Tahir Mahmood
Enabling Predictive and Preventive Maintenance using IoT and Big Data in the Telecom Sector
Mahmood, Tahir; Munir, Kamran
Abstract
Telecom sector has always been working hard to improve network quality to satisfy end user services. Fixing telecom network errors (hardware and software) precisely and quickly is a main factor to improve quality of services. Telecom operators are spending a lot of budget on ad hoc maintenance to fix these errors. This paper presents a framework using internet of things (IoT) and big data to enable predictive and preventive maintenance, which have been applied in the telecom sector. A telecom network consists of radio nodes, transport network, switching centres and civil infrastructure; and in this paper, focus is on the maintenance of Radio Access Network (RAN). A challengeable task for telecom operators has been to maintain radio nodes as these are installed on different locations. This framework for predictive maintenance is modelled using active and historical data from telecom equipment as well as data collected from IoT devices and sensors. The major benefit of implementing this framework has been a control on the time and cost of the maintenance by pre-planning maintenance activities and related budget.
Citation
Mahmood, T., & Munir, K. (2020). Enabling Predictive and Preventive Maintenance using IoT and Big Data in the Telecom Sector. . https://doi.org/10.5220/0009325201690176
Conference Name | 5th International Conference on Internet of Things, Big Data and Security |
---|---|
Conference Location | Prague, Czech Republic |
Start Date | May 7, 2020 |
End Date | May 9, 2020 |
Acceptance Date | Feb 23, 2020 |
Online Publication Date | May 20, 2020 |
Publication Date | May 8, 2020 |
Deposit Date | May 22, 2020 |
Publicly Available Date | May 22, 2020 |
ISBN | 9789897584268 |
DOI | https://doi.org/10.5220/0009325201690176 |
Public URL | https://uwe-repository.worktribe.com/output/5994746 |
Files
Paper-IoTBDS-2020 Camera Ready KM
(249 Kb)
PDF
Licence
http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
Copyright 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. This article is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
You might also like
SWEL: A domain-specific language for modeling data-intensive workflows
(2023)
Journal Article
Application of region-based video surveillance in smart cities using deep learning
(2021)
Journal Article
Arabic semantic similarity approach for farmers’ complaints
(2021)
Journal Article
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search