Applications of Big Data Analytics and Machine Learning in the Internet of Things

Yousefi, Shamim; Derakhshan, Farnaz; Karimipour, Hadis

doi:10.1007/978-3-030-38557-6_5

Cited by 18 publications

(5 citation statements)

References 115 publications

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“…Yousefi et al 65 reviewed in‐depth recent applications of big data analytics and ML in the IoT systems, in which a set of common issues and challenges are discussed. For concerned academics and developers, the evaluation might lead to new research paths in ML processes on the IoT.…”

Section: Ml‐based Solutions For Iotmentioning

confidence: 99%

A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

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The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.

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Section: Ml‐based Solutions For Iotmentioning

confidence: 99%

A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

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“…Yousefi et al [35] discussed the utilization of ANN to anticipate the IoT element state and how to minimize the expenses of IoT administration. The authors have depicted that edge security setups are labor-intensive.…”

Section: Securing Iot Systems Using Annmentioning

confidence: 99%

“…Malware detection using SVM by Agrawal [36], malware detection using Random Forest by Nakhodchi et al [37], and intrusion detection by Saharkhizan et al [38] may all be conducted on Fog nodes rather than on the cloud. Anomaly detection using ANN by Yousefi et al [35] specifically mentions using ML at the gateway layer. Table 5 shows another comparative analysis of the state-of-the-art literature review for IoT security using ML techniques.…”

Section: For Secure Fog Computingmentioning

confidence: 99%

Securing IoT-Empowered Fog Computing Systems: Machine Learning Perspective

et al. 2022

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The Internet of Things (IoT) is an interconnected network of computing nodes that can send and receive data without human participation. Software and communication technology have advanced tremendously in the last couple of decades, resulting in a considerable increase in IoT devices. IoT gadgets have practically infiltrated every aspect of human well-being, ushering in a new era of intelligent devices. However, the rapid expansion has raised security concerns. Another challenge with the basic approach of processing IoT data on the cloud is scalability. A cloud-centric strategy results from network congestion, data bottlenecks, and longer response times to security threats. Fog computing addresses these difficulties by bringing computation to the network edge. The current research provides a comprehensive review of the IoT evolution, Fog computation, and artificial-intelligence-inspired machine learning (ML) strategies. It examines ML techniques for identifying anomalies and attacks, showcases IoT data growth solutions, and delves into Fog computing security concerns. Additionally, it covers future research objectives in the crucial field of IoT security.

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“…From pharmaceutical companies to healthcare organizations, this enormous potential of data products, combined with IoT applications and AI tools [ 9 – 11 ], will play a significant role in the near future. Today, the medical applications based on IoT allow the monitoring of clinical data through the production of data generated by special devices (e.g., wearable devices) [ 12 ], remotely accessible by a physician rather than by caregivers [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review

Cozzoli

Salvatore

Faccilongo

et al. 2022

BMC Health Serv Res

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Background Multiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare? Methods Through a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by. Results The positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization. Conclusions In conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization.

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Applications of Big Data Analytics and Machine Learning in the Internet of Things

Cited by 18 publications

References 115 publications

A systematic literature review of machine learning applications in IoT

A systematic literature review of machine learning applications in IoT

Securing IoT-Empowered Fog Computing Systems: Machine Learning Perspective

How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review

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