A Machine Learning framework for Sleeping Cell Detection in a Smart-city IoT Telecommunications Infrastructure

TL;DR

This paper presents a machine learning framework for detecting sleeping cell failures in IoT telecommunications infrastructure, using KPI data and a realistic neighborhood definition based on radio propagation, achieving high detection accuracy.

Contribution

The paper introduces a novel ML-based detection framework utilizing realistic neighborhood modeling and KPI data, improving sleeping cell detection in smart-city IoT networks.

Findings

Achieved ROC AUC scores of 0.996 and 0.993 with low false positive rates.

Demonstrated effectiveness of ML classifiers in detecting sleeping cells.

Showed potential for broader pattern recognition in smart-city wireless systems.

Abstract

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.