An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment


Autoři: Saurabh Shukla aff001;  Mohd Fadzil Hassan aff001;  Muhammad Khalid Khan aff002;  Low Tang Jung aff001;  Azlan Awang aff003
Působiště autorů: Centre for Research in Data Science (CeRDaS), Computer and Information Science Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia aff001;  College of Computing and Information Sciences, PAF-KIET, Karachi, Pakistan aff002;  Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224934

Souhrn

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.

Klíčová slova:

Algorithms – Cloud computing – Clouds – Computer networks – Electrocardiography – Evolutionary algorithms – Machine learning – Machine learning algorithms


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Článek vyšel v časopise

PLOS One


2019 Číslo 11