Centre for Research in Data Science (CeRDaS), Computer and Information Science Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia.
College of Computing and Information Sciences, PAF-KIET, Karachi, Pakistan.
PLoS One. 2019 Nov 13;14(11):e0224934. doi: 10.1371/journal.pone.0224934. eCollection 2019.
Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
雾计算（FC）是一种在分布式环境中运行的新兴计算技术。FC 的目的是将云计算功能带到边缘设备。这种方法有望满足医疗物联网（IoT）设备的最低延迟要求。医疗 IoT 设备生成各种数量的医疗数据。这些大量数据导致高数据流量，从而导致网络拥塞和高延迟。由于物联网和云服务器之间的数据传输量大且跳数多，往返时间延迟增加，使得医疗数据变得毫无意义，对最终用户来说也不够用。对时间敏感的医疗应用程序需要实时数据。传统的云服务器无法满足医疗 IoT 设备和最终用户的最低延迟要求。因此，必须降低物联网数据传输的通信延迟、计算延迟和网络延迟。FC 为云计算提供了从网络边缘存储、处理和分析数据的功能，以降低高延迟。本文提出了一种解决上述问题的新方法。它包括在 FC 环境中使用分析模型和混合模糊强化学习算法。目的是降低医疗 IoT、最终用户和云服务器之间的高延迟。所提出的智能 FC 分析模型和算法使用模糊推理系统结合强化学习和神经网络进化策略，用于在 IoT-FC 环境中进行数据包分配和选择。该方法在模拟器 iFogSim（NetBeans）和 Spyder（Python）上进行了测试。结果表明，与现有方法相比，所提出的方法具有更好的性能。
