Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems
Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems
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DOI：
10.1109/tnse.2022.3223844
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发表时间：
2023-09
影响因子：
6.6
通讯作者：
Senthil Murugan Nagarajan;Ganesh Gopal Devarajan;A. Mohammed;T. V. Ramana;Uttam Ghosh
中科院分区：
文献类型：
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作者：
Senthil Murugan Nagarajan;Ganesh Gopal Devarajan;A. Mohammed;T. V. Ramana;Uttam Ghosh
基于云计算的网络物理系统（CPS）通过互联网提供资源，并允许部署各种应用以为各个行业提供服务。我们提出了基于物联网的医疗网络物理系统，以最小的执行成本在雾和云级别提供有效的资源利用。此外，我们还考虑了社交媒体网络和药物评论的数据进行分析。此外，两种不同的特征提取方法应用的基础上收集的数据。采用基于同质性得分的K均值聚类作为传感器数据特征的特征提取和选择方法，采用文本挖掘和情感分析方法进行社交媒体网络和药品评论数据的特征提取。在雾层提出了有效的资源利用率和成本效益的任务调度，在云层提出了多目标启发式蚁群优化任务调度（MOHACO-TS）。这两种任务调度算法都侧重于在最短的时间内执行最多的任务并有效利用资源。我们考虑了五种不同的数据集和现有的任务调度和分类方法，用于对所提出的IoT HCPS框架进行性能评估。从结果来看，很明显，所提出的工作IoT-HCPS优于加密技术和算法。
Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.