In this paper we advocate the use of mobile networks as sensing platforms to monitor metropolitan areas. In particular, we are interested in detecting urban anomalies (e.g., crowd gathering) by processing the control information exchanged among the base stations and the mobile users. For this, we design an anomaly detection framework based on semi-supervised learning, which enables the automatic identification of different types of anomalous events without any a-priori information. The proposed approach uses unsupervised learning techniques to gain confidence in real mobile traffic demand patterns from the city of Madrid in Spain and build an ad-hoc ground truth. A recurrent neural network is then trained to detect contextual anomalies and identify different types of urban events. Simulation results confirm the better performance of the semi-supervised method compared to pure unsupervised anomaly detection frameworks.

A Semi-supervised Method to Identify Urban Anomalies through LTE PDCCH Fingerprinting / Pelati, A.; Meo, M.; Dini, P.. - ELETTRONICO. - (2021), pp. 1-6. ((Intervento presentato al convegno 2021 IEEE International Conference on Communications, ICC 2021 tenutosi a Montreal, QC, Canada nel 14-23 June 2021 [10.1109/ICC42927.2021.9500470].

A Semi-supervised Method to Identify Urban Anomalies through LTE PDCCH Fingerprinting

Pelati A.;Meo M.;
2021

Abstract

In this paper we advocate the use of mobile networks as sensing platforms to monitor metropolitan areas. In particular, we are interested in detecting urban anomalies (e.g., crowd gathering) by processing the control information exchanged among the base stations and the mobile users. For this, we design an anomaly detection framework based on semi-supervised learning, which enables the automatic identification of different types of anomalous events without any a-priori information. The proposed approach uses unsupervised learning techniques to gain confidence in real mobile traffic demand patterns from the city of Madrid in Spain and build an ad-hoc ground truth. A recurrent neural network is then trained to detect contextual anomalies and identify different types of urban events. Simulation results confirm the better performance of the semi-supervised method compared to pure unsupervised anomaly detection frameworks.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2962772