Jazaeri Seyedeh Shabnam, Asghari Parvaneh, Jabbehdari Sam, Javadi Hamid Haj Seyyed
Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
J Supercomput. 2023 May 9:1-51. doi: 10.1007/s11227-023-05332-x.
This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources.
本文提出了一种新颖的方法，该方法使用谱聚类方法根据患者的相似性和距离对使用电子健康物联网设备的患者进行聚类，并将每个聚类连接到一个软件定义网络（SDN）边缘节点以进行高效缓存。所提出的多蜂群优化（MFO）边缘缓存算法用于根据考虑的标准选择接近最优的数据选项进行缓存，并提高服务质量（QoS）。实验结果表明，所提出的方法在性能方面优于其他方法，实现了数据检索延迟之间的平均时间减少以及76%的缓存命中率。紧急和按需请求被优先用于缓存响应数据包，而周期性请求的缓存命中率较低，为35%。与其他方法相比，该方法在性能上有所改进，突出了SDN边缘缓存和聚类在优化电子健康网络资源方面的有效性。
