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Introducing Cellular Network Layer into SUMO for Simulating Vehicular Mobile Devices' Interactions in Urban Environment.

, , and . VEHITS, page 582-589. SciTePress, (2018)

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Adaptive Edge Process Migration for IoT in Heterogeneous Cloud-Fog-Edge Computing Environment., , and . CoRR, (2018)Mobile Big Data: Foundations, State of the Art, and Future Directions., , , and . Encyclopedia of Big Data Technologies, Springer, (2019)Mobility Episode Discovery in the Mobile Networks Based on Enhanced Switching Kalman Filter., , , and . ICUMT, page 1-7. IEEE, (2018)Practical testing application of travel time estimation using applied Monte Carlo Method and adaptive estimation from probes., , and . Intelligent Vehicles Symposium, page 1078-1083. IEEE, (2012)Exploring a New Model for Mobile Positioning Based on CDR Data of The Cellular Networks., and . CoRR, (2019)An application of the Sequential Monte Carlo to increase the accuracy of travel time estimation in urban areas., , , and . ITSC, page 157-162. IEEE, (2011)Introducing Cellular Network Layer into SUMO for Simulating Vehicular Mobile Devices' Interactions in Urban Environment., , and . VEHITS, page 582-589. SciTePress, (2018)Cell phone subscribers mobility prediction using enhanced Markov Chain algorithm., , , , and . Intelligent Vehicles Symposium, page 1049-1054. IEEE, (2014)Travel Time Estimation Using Sparsely Sampled Probe GPS Data in Urban Road Networks Context. (Estimation des temps de parcours fondée sur l'utilisation des données éparses de véhicules traceurs dans un contexte urbain).. Institut national des sciences appliquées de Rouen, France, (2013)Mobility episode detection from CDR's data using switching Kalman filter., , , and . MobiGIS, page 63-69. ACM, (2015)