Call Detail Records to Characterize Usages and Mobility Events of Phone Users

National audienceCellular communications are evolving quickly to constantly adapt and tolerate the load induced by the increasing number of phones. Understanding the traffic is crucial to refine models and improve experiments. In this context, one has to understand the temporal and spatial user behavior at different levels. At the user scale, the usage is not only defined by the amount of calls but also by the user's mobility and type of communication. At a higher level, the BS have a key role on the flow quality. In this paper, we propose a 1-year Call Detail Records (CDR) analysis in Mexico in order to catch on usage turnovers and investigate overlooked parameters such as the call duration. Moreover, we look into handovers (switching from a station to an other one). Our study suggests that user mobility is pretty dependent to user calls

Can Temperature be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature

Call Detail Records to Characterize Usages and Mobility Events of Phone Users

National audienceCellular communications are evolving quickly to constantly adapt and tolerate the load induced by the increasing number of phones. Understanding the traffic is crucial to refine models and improve experiments. In this context, one has to understand the temporal and spatial user behavior at different levels. At the user scale, the usage is not only defined by the amount of calls but also by the user's mobility and type of communication. At a higher level, the BS have a key role on the flow quality. In this paper, we propose a 1-year Call Detail Records (CDR) analysis in Mexico in order to catch on usage turnovers and investigate overlooked parameters such as the call duration. Moreover, we look into handovers (switching from a station to an other one). Our study suggests that user mobility is pretty dependent to user calls

Call Detail Records to Characterize Usages and Mobility Events of Phone Users

National audienceCellular communications are evolving quickly to constantly adapt and tolerate the load induced by the increasing number of phones. Understanding the traffic is crucial to refine models and improve experiments. In this context, one has to understand the temporal and spatial user behavior at different levels. At the user scale, the usage is not only defined by the amount of calls but also by the user's mobility and type of communication. At a higher level, the BS have a key role on the flow quality. In this paper, we propose a 1-year Call Detail Records (CDR) analysis in Mexico in order to catch on usage turnovers and investigate overlooked parameters such as the call duration. Moreover, we look into handovers (switching from a station to an other one). Our study suggests that user mobility is pretty dependent to user calls