Open Access

ARTICLE

IoMT-Cloud Task Scheduling Using AI

Adedoyin A. Hussain^1,2,*, Fadi Al-Turjman^3,4

1Computer Engineering Department and Research Centre for AI and IoT, Near East University, Nicosia, Turkey
2Computer Engineering Department, Cyprus West University, Gazimagusa, Turkey
3Artificial Intelligence Engineering Department and AI and Robotics Institutes, Near East University, Nicosia, Turkey
4Research Centre for AI and IoT, Faculty of Engineering, University of Kyrenia, Kyrenia, Turkey

* Corresponding Author: Adedoyin A. Hussain. Email:

(This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)

Computer Modeling in Engineering & Sciences 2023, 135(2), 1345-1369. https://doi.org/10.32604/cmes.2023.022783

Received 25 March 2022; Accepted 13 June 2022; Issue published 27 October 2022

Abstract

The internet of medical things (IoMT) empowers patients to get adaptable, and virtualized gear over the internet. Task scheduling is the most fundamental problem in the IoMT-cloud since cloud execution commonly relies on it. Thus, a proposition is being made for a distinct scheduling technique to suitably meet these solicitations. To manage the scheduling issue, an artificial intelligence (AI) method known as a hybrid genetic algorithm (HGA) is proposed. The proposed AI method will be justified by contrasting it with other traditional optimization and AI scheduling approaches. The CloudSim is utilized to quantify its effect on various parameters like time, resource utilization, cost, and throughput. The proposed AI technique enhanced the viability of task scheduling with a better execution rate of 32.47 ms and a reduced time of 40.16 ms. Thus, the experimented outcomes show that the HGA reduces cost as well as time profoundly.

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