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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

A review on edge computing with data analysis and IoT techniques

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pp. 947–957Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1618XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1618
Pages:
947–957

Abstract

The Internet of Things (IoT) has permeated people’s daily lives, giving critical measurement and data collection capabilities to inform everyone’s choices. Edge computing has developed as a new paradigm for addressing IoT and localized computing requirements as a solution for mitigating resource congestion. These methods will produce huge volumes of important data at a network edge, necessitating not just immediate data processing but also smart data analysis to properly exploit the edge big data potential. Due to their limited calculation and high latency capability, both on-device computing and traditional cloud computing cannot adequately handle this issue. The purpose of this paper is to review recent efforts on machine learning and deep learning empowered edge computing applications and to provide insights into how to influence deep learning advances to ease edge applications from different domains, namely, smart city, smart industry, smart multimedia, and smart transportation. In a comparative analysis, cloud Machine Learning (ML) has the highest 98.04% accuracy and Convolutional Neural Network (CNN) ResNet-50 has 81.06% accuracy. Additionally, emphasize the critical research issues and promising research areas associated with them. This paper will inspire additional research and contributions to this promising area.

Keywords

Edge computingIoTReliabilitySecurityEcosystem

Subject Classifications

68xx

References

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