Data Science for Internet of Things (IoT)

Devi, M. Ramya; Dhaya, R.; Kanthavel, R.; Algarni, Fahad; Dixikha, Pooja

doi:10.1007/978-3-030-37051-0_7

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…They also highlighted various applications of the industrial Internet of Things (IIoT) [ 27 ] such as smart transportation, etc. Devi et al in [ 10 ] highlighted the importance of data science as a sub-domain of the IoT. The authors highlighted traditional data science and the IoT.…”

Section: Basics and The Backgroundmentioning

confidence: 99%

“…Similarly, Youhua et al [ 4 ] presented the big data aspect and partially discussed the data science disciplines in their research study. Devi et al [ 10 ] explored the data science discipline for the IoT. They explained the importance of data science from the perspective of the IoE (Internet of Everything) [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…They explained the importance of data science from the perspective of the IoE (Internet of Everything) [ 11 ]. The IoE depends upon data science [ 10 ]. The authors did not cover network science and big data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Step toward Next-Generation Advancements in the Internet of Things Technologies

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Abbasi

Mateen

et al. 2022

Sensors

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The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.

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Section: Basics and The Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%