A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure
The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called “sleeping cells”, which can render...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9046847/ |
id |
doaj-28f7a95cfb4b4cb2aa3acc2ea0244968 |
---|---|
record_format |
Article |
spelling |
doaj-28f7a95cfb4b4cb2aa3acc2ea02449682021-03-30T01:29:32ZengIEEEIEEE Access2169-35362020-01-018612136122510.1109/ACCESS.2020.29833839046847A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications InfrastructureOrestes G. Manzanilla-Salazar0https://orcid.org/0000-0001-6287-6516Filippo Malandra1https://orcid.org/0000-0002-1817-0310Hakim Mellah2https://orcid.org/0000-0002-1540-3449Constant Wette3https://orcid.org/0000-0002-9188-3587Brunilde Sanso4https://orcid.org/0000-0002-6055-4625Polytechnique Montréal, Montreal, QC, CanadaDepartment of Electrical Engineering, University at Buffalo, Buffalo, NY, USAPolytechnique Montréal, Montreal, QC, CanadaEricsson, Stockholm, SwedenPolytechnique Montréal, Montreal, QC, CanadaThe smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called “sleeping cells”, which can render a cell catatonic without triggering any alarms or provoking immediate effects on cell performance, making them difficult to discover. To detect this kind of failure, we propose a Machine Learning (ML) framework based on the use of Key Performance Indicators (KPIs) statistics from the BS under study, as well as those of the neighboring BSs with propensity to have their performance affected by the failure. A simple way to define neighbors is to use adjacency in Voronoi diagrams. In this paper, we propose a much more realistic approach based on the nature of radio-propagation and the way devices choose the BS to which they send access requests. We gather data from large-scale simulators that use real location data for BSs and IoT devices and pose the detection problem as a supervised binary classification problem. We measure the effects on the detection performance by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition. The Extra Trees and Naive Bayes classifiers achieve Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) scores of 0.996 and 0.993, respectively, with False Positive Rates (FPRs) under 5%. The proposed framework holds potential for other pattern recognition tasks in smart-city wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications.https://ieeexplore.ieee.org/document/9046847/Failure detectionIoTM2M communicationsmachine learningsleeping cellssmart cities |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Orestes G. Manzanilla-Salazar Filippo Malandra Hakim Mellah Constant Wette Brunilde Sanso |
spellingShingle |
Orestes G. Manzanilla-Salazar Filippo Malandra Hakim Mellah Constant Wette Brunilde Sanso A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure IEEE Access Failure detection IoT M2M communications machine learning sleeping cells smart cities |
author_facet |
Orestes G. Manzanilla-Salazar Filippo Malandra Hakim Mellah Constant Wette Brunilde Sanso |
author_sort |
Orestes G. Manzanilla-Salazar |
title |
A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure |
title_short |
A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure |
title_full |
A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure |
title_fullStr |
A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure |
title_full_unstemmed |
A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure |
title_sort |
machine learning framework for sleeping cell detection in a smart-city iot telecommunications infrastructure |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called “sleeping cells”, which can render a cell catatonic without triggering any alarms or provoking immediate effects on cell performance, making them difficult to discover. To detect this kind of failure, we propose a Machine Learning (ML) framework based on the use of Key Performance Indicators (KPIs) statistics from the BS under study, as well as those of the neighboring BSs with propensity to have their performance affected by the failure. A simple way to define neighbors is to use adjacency in Voronoi diagrams. In this paper, we propose a much more realistic approach based on the nature of radio-propagation and the way devices choose the BS to which they send access requests. We gather data from large-scale simulators that use real location data for BSs and IoT devices and pose the detection problem as a supervised binary classification problem. We measure the effects on the detection performance by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition. The Extra Trees and Naive Bayes classifiers achieve Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) scores of 0.996 and 0.993, respectively, with False Positive Rates (FPRs) under 5%. The proposed framework holds potential for other pattern recognition tasks in smart-city wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications. |
topic |
Failure detection IoT M2M communications machine learning sleeping cells smart cities |
url |
https://ieeexplore.ieee.org/document/9046847/ |
work_keys_str_mv |
AT orestesgmanzanillasalazar amachinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT filippomalandra amachinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT hakimmellah amachinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT constantwette amachinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT brunildesanso amachinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT orestesgmanzanillasalazar machinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT filippomalandra machinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT hakimmellah machinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT constantwette machinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure AT brunildesanso machinelearningframeworkforsleepingcelldetectioninasmartcityiottelecommunicationsinfrastructure |
_version_ |
1724186875093057536 |