A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

Abeer Mohammed Shanshool, Noor Alhuda F. Abbas


The Internet-of-Things (IoT) is an important technology and is considered the future of the Internet. Healthcare is described as one of the important areas in IoT used for remote patient monitoring. Real-time remote monitoring health applications are important as delays in data transfer between the cloud, and the application may be unacceptable. Fog computing refers to a geographically distributed computing system with several devices connected to the same network to achieve flexible and collaborative computation, storage, and communication services. Fog computing is mainly used for efficient data processing between sensors and cloud computing as it reduces the volume of data exchanged between sensors and the cloud, thereby improving the whole system’s efficiency. Wireless sensor networks (WSN) are also used in health monitoring systems to simultaneously transfer huge data volumes (of different priority levels and length values) to the fog computing system. Hence, there is a need to appropriately implement a task scheduling mechanism that can accurately prioritize tasks irrespective of their length. This study aims to systematically review the existing fog computing technologies in the Internet of things HealthCare (IoTH) systems and improve the performance of the available static task scheduling algorithms using the Tasks Classification (TC) method where task importance is paramount. The performance of the suggested approach was evaluated based on the Max-Min scheduling algorithm (SA).


Fog computing (FC); edge computing; cloud computing; AI; IoT healthcare.

Full Text:



M. Anuradha et al., “IoT enabled cancer prediction system to enhance the authentication and security using cloud computing,†Microprocessors and Microsystems, vol. 80, p. 103301, 2021.

F. Muheidat and L. Tawalbeh, “Mobile and cloud computing security,†in Machine Intelligence and Big Data Analytics for Cybersecurity Applications, Springer, 2021, pp. 461–483.

G. Rekha, A. K. Tyagi, and N. Anuradha, “Integration of fog computing and internet of things: an useful overview,†in Proceedings of ICRIC 2019, Springer, 2020, pp. 91–102.

P. H. Vilela, J. J. P. C. Rodrigues, R. da R. Righi, S. Kozlov, and V. F. Rodrigues, “Looking at fog computing for e-health through the lens of deployment challenges and applications,†Sensors (Switzerland), vol. 20, no. 9. MDPI AG, May 01, 2020. doi: 10.3390/s20092553.

P. H. Vilela, J. J. P. C. Rodrigues, R. da R. Righi, S. Kozlov, and V. F. Rodrigues, “Looking at fog computing for e-health through the lens of deployment challenges and applications,†Sensors, vol. 20, no. 9, p. 2553, 2020.

J. Zhao, P. Zeng, and K.-K. R. Choo, “An efficient access control scheme with outsourcing and attribute revocation for fog-enabled E-health,†IEEE Access, vol. 9, pp. 13789–13799, 2021.

Z. Bakhshi, G. Rodriguez-Navas, and H. Hansson, “Dependable fog computing: A systematic literature review,†in 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2019, pp. 395–403.

M. Sarrab and F. Alshohoumi, “Assisted Fog Computing Approach for Data Privacy Preservation in IoT-Based Healthcare,†in Security and Privacy Preserving for IoT and 5G Networks, Springer, 2022, pp. 191–201.

F. M. Talaat, M. S. Saraya, A. I. Saleh, H. A. Ali, and S. H. Ali, “A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment,†Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 11, pp. 4951–4966, 2020.

I. S. B. M. Isa, T. E. H. El-Gorashi, M. O. I. Musa, and J. M. H. Elmirghani, “Energy Efficient Fog-Based Healthcare Monitoring Infrastructure,†IEEE Access, vol. 8, pp. 197828–197852, 2020.

M. Ahmed, R. Mumtaz, S. M. H. Zaidi, M. Hafeez, S. A. R. Zaidi, and M. Ahmad, “Distributed fog computing for Internet of Things (IOT) based ambient data processing and analysis,†Electronics (Basel), vol. 9, no. 11, p. 1756, 2020.

S. Shukla, M. Hassan, D. C. Tran, R. Akbar, I. V. Paputungan, and M. K. Khan, “Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR),†Cluster Computing, pp. 1–24, 2021.

C. L. Stergiou, A. P. Plageras, K. E. Psannis, and B. B. Gupta, “Secure machine learning scenario from big data in cloud computing via internet of things network,†in Handbook of computer networks and cyber security, Springer, 2020, pp. 525–554.

X. Li, Y. Lu, X. Fu, and Y. Qi, “Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing,†Future Generation Computer Systems, vol. 118, pp. 282–296, 2021.

M. Sriraghavendra, P. Chawla, H. Wu, S. S. Gill, and R. Buyya, “DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments,†in Energy Conservation Solutions for Fog-Edge Computing Paradigms, Springer, 2022, pp. 21–47.

M. Sheikh Sofla, M. Haghi Kashani, E. Mahdipour, and R. Faghih Mirzaee, “Towards effective offloading mechanisms in fog computing,†Multimedia Tools and Applications, vol. 81, no. 2, pp. 1997–2042, 2022.

R. Priyadarshini, R. Kumar Barik, and H. Dubey, “Fogâ€SDN: A light mitigation scheme for DdoS attack in fog computing framework,†International Journal of Communication Systems, vol. 33, no. 9, p. e4389, 2020.

N. Premkumar and R. Santhosh, “Challenges and Issues of E-Health Applications in Cloud and Fog Computing Environment,†in Mobile Computing and Sustainable Informatics, Springer, 2022, pp. 711–721.

