Optimizing Telehealth Internet of Things Integration: A Sustainable Approach through Fog and Cloud Computing Platforms for Energy Efficiency

Authors: Yunyong Guo, Sudhakar Ganti, Bryan Guo

Abstract:

The swift proliferation of telehealth Internet of Things (IoT) devices has sparked concerns regarding energy consumption and the need for streamlined data processing. This paper presents an energy-efficient model that integrates telehealth IoT devices into a platform based on fog and cloud computing. This integrated system provides a sustainable and robust solution to address the challenges. Our model strategically utilizes fog computing as a localized data processing layer and leverages cloud computing for resource-intensive tasks, resulting in a significant reduction in overall energy consumption. The incorporation of adaptive energy-saving strategies further enhances the efficiency of our approach. Simulation analysis validates the effectiveness of our model in improving energy efficiency for telehealth IoT systems, particularly when integrated with localized fog nodes and both private and public cloud infrastructures. Subsequent research endeavors will concentrate on refining the energy-saving model, exploring additional functional enhancements, and assessing its broader applicability across various healthcare and industry sectors.

Keywords: Energy-efficient, fog computing, IoT, telehealth.

References:

[1] Atlam, H. F., Walters, R. J., and Wills, G. B. (2018). Fog computing and the internet of things: A review. Big Data and Cognitive Computing, 2(2):10, DOI:10.3390/bdcc202001.
[2] Kumar, P., Patil, K., Lee, J. H., & Lee, H. J. (2020). IoT-based remote patient monitoring: A survey on the capabilities, challenges, and future directions. Electronics, 9(10), 1702.
[3] Gupta, H., Nath, A. R., Chakraborty, S., & Ghosh, S. K. (2016). iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: Practice and Experience, 47(9), 1275-1296.
[4] Oueis, J., Strinati, E. C., & Barbarossa, S. (2015). The fog balancing: Load distribution for small cell cloud computing. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) (pp. 1-6). IEEE.
[5] Barcelo, M., Correa, A., Llorca, J., Tulino, A. M., Vicario, J. L., & Morell, A. (2016). IoT-cloud service optimization in next generation smart environments. IEEE Journal on Selected Areas in Communications, 34(12), 4077-4090.
[6] Zeng, X., Garg, S., & Strazdins, P. (2017). A comparative study of IoT cloud and fog computing simulations using iFogSim and Cooja. In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) (pp. 108-113). IEEE.
[7] Byers, C. C., & Wetterwald, P. (2015). Fog computing distributing data and intelligence for resiliency and scale necessary for IoT: The Internet of Things (ubiquity symposium). Ubiquity, 2015(November), 1-12.
[8] Bermejo, P. J., Rodríguez, S., Valladares, D. R., & Boubeta-Puig, J. (2020). YAFS: A simulator for IoT scenarios in fog computing. IEEE Access, 8, 111908-111922.
[9] García, A. M., Pérez, J. P., & Bellido, O. J. (2018). YAFS: A simulator for IoT scenarios in fog computing. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 215-222). IEEE.