面向医疗数据优化的自适应启发式边缘辅助雾计算设计

Syed Sabir Mohamed S, Gopi R, Thiruppathy Kesavan V, Karthikeyan Kaliyaperumal
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摘要

在当今快速发展的医疗保健系统中,实时医疗数据分析对病人护理、研究和决策都有帮助。这项研究的意义在于,它能够将计算资源迁移到更靠近数据源的地方,从而促进对医疗数据进行更快速、更准确的分析,从而彻底改变医疗系统。传统的以云为中心的技术普遍存在延迟、隐私问题和无法扩展等问题。边缘计算和雾计算能够就近处理数据,因此有可能彻底改变医疗分析。医疗保健行业在应用边缘计算和雾计算方面有着独特的机遇和问题。必须重视数据安全和隐私、工作负载灵活性、互操作性、资源优化以及无中断的数据集成。本研究建议采用自适应启发式边缘辅助雾计算设计(AHE-FCD),利用旨在改进医学分析的新型架构来解决这些问题。在 AHE-FCD 的帮助下,边缘设备和雾节点可共同执行分布式数据处理和分析。启发式算法通常用于优化问题,因为使用标准方法很难甚至不可能建立最佳解决方案。启发式算法利用搜索算法探索搜索空间并确定结果。AHE-FCD 可以在边缘和雾层进行实时、低延迟的分析,从而改善患者护理、医学研究和医疗流程效率。研究结果可能会改善医疗分析,使其具有最小延迟、高可靠性和数据隐私性。因此,复杂的分布式系统中的操作不是集中进行的,而是在多个端点进行的。这有助于在危险蔓延到整个网络之前更快地发现情况。AHE-FCD 是一个很有希望的突破,它使我们更接近于实现先进的医疗分析系统,在这种系统中,及时和知情的决策对于提供优质的医疗服务至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive heuristic edge assisted fog computing design for healthcare data optimization
Patient care, research, and decision-making are all aided by real-time medical data analysis in today’s rapidly developing healthcare system. The significance of this research comes in the fact that it has the ability to completely change the healthcare system by relocating computing resources closer to the data source, hence facilitating more rapid and accurate analysis of medical data. Latency, privacy concerns, and inability to scale are common in traditional cloud-centric techniques. With their ability to process data close to where it is created, edge and fog computing have the potential to revolutionize medical analysis. The healthcare industry has unique opportunities and problems for the application of edge and fog computing. There must be an emphasis on data security and privacy, workload flexibility, interoperability, resource optimization, and data integration without any interruptions. In this research, it is suggested the Adaptive Heuristic Edge assisted Fog Computing design (AHE-FCD) to solve these issues using a novel architecture meant to improve medical analysis. Together, edge devices and fog nodes may perform distributed data processing and analytics with the help of AHE-FCD. Heuristic algorithms are often employed for optimization issues that establishing an optimum solution using standard approaches is difficult and impossible. Heuristic algorithms utilize search algorithms to explore the search space and identify a result. Improved patient care, medical research, and healthcare process efficiency are all possible to AHE-FCD real-time, low-latency analysis at the edge and fog layers. Improved medical analysis with minimal latency, high reliability, and data privacy are all likely to emerge from the study’s findings. As a result, rather from being centralized, operations in a sophisticated distributed system occur at several end points. That helps the situation quicker to detect possible dangers prior to propagate across the network. The AHE-FCD is a promising breakthrough that moves us closer to the realization of advanced medical analysis systems, where prompt and well-informed decision-making is essential to providing excellent healthcare.
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