Cloud Provisioning and Modeling of Internet of Things (IoT) Systems for Evaluating Big Data Uncertainties

Abstract

The present cyber-physical schemes and the Internet of Things (IoT) schemes comprise of both complex and simple interactions defining the different sources of the IoT systems such as cloud information and the edge internet service centres. All the modeling frameworks have been established on the virtualization dimensions that include both the cloud and the edge structures. Apart from that, the systems deal with big data based on the connections of various forms of services and networks. In that case, various forms of data uncertainties are evident. These uncertainties include elasticity and actuation uncertainties. As a result, this leads to a number of challenges that affect the process of testing these uncertainties in the big data systems. Nonetheless, there is a research gap present to effectively model and design the precise infrastructure frameworks that handle the necessities for evaluating these emergent big data uncertainties. With that regard, this scholastic paper focusses on the techniques used to generate and determine the deployment configurations used in the process of evaluating both the cloud and IoT systems. In this research, the survey will consider the actual-world application for analysing and monitoring the transceiver frameworks.