Gautam Divgi

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We have analyzed measurement traffic statistics for high-speed wireless Internet access sessions collected in a public nationwide Wi-Fi network. By ranking session lengths and traffic volumes we have found a law implying the truncated Pareto distribution. This finding is analogous to the relationship between Zipf's law and the Pareto distribution. By(More)
— A new probability distribution following from Lavalette's law has been proposed for modeling wireless Internet access session statistics collected in a public nationwide Wi-Fi network. We have derived maximum likelihood estimators for this distribution and found them for three data sets of session duration and traffic volume measurements. Goodness of all(More)
Commercial Wi-Fi hotspot operators often categorize their customers depending on price, time and traffic limits. This paper presents the first comparison of user and traffic characteristics of three different categories of commercial Wi-Fi hotspot users. We examine a five month long trace of user activity and traffic collected by a wireless hotspot service(More)
The current characterization methodology for Wi-Fi hotspot networks analyzes parameters related to the entire subscriber population. It does not consider the impact of the fluctuating number of subscribers. In this paper we develop a methodology to characterize user specific, population independent parameters of a Wi-Fi hotspot network and apply it to data(More)
— A novel unique probability distribution, which has a lognormal body and either light or heavy tail, has been fitted to various empirical data sets of Web file sizes. The optimal parameters of this distribution have been determined by the maximum likelihood estimation combined with the optimization algorithm minimizing a goodness-of–fit metric specially(More)
This paper presents the characterization of a commercial nationwide Wi-Fi hotspot network. We examine a 5 month long log of user activity and traffic volume collected by a wireless network service provider operating hotspots in restaurants, serviced apartments, hotels and airports all over Australia. We categorize users based on their account time limits to(More)
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