Modeling spatial node density in waypoint mobility

Astuto, Bruno

doi:10.1109/mass.2012.6502547

Cited by 4 publications

(1 citation statement)

References 25 publications

(30 reference statements)

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“…These approaches seem to fail to meet the traffic load estimation requirement of our work, as it is not possible to extrapolate an estimator from the proposed models. In the same context of modeling the spatial cell density of a mobile network, authors in [15] propose a stochastic model to compute the probability of staying in a given location for a given period of time as well as the probability of moving from a given location to another one, using a random waypoint-based mobility pattern. Also, relevant works targeting mobility pattern detection from real cellular network data have been studied e.g., [16] [17] [18].…”

Section: Related Workmentioning

confidence: 99%

Crowded spot estimator for urban cellular networks

Hoteit

Secci²,

Premoli

2017

Ann. Telecommun.

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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 realtime 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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Section: Related Workmentioning

confidence: 99%