Identifying abnormal patterns in cellular communication flows

Goergen, David; Mendiratta, Veena; State, Radu; Engel, Thomas

doi:10.1145/2554666.2554671

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Fine-grained temporal classifications have mainly focused on outlying situations [11]. Specifically, both planned [12] and unplanned [13] events were found to induce significant variations in the typical temporal structure of the mobile traffic demand.…”

Section: Related Workmentioning

confidence: 99%

Joint spatial and temporal classification of mobile traffic demands

Fumo

Fiore

Stanica

2017

IEEE INFOCOM 2017 - IEEE Conference on Computer Communications

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Mobile traffic data collected by network operators is a rich source of information about human habits, and its analysis provides insights relevant to many fields, including urbanism, transportation, sociology and networking. In this paper, we present an original approach to infer both spatial and temporal structures hidden in the mobile demand, via a first-time tailoring of Exploratory Factor Analysis (EFA) techniques to the context of mobile traffic datasets. Casting our approach to the time or space dimensions of such datasets allows solving different problems in mobile traffic analysis, i.e., network activity profiling and land use detection, respectively. Tests with real-world mobile traffic datasets show that, in both its variants above, the proposed approach (i) yields results whose quality matches or exceeds that of state-of-the-art solutions, and (ii) provides additional joint spatiotemporal knowledge that is critical to result interpretation.

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Section: Related Workmentioning

confidence: 99%

Joint spatial and temporal classification of mobile traffic demands

Fumo

Fiore

Stanica

2017

IEEE INFOCOM 2017 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

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“…In [13], the authors analyze the 2013 D4D Challenge [14] datasets to discover abnormal patterns in the communication flows of Ivory Coast population over a 5-month period. The proposed approach labels as outliers those time slots with a high number of base stations exhibiting an hourly traffic strongly deviating from its expectation.…”

Section: Related Workmentioning

confidence: 99%

Mobile Demand Profiling for Cellular Cognitive Networking

Furno

Naboulsi

Stanica

et al. 2017

IEEE Trans. on Mobile Comput.

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In the next few years, mobile networks will undergo significant evolutions in order to accommodate the ever-growing load generated by increasingly pervasive smartphones and connected objects. Among those evolutions, cognitive networking upholds a more dynamic management of network resources that adapts to the significant spatiotemporal fluctuations of the mobile demand. Cognitive networking techniques root in the capability of mining large amounts of mobile traffic data collected in the network, so as to understand the current resource utilization in an automated manner. In this paper, we take a first step towards cellular cognitive networks by proposing a framework that analyzes mobile operator data, builds profiles of the typical demand, and identifies unusual situations in network-wide usages. We evaluate our framework on two real-world mobile traffic datasets, and show how it extracts from these a limited number of meaningful mobile demand profiles. In addition, the proposed framework singles out a large number of outlying behaviors in both case studies, which are mapped to social events or technical issues in the network.

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