3 research outputs found
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 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 Indicator (KPI) 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 Rate (FPR) 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.Comment: Submitted to the IEEE Access Journa