IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment

Gi dae Hong; Young Min An; Seol Roh; Eui Nam Huh

doi:10.1007/978-981-99-1252-0_103

IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment

Gi dae Hong, Young Min An, Seol Roh, Eui Nam Huh

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

Abstract

The Internet of Things (IoT) environment, which enables networking and computing in everything, is rapidly spreading. IoT environments cause bottlenecks and service delays to process data and provide services to users through a cloud-based central processing structure. In order to solve this problem, edge computing, which provides services to users by directly frosting data from IoT nodes and access networks, is rapidly spreading to cloud environment, where research continues to efficiently provide low delay intelligent services to users. In this paper, the response time of the service required to complete LSTM training is mainly measured by comparing to the locality of centralized data in the core cloud. In addition, the accuracy of the existing LSTM and the improved algorithm is compared on various reliability conditions.

Original language	English
Title of host publication	Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022
Editors	Ji Su Park, Laurence T. Yang, Yi Pan, Yi Pan, Jong Hyuk Park
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	771-778
Number of pages	8
ISBN (Print)	9789819912513
DOIs	https://doi.org/10.1007/978-981-99-1252-0_103
Publication status	Published - 2023
Event	14th International Conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022 - Vientiane, Lao People's Democratic Republic Duration: 19 Dec 2022 → 21 Dec 2022

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1028 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	14th International Conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022
Country/Territory	Lao People's Democratic Republic
City	Vientiane
Period	19/12/22 → 21/12/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Apache Spark
Edge-computing
LSTM

Access to Document

10.1007/978-981-99-1252-0_103

Cite this

Hong, G. D., An, Y. M., Roh, S., & Huh, E. N. (2023). IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment. In J. S. Park, L. T. Yang, Y. Pan, Y. Pan, & J. H. Park (Eds.), Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022 (pp. 771-778). (Lecture Notes in Electrical Engineering; Vol. 1028 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-1252-0_103

Hong, Gi dae ; An, Young Min ; Roh, Seol et al. / IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment. Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022. editor / Ji Su Park ; Laurence T. Yang ; Yi Pan ; Yi Pan ; Jong Hyuk Park. Springer Science and Business Media Deutschland GmbH, 2023. pp. 771-778 (Lecture Notes in Electrical Engineering).

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abstract = "The Internet of Things (IoT) environment, which enables networking and computing in everything, is rapidly spreading. IoT environments cause bottlenecks and service delays to process data and provide services to users through a cloud-based central processing structure. In order to solve this problem, edge computing, which provides services to users by directly frosting data from IoT nodes and access networks, is rapidly spreading to cloud environment, where research continues to efficiently provide low delay intelligent services to users. In this paper, the response time of the service required to complete LSTM training is mainly measured by comparing to the locality of centralized data in the core cloud. In addition, the accuracy of the existing LSTM and the improved algorithm is compared on various reliability conditions.",

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Hong, GD, An, YM, Roh, S & Huh, EN 2023, IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment. in JS Park, LT Yang, Y Pan, Y Pan & JH Park (eds), Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022. Lecture Notes in Electrical Engineering, vol. 1028 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 771-778, 14th International Conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022, Vientiane, Lao People's Democratic Republic, 19/12/22. https://doi.org/10.1007/978-981-99-1252-0_103

IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment. / Hong, Gi dae; An, Young Min; Roh, Seol et al.
Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022. ed. / Ji Su Park; Laurence T. Yang; Yi Pan; Yi Pan; Jong Hyuk Park. Springer Science and Business Media Deutschland GmbH, 2023. p. 771-778 (Lecture Notes in Electrical Engineering; Vol. 1028 LNEE).

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

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AB - The Internet of Things (IoT) environment, which enables networking and computing in everything, is rapidly spreading. IoT environments cause bottlenecks and service delays to process data and provide services to users through a cloud-based central processing structure. In order to solve this problem, edge computing, which provides services to users by directly frosting data from IoT nodes and access networks, is rapidly spreading to cloud environment, where research continues to efficiently provide low delay intelligent services to users. In this paper, the response time of the service required to complete LSTM training is mainly measured by comparing to the locality of centralized data in the core cloud. In addition, the accuracy of the existing LSTM and the improved algorithm is compared on various reliability conditions.

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Hong GD, An YM, Roh S, Huh EN. IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment. In Park JS, Yang LT, Pan Y, Pan Y, Park JH, editors, Advances in Computer Science and Ubiquitous Computing - Proceedings of CUTE-CSA 2022. Springer Science and Business Media Deutschland GmbH. 2023. p. 771-778. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-99-1252-0_103

IoT Sensor Predictive Analysis System Using Apache Spark in Edge Cloud Environment

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Bibliographical note

Keywords

Access to Document

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