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F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

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Abstract

Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other and central nodes over a wireless network and internet. Recently, due to the crucial importance of human well-being, IoT-enabled healthcare systems have gained significant attention. On the other hand, as IoT networks are large-scaled and battery-powered, developing proper energy and resource management mechanisms for them is inevitable. On account of the large amount of data generated in IoT environments, data aggregation is vital to lower energy consumption and extend network lifespan, and many researchers have endeavored to enhance its efficiency. However, there is no optimized method for the dynamic, complex, and nonlinear nature of healthcare applications. Fuzzy logic could be effective in these scenarios because it can convert qualitative data to quantitative, implement complex nonlinear functions, and present approximate solutions for cases where there is no single optimal answer, and it changes with a slight variation in conditions. This paper proposes the F-LEACH, a Fuzzy-based data aggregation scheme for IoT-enabled healthcare applications aiming to maximize the network lifetime. According to the simulation results, the proposed method outperformed similar works by 5–20%.

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Authors and Affiliations

  1. Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyedeh Nafiseh Sajedi & Meisam Nesari Moghadam

  2. Department of Electrical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Mohsen Maadani

Authors
  1. Seyedeh Nafiseh Sajedi
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  2. Mohsen Maadani
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  3. Meisam Nesari Moghadam
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Correspondence to Mohsen Maadani.

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Sajedi, S.N., Maadani, M. & Nesari Moghadam, M. F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems. J Supercomput 78, 1030–1047 (2022). https://doi.org/10.1007/s11227-021-03890-6

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  • DOI: https://doi.org/10.1007/s11227-021-03890-6

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