In TCEs (Temporary Crowded Events), for example, music festivals, users are faced with problems accessing the Internet. TCEs are limited time events with a high concentration of people moving within the event enclosure while accessing the Internet. Unlike other events where the user locations are constant and known at the start (e.g. stadiums), the traffic generation and the user movement in TCEs is variable and influenced by the dynamics of the event. The movement of users can lead to overloads in APs (Access Point) in case they are fixed. In order to minimize this phenomenon, new techniques have been explored that resort to the adjustable positioning of APs integrated into UAVs (Unmanned Aerial Vehicles). In these scenarios, the dynamic of the location of the APs requires that tools of prediction of the users movements and, in turn, of the sources of traffic, gain particular expression when being related to the algorithms of positioning of the referred APs. In order to allow the development and analysis of new network planning solutions for TCEs, it is necessary to recreate these scenarios in simulation, which, in turn, requires a detailed characterization of this kind of events. This article aims to characterize and model the mobility and traffic generated by users in TCEs. This characterization will enable the development of new statistical models of traffic generation and user mobility in TCEs.
The articles @cite_1 and @cite_8 are close to the desired solution, since in both cases there is a traffic characterization and in the second case there is a traffic prediction model. However, in these cases the APs are fixed, which, in case of TCE events, is disadvantageous compared to the use of UAVs. This is due to the fact that there may be obstacles in the event venue and interference with the crowd's field of vision. These studies are also distant from the intended since they deal with cases of cellular networks, and, in our case, a Wi-Fi based network solution is desired.
@cite_1 @cite_8
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Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis. Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.