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Latency aware smart health care system using edge and fog computing

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Abstract

Numerous gadgets are linked together globally by the Internet of Things (IoT). Health checking, exercise programmers and remote medical assistance are a few examples of emerging areas in the healthcare system. Implementing cloud computing functionality on edge devices is the constant goal of fog computing. The approach is anticipated to surpass the minimum latencies requirement when used with Internet of Things (IoT) medical equipment. IoT devices produce different amounts of healthcare data. Due to the enormous volume of data produced, networks get overloaded, increasing delay. Traditional cloud servers are unable to meet the low latency requirements of IoT medical equipment and consumers. IoT data transfer, it is therefore vital to reduce network latency, computation delay, and energy consumption. Using FC, data can be stored, processed, and analyzed. Cloud computing data is located at a network edge to reduce high latency. Here, a novel resolution to the problem mentioned earlier is proposed. It combines an analytical model with a hybrid fuzzy-based reinforced learning technique in an FC setting. The objective is to reduce energy usage and cloud server latency for health-care IoT. The Internet of Things-FC context is selected and placed by the proposed smart FC analysis technique and algorithm using a fuzzy inference system, optimization techniques, and development approaches. The results showed that our suggested strategy reduced latency by 1.2% in comparison to other techniques.

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Authors and Affiliations

  1. Department of Computer Science Faculty of Computing and Artificial Intelligent Air University, Islamabad, Pakistan

    Arif Ullah

  2. School of Computing, Riphah International University, Islamabad, Pakistan

    Saman Yasin

  3. Faculty of Computer and Information Systems, Islamic University of Madinah, Medina, Saudi Arabia

    Tanweer Alam

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  1. Arif Ullah
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  2. Saman Yasin
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Ullah, A., Yasin, S. & Alam, T. Latency aware smart health care system using edge and fog computing. Multimed Tools Appl 83, 34055–34081 (2024). https://doi.org/10.1007/s11042-023-16899-1

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