2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 15 , Issue 1 , PP: 144-156, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ponugoti Kalpana 1 * , Potu Narayana 2 , Smitha, L. 3 , Dasari Madhavi 4 , K. Keerthi 5 , Aseel Smerat 6 , Muhannad Akram Nazzal 7
Doi: https://doi.org/10.54216/JISIoT.150112
Internet of Things (IoT) integrated with the disruptive technologies are becoming increasingly popular and they have extended their capabilities in all domains such as automotive, health care and automation. IoT is connecting the billions of devices and humans to bring the fruitful advantages to society. Since IoT devices are operated with the centralized cloud environment, pervasive and continuous monitoring of the user information can be facilitated. However, owing to the inherent characteristics of cloud, such as large end-to-end latency, larger bandwidth consumption, handling the larger volume of data from the IoT devices would be bottleneck for implementing the IoT for the smart health care system that aids for the treatment and diagnosis process. To address these issues, this research article proposes powerful paradigm, Heath-FoTs (Fog of things) which incorporates the fog devices where the data are processed and filtered near the IoT nodes which is useful for improving the quality of services. To further improve the speed of communication, distributed fogs are introduced between the IoT devices and Cloud to process the health care data and provides the optimal solution to tackle the latencies problems and bandwidth requirements. The complete experimentation is carried out using the NodeMCU and Raspberry Pi 3 Model in which the MQTT (Message Queuing Telemetry Transportation) protocol is used as the major communication protocol between the IoT and Fog Nodes. To evaluate the proposed model, performance metrics such as latency, throughput, and communication cost is measured and compared with the traditional environments. Results demonstrate the Health-FoTs environment has shown the promising performance with the 23% lesser latency, 32% higher throughputs and 25% less communication overhead than the traditional IoT infrastructure and proves its strong place for the high speed health care environment.
Internet of Things , Fog of Things , Distributed Fogs , Centralized Cloud Environment , MQTT , NodeMCU , Raspberry Pi Model B+
[1] K. S. Awaisi, S. Hussain, M. Ahmed, A. A. Khan, and G. Ahmed, "Leveraging IoT and fog computing in healthcare systems," IEEE Internet of Things Magazine, vol. 3, no. 2, pp. 52–56, 2021.
[2] F. Alkahtani and H. Nordin, "Deep learning methods for solar fault detection and classification: A review," Information Sciences Letters, vol. 10, no. 2, p. 13, 2021.
[3] A. Al-Sammarraee and N. J. Alshareeda, "The role of artificial intelligence by using automatic accounting information system in supporting the quality of financial statements," Information Sciences Letters, vol. 10, no. 2, p. 8, 2021.
[4] I. Atia, M. L. Salem, A. Elkholy, W. Elmashad, and G. A. Ali, "In-silico analysis of protein receptors contributing to SARS-CoV-2 high infectivity," Information Sciences Letters, vol. 10, no. 3, p. 19, 2021.
[5] Y. M. Al-Rawi, W. S. Al-Dayyeni, and I. J. Reda, "COVID-19 impact on education and work in the Kingdom of Bahrain: Survey study," Information Sciences Letters, vol. 10, no. 3, p. 5, 2021.
[6] S. Sengan et al., "Secured and privacy-based IDS for healthcare systems on e-medical data using a machine learning approach," International Journal of Reliable and Quality E-Healthcare, vol. 11, no. 3, pp. 1–11, 2022.
[7] S. A. Syed et al., "Design of resources allocation in 6G cybertwin technology using the fuzzy neuro model in healthcare systems," Journal of Healthcare Engineering, vol. 2022, 2022, Art. ID 4536789.
[8] M. E. Karar et al., "IoT and neural network-based water pumping control system for smart irrigation," Information Sciences Letters, vol. 9, no. 2, pp. 107–112, 2021.
[9] S. M. E. Ammer, "Content analysis of lighting and color in the embodiment of fear concept in horror movies: A semiotic approach," Information Sciences Letters, vol. 9, no. 2, pp. 135–142, 2021.
