A Multivariate-Time-Series-Prediction-Based Adaptive Data Transmission Period Control Algorithm for IoT Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Jaeseob | - |
dc.contributor.author | Lee, Gyeong Ho | - |
dc.contributor.author | Park, Sangdon | - |
dc.contributor.author | Lee, Joohyung | - |
dc.contributor.author | Choi, Jun Kyun | - |
dc.date.accessioned | 2022-01-08T06:40:26Z | - |
dc.date.available | 2022-01-08T06:40:26Z | - |
dc.date.created | 2022-01-08 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83181 | - |
dc.description.abstract | In order to reduce unnecessary data transmissions from Internet of Things (IoT) sensors, this article proposes a multivariate-time-series-prediction-based adaptive data transmission period control (PBATPC) algorithm for IoT networks. Based on the spatio-temporal correlation between multivariate time-series data, we developed a novel multivariate time-series data encoding scheme utilizing the proposed time-series distance measure ADMWD. Composed of two significant factors for a multivariate time-series prediction, i.e., the absolute deviation from the mean (ADM) and the weighted differential (WD) distance, the ADMWD considers both the time distance from a prediction point and a negative correlation between the time-series data concurrently. Utilizing the convolutional neural network (CNN) model, a subset of IoT sensor readings can be predicted from encoded multivariate time-series measurements, and we compared the predicted sensor values with actual readings to obtain the adaptive data transmission period. Extensive performance evaluations show a substantial performance gain of the proposed algorithm in terms of the average power reduction ratio (approximately 12%) and average data reconstruction error (approximately 8.32% MAPE). Finally, this article also provides a practical implementation of the proposed PBATPC algorithm via the HTTP protocol under the IEEE 802.11-based WLAN network. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE INTERNET OF THINGS JOURNAL | - |
dc.title | A Multivariate-Time-Series-Prediction-Based Adaptive Data Transmission Period Control Algorithm for IoT Networks | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000733323800030 | - |
dc.identifier.doi | 10.1109/JIOT.2021.3124673 | - |
dc.identifier.bibliographicCitation | IEEE INTERNET OF THINGS JOURNAL, v.9, no.1, pp.419 - 436 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85118674312 | - |
dc.citation.endPage | 436 | - |
dc.citation.startPage | 419 | - |
dc.citation.title | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.volume | 9 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Lee, Joohyung | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | data transmission period | - |
dc.subject.keywordAuthor | Internet of Things (IoT) | - |
dc.subject.keywordPlus | CORRELATION-COEFFICIENT | - |
dc.subject.keywordPlus | INTERNET | - |
dc.subject.keywordPlus | THINGS | - |
dc.subject.keywordPlus | COMPUTATION | - |
dc.subject.keywordPlus | THROUGHPUT | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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