Queuing Modeling Approach to Developing Conceptual Framework for Smart Health Care System by using IoT and Cloud Computing Technologies
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Abstract
Healthcare IoT explodes with data from wearables and medical devices from wearable sensors and medical devices. Healthcare wrestles with mountains of data from wearables. Fog computing, processing data closer to its source, emerges as a solution for real-time patient insights can help to address this challenge. One key aspect of fog computing is load balancing, which distributes data Processing tasks across multiple servers to prevent overload and ensure smooth operation. In the context of health IoT, a “server busy” policy can be used to re-queue data when all servers are occupied. This ensures that critical health information is not lost and can be processed as soon as resources become available. With this in mind, a queuing modeling approach has been introduced to develop the conceptual framework for smart health care system in this paper for effectively computing the performance measures of the system.
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Ronaghi, Mohammad Hossein; Toward a model for assessing smart hospital readiness within the Industry 4.0 paradigm, Journal of Science and Technology Policy Management 15(2) (2024), 353-373.
Joseph Migga Kizza; Internet of things (iot): growth, challenges, and security, Guide to Computer Network Security, Springer International Publishing, 2024.
Mutlag, Ammar Awad, Mohd Khanapi Abd Ghani, Othman Mohd, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Meshal Alharbi, and Zaid J. Al-Araji; A new fog computing resource management (FRM) model based on hybrid load balancing and scheduling for critical healthcare applications, Physical Communication 59 (2023), 102109.
Ali, Ihsan, Hasniuj Zahan and Spyridon Mastorakis; Sensor Clouds: Recent Advancements, Use Cases and Open Challenges, IEEE Internet of Things Magazine 7(1) (2024), 98-103.
Turner, Sarah, and Leonie Maria Tanczer; In principle vs in practice: User, expert and policymaker attitudes towards the right to data portability in the internet of things, Computer Law & Security Review 52 (2024), 105912.
Evangelin, K. Ruth and V. Vidhya; Performance measures of queuing models using cloud computing, Asian Journal of Engineering and Applied Technology, 4(1) (2015), 8-11.
Mattia, Gabriele Proietti, Antonio Pietrabissa, and Roberto Beraldi; A load balancing algorithm for equalising latency across fog or edge computing nodes, IEEE Transactions on Services Computing, 16(5) (2023), 3129-3140.
S.T. Alam, F.A. Azrin, A.S.M.N. Uddin and E. Sagor; Formulation and performance assessment for multiple server queueing models, 2023.
Nakamura, Shigenari, Tomoya Enokido and Makoto Takizawa; Load balancing algorithm for information flow control in fog computing model, International Conference on Emerging Internetworking, Data & Web Technologies, Springer International Publishing, 2023.
M.M. Kembe, E.S. Onah and S. Iorkegh; A study of waiting and service costs of a multi-server queuing model in a specialist hospital, International journal of scientific & technology research, 1(8) (2012), 19-23.
Jafarnejad Ghomi, Einollah, Amir Masoud Rahmani, and Nooruldeen Nasih Qader; Applying queue theory for modeling of cloud computing: A systematic review, Concurrency and Computation: Practice and Experience, 31(17) (2019), e5186.
Arpita Basak, and Amit Choudhury; Bayesian inference and prediction in single server M/M/1 queuing model based on queue length, Communications in Statistics-Simulation and Computation, 50(6) (2021), 1576-1588.
A Adeniran, Muhammad Sani Burodo and Shamsuddeen Suleiman; Application of queuing theory and management of waiting time using multiple server model: empirical evidence from Ahmadu Bello University Teaching Hospital, Zaria, Kaduna State, Nigeria, International Journal of Scientific and Management Research, 5(4) (2022), 159-174.
Anshul Kumar and Madhu Jain; Cost optimization of an unreliable server queue with two stage service process under hybrid vacation policy, Mathematics and Computers in Simulation, 204 (2023), 259-281.
D. Baburao, T. Pavankumar and C.S.R. Prabhu; Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method, Applied Nanoscience, 13(2) (2023), 1045-1054..
M. Aazam, E.-N. Huh, M. St-Hilaire, C.-H. Lung, and I. Lambadaris; Cloud of things: integration of IoT with cloud computing, Robots and sensor clouds, (2016), 77-94.