Abstract

The Internet of Things (IoT) transforms many fields, including the educational, logistics, and manufacturing industries. The IoT is an internet framework whereby a large number of devices or equipment are connected and synchronized using gateways, third-party technologies, and software in machine-to-machine and cloud computing networks. With the flourishing development of IoT, cloud computing plays an essential role in its application layer. Cloud computing technology has been widely applied in various industries and developed as particular cloud computing types: education as a service (EaaS), logistics as a service (LaaS), and manufacturing as a service (MaaS). The applicability of cloud computing in various industries has attracted significant attention from researchers and professionals. This study investigated the technical trends of emerging cloud computing technologies and surveyed 3,697 cloud computing-related studies from 2010 to 2019. The findings indicate that intelligence and automation are the core issues that drive research on cloud computing. The main types of research are critical review, system design, and systematic analysis. Cloud computing services (e.g., XaaS, EaaS, LaaS, MaaS) are related to big data, analytical technologies, service orientation, and IoT. This study applied machine-learning algorithms to analyze educational, logistic, and manufacturing data and yielded results with more than 90% accuracy and AUC. This study used various devices such as laptops, tablets, and smartphones to configure and review machine-learning models using third-party cloud platforms, which are infinitely scalable and flexible for data analytics, thereby allowing users to make quicker predictions and decisions focused on business needs.

Highlights

  • In 2000, the MIT Auto-ID Center proposed the framework of the Internet of Things (IoT) [1]

  • APPLYING THE CLOUD COMPUTING SERVICE IN THE INDUSTRIES This study investigated education as a service (EaaS), logistics as a service (LaaS), and manufacturing as a service (MaaS) by applying a ML model to predict UCI data from various sources

  • receiver operating characteristic curve (ROC) is a probability curve plotted with the false positive rate (FPR) on the x-axis against sensitivity on the y-axis

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Summary

Introduction

In 2000, the MIT Auto-ID Center proposed the framework of the Internet of Things (IoT) [1]. The IoT European Research Council defined IoT as the connection of devices, physical objects, and humans at any time, in any place, with anything on the Internet [2], [3]. The IEEE IoT Initiative recently proposed that the standard of IoT framework involves descriptions of various IoT domains [4]. IoT can be considered as an infrastructure that connects devices and objects with networks and databases; it is expected to revolutionize many industries [5]. IoT provided a machine-to-machine (M2M) and human-to-machine (H2M) architecture for advanced metering infrastructure (AMI) [6].

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