, 2018.
A. Nayyar and V. Puri, "Smart Monitoring and Controlling of Biomedical Signals Using IoT," in 2018 International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2018, pp. 167-174.
J. Li, H. Zhang, X. Chen, and J. Song, "Wearable IoT Monitoring System for Health," Sensors, vol. 18, no. 7, p. 2254, 2018.
P. Gope and T. Hwang, "BSN-Care: A Secure IoT-Based Modern Healthcare System Using Body Sensor Network," IEEE Sensors Journal, vol. 16, no. 5, pp. 1368-1376, 2016.
M. Conti, Q. Li, and S. Kumar, "Wearable Healthcare Monitoring Systems: Challenges and Opportunities," IEEE Internet of Things Journal, vol. 6, no. 1, pp. 171-180, 2019.
A. Polak, S. Fedor, and T. Martinek, "IoT and Cloud-Based Wearable Device for Long-Term Health Monitoring," IEEE Access, vol. 7, pp. 23554-23563, 2019.
H. Li, K. Ota, and M. Dong, "Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing," IEEE Network, vol. 32, no. 1, pp. 96-101, 2018.
Al Bassam, N., Hussain, S. A., Al Qaraghuli, A., Khan, J., Sumesh, E. P., & Lavanya, V. (2021). IoT based wearable device to monitor the signs of quarantined remote patients of COVID-19.Informatics in medicine unlocked,24, 100588.
Keserwani, H., Kakade, S. V., Sharma, S. K., Manchanda, M., & Nama, G. F. (2023). Real-Time Analysis of Wearable Sensor Data Using IoT and Machine Learning in Healthcare.International Journal of Intelligent Systems and Applications in Engineering,11(7s), 85-90.
Abdulmalek, S., Nasir, A., Jabbar, W. A., Almuhaya, M. A., Bairagi, A. K., Khan, M. A. M., & Kee, S. H. (2022, October). IoT-based healthcare-monitoring system towards improving quality of life: A review. InHealthcare(Vol. 10, No. 10, p. 1993). MDPI.
Ganji, K., & Parimi, S. (2022). ANN model for user perception on IOT based smart healthcare monitoring devices and its impact with the effect of COVID 19.Journal of Science and Technology Policy Management,13(1), 6-21.
Shumba, A. T., Montanaro, T., Sergi, I., Fachechi, L., De Vittorio, M., & Patrono, L. (2022). Leveraging IOT-aware technologies and AI techniques for real-time critical healthcare applications.Sensors,22(19), 7675.
Abbas, S.H., Ranjan, R., Maurya, B., Warsi, A.H., & Khan, S. (2025). Evaluating Healthcare Providers’ Perceptions, Expertise, and Barriers Regarding the Adoption of AI in Rehabilitation. Cuestiones de Fisioterapia, 54(3). Available at: https://doi.org/10.48047/9ea0sj23.
Zuberi, A. H., Anees, A., Anjum, N., Warsi, A. H., Khan, P. R., Singh, S. K., Singh, N. K., Singh, R., Abbas, S. H., & Ranjan, R. (2025). Machine Learning-Based Sentiment Analysis for Suicide Prevention and Mental Health Monitoring in Educational Institutions. Journal of Neonatal Surgery, Vol. 14, No. 5S. Scopus Q3.
Abbas, S. H. (2025). Modern Healthcare with Machine Learning: Innovation and Future Prospects. Cuestiones de Fisioterapia, Vol. 54, No. 4. Scopus Q3.
Kang, M. J., & Hwang, Y. C. (2022). Exploring the Factors Affecting the Continued Usage Intention of IoT-Based Healthcare Wearable Devices Using the TAM Model.Sustainability,14(19), 12492.
Jaber, M. M., Alameri, T., Ali, M. H., Alsyouf, A., Al-Bsheish, M., Aldhmadi, B. K., .. & Jarrar, M. T. (2022). Remotely monitoring COVID-19 patient health condition using metaheuristics convolute networks from IoT-based wearable device health data. Sensors, 22(3), 1205.
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