Increasing Interoperability by Using Distributed Computation, Machine Learning, and the IoT Ecosystem

Mikhail Tishin; Constandinos X. Mavromoustakis; Athina Bourdena; Evangelos K. Markakis; George Mastorakis; Evangelos Pallis

doi:10.1007/978-3-031-58527-2_10

Increasing Interoperability by Using Distributed Computation, Machine Learning, and the IoT Ecosystem

Mikhail Tishin, Constandinos X. Mavromoustakis, Athina Bourdena, Evangelos K. Markakis, George Mastorakis, Evangelos Pallis

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Distributed Computation and Machine Learning is a topic, which blends two independent topics from Computer Science. These fields are so much independent that each of them by itself can be a topic for research. It is difficult to overestimate their influence and practical meaning for a wide range of industries in the modern world, to name a few of many—IoT, Business Corporations, Factories, and Pharmaceuticals. In this chapter, I will briefly cover the essential concepts of Distributed Computation, Machine Learning, as well as the field of their interception between Distributed Computation and Machine Learning.

Original language	English
Title of host publication	Signals and Communication Technology
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	219-240
Number of pages	22
DOIs	https://doi.org/10.1007/978-3-031-58527-2_10
Publication status	Published - 2024

Publication series

Name	Signals and Communication Technology
Volume	Part F3315
ISSN (Print)	1860-4862
ISSN (Electronic)	1860-4870

Keywords

Distributed Computation
Interoperability
IoT ecosystem
Machine Learning

Access to Document

10.1007/978-3-031-58527-2_10

Cite this

Tishin, M., Mavromoustakis, C. X., Bourdena, A., Markakis, E. K., Mastorakis, G., & Pallis, E. (2024). Increasing Interoperability by Using Distributed Computation, Machine Learning, and the IoT Ecosystem. In Signals and Communication Technology (pp. 219-240). (Signals and Communication Technology; Vol. Part F3315). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-58527-2_10

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abstract = "Distributed Computation and Machine Learning is a topic, which blends two independent topics from Computer Science. These fields are so much independent that each of them by itself can be a topic for research. It is difficult to overestimate their influence and practical meaning for a wide range of industries in the modern world, to name a few of many—IoT, Business Corporations, Factories, and Pharmaceuticals. In this chapter, I will briefly cover the essential concepts of Distributed Computation, Machine Learning, as well as the field of their interception between Distributed Computation and Machine Learning.",

keywords = "Distributed Computation, Interoperability, IoT ecosystem, Machine Learning",

author = "Mikhail Tishin and Mavromoustakis, {Constandinos X.} and Athina Bourdena and Markakis, {Evangelos K.} and George Mastorakis and Evangelos Pallis",

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doi = "10.1007/978-3-031-58527-2_10",

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Tishin, M, Mavromoustakis, CX, Bourdena, A, Markakis, EK, Mastorakis, G & Pallis, E 2024, Increasing Interoperability by Using Distributed Computation, Machine Learning, and the IoT Ecosystem. in Signals and Communication Technology. Signals and Communication Technology, vol. Part F3315, Springer Science and Business Media Deutschland GmbH, pp. 219-240. https://doi.org/10.1007/978-3-031-58527-2_10

Increasing Interoperability by Using Distributed Computation, Machine Learning, and the IoT Ecosystem. / Tishin, Mikhail; Mavromoustakis, Constandinos X.; Bourdena, Athina et al.
Signals and Communication Technology. Springer Science and Business Media Deutschland GmbH, 2024. p. 219-240 (Signals and Communication Technology; Vol. Part F3315).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Markakis, Evangelos K.

AU - Mastorakis, George

AU - Pallis, Evangelos

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KW - IoT ecosystem

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PB - Springer Science and Business Media Deutschland GmbH

ER -

Increasing Interoperability by Using Distributed Computation, Machine Learning, and the IoT Ecosystem

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Keywords

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