Department of Computer Science and Information Technology, School of Computing, Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia.
La Trobe Business School, La Trobe University, Bundoora, VIC 3086, Australia.
Sensors (Basel). 2023 Aug 20;23(16):7286. doi: 10.3390/s23167286.
In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.
在物联网 (IoT) 的领域中，传感器和执行器网络协同工作以完成特定任务。随着对 IoT 网络的需求持续增长，确保这项技术的稳定性并对其进行扩展变得至关重要。通过对相关工作的分析，包括基于反馈的优化模糊调度方法（FOFSA）算法、自适应任务分配技术（ATAT）和渗透负载均衡算法（OLB），我们发现它们在实现最佳能效和快速决策方面存在局限性。为了解决这些局限性，本研究提出了一种新的方法来提高 IoT 网络的处理时间和能效。该方法通过在 Mist 层在早期阶段高效分配 IoT 数据资源来实现这一点。我们将该方法应用于我们提出的 Mist 基于模糊医疗保健系统（MFHS）中，该系统具有克服现有挑战并为未来高效的医疗保健物联网 (IIoHT) 铺平道路的巨大潜力。