G. Fersi, “Study of middleware for Internet of healthcare things and their applications,†in International Conference on Smart Homes and Health Telematics, 2020, pp. 223–231.

A. R. Nair and S. Tanwar, “Fog Computing Architectures and Frameworks for Healthcare 4.0,†in Fog Computing for Healthcare 4.0 Environments, Springer, 2021, pp. 55–78.

V. C. M. Leung, X. Wang, A. Jamalipour, X. Chen, and S. Bouzefrane, “IEEE Access Special Section Editorial: Edge Computing and Networking for Ubiquitous AI,†IEEE Access, vol. 9, pp. 90933–90936, 2021.

J. Zhu, J. Hu, M. Zhang, Y. Chen, and S. Bi, “A fog computing model for implementing motion guide to visually impaired,†Simulation Modelling Practice and Theory, vol. 101, p. 102015, 2020.

T. Qayyum, Z. Trabelsi, A. W. Malik, and K. Hayawi, “Multi-level resource sharing framework using collaborative fog environment for smart cities,†IEEE access, vol. 9, pp. 21859–21869, 2021.

M. T. Hossain and E. Robson, “Cloudlet dwell time model and resource availability for vehicular fog computing,†in 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 2021, pp. 1–8.

F. M. Talaat, “Effective prediction and resource allocation method (EPRAM) in fog computing environment for smart healthcare system,†Multimedia Tools and Applications, vol. 81, no. 6, pp. 8235–8258, 2022.

A. Bozorgchenani, D. Tarchi, and W. Cerroni, “On-Demand Service Deployment Strategies for Fog-as-a-Service Scenarios,†IEEE Communications Letters, vol. 25, no. 5, pp. 1500–1504, 2021.

G. Ortiz, M. Zouai, O. Kazar, A. Garcia-de-Prado, and J. Boubeta-Puig, “Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing,†Computer Standards & Interfaces, vol. 79, p. 103550, 2022.

A. M. Farooqi, M. A. Alam, S. I. Hassan, and S. M. Idrees, “A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation,†Applied Sciences, vol. 12, no. 4, p. 2083, 2022.

J. Choi and S. Ahn, “Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment,†Sensors, vol. 21, no. 4, p. 1506, 2021.

V. Dave and N. Joshi, “Fog computing enabled Ambient Assisted Healthcare systems,†in 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2019, pp. 1–7.

Z. Ning et al., “Mobile edge computing enabled 5G health monitoring for Internet of medical things: A decentralized game theoretic approach,†IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, pp. 463–478, 2020.

T. Alam, M. Tajammul, and R. Gupta, “Towards the Sustainable Development of Smart Cities Through Cloud Computing,†in AI and IoT for Smart City Applications, Springer, 2022, pp. 199–222.

M. Bhatia, T. A. Ahanger, U. Tariq, and A. Ibrahim, “Cognitive intelligence in fog computing-inspired veterinary healthcare,†Computers & Electrical Engineering, vol. 91, p. 107061, 2021.

T. M. Fernández-Caramés, I. Froiz-Míguez, O. Blanco-Novoa, and P. Fraga-Lamas, “Enabling the internet of mobile crowdsourcing health things: A mobile fog computing, blockchain and IoT based continuous glucose monitoring system for diabetes mellitus research and care,†Sensors, vol. 19, no. 15, p. 3319, 2019.

T. M. Fernández-Caramés, I. Froiz-Míguez, O. Blanco-Novoa, and P. Fraga-Lamas, “Enabling the internet of mobile crowdsourcing health things: A mobile fog computing, blockchain and IoT based continuous glucose monitoring system for diabetes mellitus research and care,†Sensors, vol. 19, no. 15, p. 3319, 2019.

N. Tewari and S. K. Budhani, “Analysis of Risk and Security Within Fog Computing-Enabled e-Healthcare System in Uttarakhand,†in Cyber Security and Digital Forensics, Springer, 2022, pp. 21–30.

L. Lakshmi, A. N. Kalyani, G. N. Satish, D. Swapna, and M. P. Reddy, “The preeminence of Fog Computing and IoT enabled Cloud Systems in Health care,†in 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021, pp. 368–375.

M. Abu-Elkheir, H. S. Hassanein, and S. M. A. Oteafy, “Enhancing emergency response systems through leveraging crowdsensing and heterogeneous data,†in 2016 international wireless communications and mobile computing conference (IWCMC), 2016, pp. 188–193.

R. Mahmud and A. N. Toosi, “Con-pi: A distributed container-based edge and fog computing framework,†IEEE Internet of Things Journal, 2021.

A. Mukherjee, S. Ghosh, A. Behere, S. K. Ghosh, and R. Buyya, “Internet of health things (IoHT) for personalized health care using integrated edge-fog-cloud network,†Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 943–959, 2021.

P. G. Shynu, V. G. Menon, R. L. Kumar, S. Kadry, and Y. Nam, “Blockchain-based secure healthcare application for diabetic-cardio disease prediction in fog computing,†IEEE Access, vol. 9, pp. 45706–45720, 2021.

R. Dhanagopal and B. Muthukumar, “IOT Based Energy Efficient Early Landslide Detection System,†Journal of Electrical Engineering, vol. 21, no. 1, p. 10, 2021.

D. Sarabia-Jácome, R. Usach, C. E. Palau, and M. Esteve, “Highly-efficient fog-based deep learning AAL fall detection system,†Internet of Things, vol. 11, p. 100185, 2020.

DOI: http://dx.doi.org/10.18517/ijaseit.12.6.16047


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development