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Crowded spot estimator for urban cellular networks

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Abstract

The real-time detection of crowded spots in access networks is considered nowadays a necessary step in the evolution of mobile cellular networks as it can be of great benefit for many use-cases. On the one hand, a dynamic positioning of contents and computing resources in the most crowded regions can lower connection latency and data loss and can allow us to have a seamless service provided for the users, without performance degradation across the network. On the other hand, a dynamic resource allocation among access points taking into account their loads can enhance the user’s quality of service and indeed network performances. In this context, using real mobile data traces from a cellular network operator in France, provided us with a temporal and spatial analysis of user content consumption habits in different French Metropolitan areas (Paris, Lyon and Nice). Furthermore, we put to use a real-time crowded spot estimator computed using two user mobility metrics, using a linear regression approach. Evaluating our estimator against more than one million user databases from a major French network operator, it appears to be an excellent crowd detection solution of cellular and backhauling network management. We show that its error count definitely decreases with the cell load, and it becomes very small for reasonable crowded spot load reaching upper thresholds. We also show that our crowded spot estimator is time and city-independent as it shows a stable behavior for different times of the day and for different cities with different topographies. Furthermore, compared to another crowded spot estimator from the literature, we show that our proposed estimator offers more suitable and accurate results in terms of crowded spot estimation for the three selected areas.

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Notes

  1. We note that in order to compute precisely the centroid and the radius of gyration of the users, we need sufficient location information of each one of them. This is not an issue nowadays as most of users are smartphones holders with persistent Internet connectivity (i.e., a lot of samplings can be obtained for each one of them).

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Acknowledgements

The authors would like to thank Dr. Cezary Ziemlicki and Dr. Zbigniew Smoreda from Orange Labs for providing the data used for the experiments and Prof. Guy Pujolle from LIP6 for his useful comments. This work was partially supported by the ANR ABCD project (Grant No: ANR-13-INFR-005), and by the EU FP7 IRSES MobileCloud Project (Grant No. 612212).

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Authors and Affiliations

  1. Ecole d’ingénieurs du numérique ISEP, 10 rue de Vanves, 92130, Issy les Moulineaux, France

    Sahar Hoteit

  2. Sorbonne Universities, UPMC Univ. Paris 06, UMR 7606, LIP6, 75005, Paris, France

    Stefano Secci

  3. Università di Milano, Milan, Italy

    Marco Premoli

Authors
  1. Sahar Hoteit
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  2. Stefano Secci
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  3. Marco Premoli
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Correspondence to Sahar Hoteit.

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Hoteit, S., Secci, S. & Premoli, M. Crowded spot estimator for urban cellular networks. Ann. Telecommun. 72, 743–754 (2017). https://doi.org/10.1007/s12243-017-0591-6

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  • DOI: https://doi.org/10.1007/s12243-017-0591-6

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