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%.

物联网 (IoT) 是一种新兴范式，由许多带有嵌入式传感器的连接和相互关联的设备组成，通过无线网络和互联网相互交换数据并与中心节点交换数据。最近，由于人类福祉至关重要，支持物联网的医疗保健系统受到了极大关注。另一方面，由于物联网网络规模庞大且由电池供电，因此为其开发适当的能源和资源管理机制是不可避免的。由于物联网环境中产生大量数据，数据聚合对于降低能耗和延长网络寿命至关重要，许多研究人员一直在努力提高其效率。然而，没有针对医疗保健应用的动态、复杂和非线性性质的优化方法。模糊逻辑在这些场景中可能是有效的，因为它可以将定性数据转换为定量数据，实现复杂的非线性函数，并为没有单一最佳答案的情况提供近似解，并且随着条件的轻微变化而变化。本文提出了 F-LEACH，这是一种基于模糊的数据聚合方案，用于支持物联网的医疗保健应用程序，旨在最大限度地延长网络寿命。根据模拟结果，所提出的方法比同类工作高出 5-20%。一种基于模糊的数据聚合方案，用于支持物联网的医疗保健应用程序，旨在最大限度地延长网络寿命。根据模拟结果，所提出的方法比类似的工作高出 5-20%。一种基于模糊的数据聚合方案，用于支持物联网的医疗保健应用程序，旨在最大限度地延长网络寿命。根据模拟结果，所提出的方法比类似的工作高出 5-20%。