An HRM System for Small and Medium Enterprises (Sme)s Based on Cloud Computing Technology

Abdulkareem

et al. 2021

QAJ

Self Cite

250

With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

Section: A Securitymentioning

confidence: 99%

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

Abdulkareem

et al. 2021

QAJ

Self Cite

250

“…The basic equipment is routinely facilitated in vastly distributed systems using various virtualization practices to achieve high scalability, availability, and agility [57]. However, cloud computing services' allocation serves as a study subject that led to groundbreaking algorithms and methods [58]. Cloud computing is the field of highest interest for telecommunications professionals, with potential uses in mind [59].…”

Section: H Distributed Systems and Cloud Computingmentioning

confidence: 99%

Energy Management for Internet of Things via Distributed Systems

2021

JASTT

108

The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggregators and the end-users participating in subsidiary systems within Smart Grid programmers and technologies. In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.

“…The components of the cloud are shown in Figure 1. [20] Data exchange in the cloud computers allows multiple users to openly share community data, which increases working efficiency and broadly applicate in cooperative environments [21,22]. However, data privacy maintenance in a community and the successful exchange of externalized data are great challenges in a group setting [23].…”

Section: Cloud Systemsmentioning

confidence: 99%

Comprehensive Survey of Big Data Mining Approaches in Cloud Systems

Ageed

et al. 2021

QAJ

107

Cloud computing, data mining, and big online data are discussed in this paper as hybridization possibilities. The method of analyzing and visualizing vast volumes of data is known as the visualization of data mining. The effect of computing conventions and algorithms on detailed storage and data communication requirements has been studied. When researching these approaches to data storage in big data, the data analytical viewpoint is often explored. These terminology and aspects have been used to address methodological development as well as problem statements. This will assist in the investigation of computational capacity as well as new knowledge in this area. The patterns of using big data were compared in about fifteen articles. In this paper, we research Big Data Mining Approaches in Cloud Systems and address cloud-compatible problems and computing techniques to promote Big Data Mining in Cloud Systems.