Semantic Scholar Open Access 2020 28 sitasi

A Novel Approach for Big Data Classification and Transportation in Rail Networks

Mahdi Saki M. Abolhasan J. Lipman

Abstrak

This paper introduces a new framework into future data-driven railway condition monitoring systems (RCM). For this purpose, we have proposed an edge processing unit that includes two main parts: a data classification model that classifies Internet of Things (IoT) data into maintenance-critical data (MCD) and maintenance-non-critical data (MNCD) and a data transmission unit that, based on the class of data, employs appropriate communication methods to transmit data to railway control centers. For the transmission of MNCD, we propose a travel pattern method that employs train stations as points of data offloading so that trains can deliver data as well as passengers at stations. The performance of our proposed solution is successfully validated via three various data sets under different operating conditions.

Topik & Kata Kunci

Computer Science

Penulis (3)

Mahdi Saki

M. Abolhasan

J. Lipman

Format Sitasi

APA MLA BibTeX

Saki, M., Abolhasan, M., Lipman, J. (2020). A Novel Approach for Big Data Classification and Transportation in Rail Networks. https://doi.org/10.1109/TITS.2019.2905611

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.1109/TITS.2019.2905611

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 28×
Sumber Database: Semantic Scholar
DOI: 10.1109/TITS.2019.2905611
Akses: Open Access ✓