[10] M. E. Karar et al., "A pilot study of smart agricultural irrigation using unmanned aerial vehicles and IoT-based cloud system," Information Sciences Letters, vol. 10, no. 1, pp. 131–140, 2021.
[11] V. Kashyap, A. Kumar, and Y.-C. Hu, "A systematic survey on fog and IoT-driven healthcare: Open challenges and research issues," Electronics, vol. 11, no. 17, p. 2668, 2022.
[12] B. Farahani et al., "Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare," Future Generation Computer Systems, vol. 78, no. 2, pp. 659–676, 2018.
[13] P. Verma and S. K. Sood, "Fog-assisted IoT-enabled patient health monitoring in smart homes," IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1789–1796, 2018.
[14] A. Taloba et al., "Machine algorithm for heartbeat monitoring and arrhythmia detection based on ECG systems," Computational Intelligence and Neuroscience, vol. 2021, Art. ID 7677568, 9 pages, 2021.
[15] P. D. Singh et al., "Fog-centric IoT-based smart healthcare support service for monitoring and controlling an epidemic of swine flu virus," Informatics in Medicine Unlocked, vol. 26, p. 100636, 2021.
[16] V. K. Quy et al., "Smart healthcare IoT applications based on fog computing: Architecture, applications, and challenges," Complex & Intelligent Systems, vol. 8, no. 5, pp. 3805–3815, 2022.
[17] N. Kumari and V. K. Jain, "Fog-based healthcare monitoring system in SDN-IoT networks," in 2022 IEEE Advanced Technologies Conference, Bangalore, India, 2022, pp. 1–6, doi: 10.1109/ICATIECE56365.2022.10047334.
[18] A. George et al., "Internet of Things in healthcare using fog computing," in Proc. IEEE Long Island Systems, Applications, and Technology Conference (LISAT), Farmingdale, NY, USA, May 2018, pp. 1–6.
[19] I. Ahmad, S. Abdullah, and A. Ahmed, "IoT-fog-based healthcare 4.0 system using blockchain technology," Journal of Supercomputing, vol. 79, no. 4, pp. 3999–4020, 2023.
[20] S. Tripathy et al., "An intelligent healthcare system in fog platform with optimized performance," Sustainability, vol. 15, no. 3, p. 1862, 2023.
[21] D. Navakauskas and M. Kazlauskas, "Fog computing in healthcare: Systematic review," Informatica, vol. 34, no. 3, pp. 577–602, 2023.
[22] J. Bi et al., "Latency-minimized computation offloading in fog computing with hybrid whale optimization," in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic, 2022, pp. 504–509, doi: 10.1109/SMC53654.2022.9945284.
[23] Y. Cheng and H. Qian, "Minimizing fog nodes in double-layer distributed fog radio access networks," in 2022 IEEE Advanced Information Technology, Electronic, and Automation Control Conference, Beijing, China, 2022, pp. 373–377, doi: 10.1109/IAEAC54830.2022.9929717.
[24] S. Nabi et al., "Distributed private preserving learning-based chaotic encryption framework for cognitive healthcare IoT systems," Informatics in Medicine Unlocked, vol. 49, p. 101547, 2024.
[25] R. Mayer et al., "Emufog: Extensible and scalable emulation of large-scale fog computing infrastructures," arXiv preprint arXiv: 1709.07563, 2018.
[26] D. Mala et al., "Fog computing and IoT-based smart healthcare system for detecting heart-related problems," AIP Conference Proceedings, vol. 2451, no. 1, p. 020060, 2022.
[27] A. Elhadad et al., "Fog computing service in the healthcare monitoring system for managing real-time notification," Journal of Healthcare Engineering, vol. 2022, Art. ID 5337733, 2022.
[28] V. Kashyap et al., "Challenges and future directions of fog-driven IoT in healthcare," Electronics, vol. 11, no. 17, p. 2678, 2022.
[29] M. E. Karar et al., "Cloud and IoT-based smart irrigation systems for optimizing water usage," Information Sciences Letters, vol. 10, no. 3, pp. 100–106, 2021.
