# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 2 Related Works  
The research trend in the context of mobile crowdsourcing aims to address practical challenges (e.g., traffic prediction, traffic classification, traffic routing, congestion control, resource management, QoE management, network security, etc.) and meet the needs of the system actors (users, operators, providers). As a result, numerous mobile crowdsourcing campaigns were achieved in order to collect real datasets and to permit the study of a particular challenge for mobile, service providers, or device vendors. In this kind of campaign, different elements have to be taken into account like the measurement tools, the used devices, the considered services, the access technologies and the mobility test modes [11; 12; 13; 14]. In fact, collecting datasets requires a lot of time and resources, in addition to the mobilization of volunteers and/or testers to achieve the tests. The mobility aspects increase the complexity level of crowdsourcing campaigns as the geo-localization of the users or connected devices is an important factor and directly impacts the perceived quality [14; 15; 16]. In [14], the collected mobile dataset by driving a car along a distinct route in the Sydney metropolitan area considering the 3\(G\) (_UMS_, _HSPA_) networks in 2008. The goal is to study the impact of mobility in a vehicular network context. In [15], the authors use three mobility test modes (static, car, high-speed rail) for the \(LTE\) network between two large metropolitan areas in China: Beijing and Tianjin. The objective is to evaluate the network performance, mainly the handover process, in high mobility (300 km/h or above). In [13], the physical indicators for \(LTE\) and non-\(LTE\) technologies are considered. The collected data concern two network providers in three countries (U.S., Germany, Netherlands). Indeed, many mobility patterns are tested including sitting/standing, walking, biking (fast), car, bus, trains and planes. The goal of this study consists of statistical analysis of the impact of mobility speed on \(LTE\) performance. In [17], the authors publish a mobile dataset for \(LTE\) and \(HSPA\) technologies taken around Covenant University in Nigeria. In this study, the indoor pattern is evaluated for two months between June and July 2020. All the measures were taken from 7:30 a.m. to 11:00 p.m. The goal is to investigate the performance of local operators networks. This study is one of the first studies that concerns cellular technologies in Africa. The authors in [2] used a _Keysight_\(software\) tool to collect the mobile dataset at the Institute of Telecommunications, TU Wien, Vienna, Austria. The authors choose a static indoor pattern to analyze the effect of, on one hand, the short-term fluctuations of the measured key parameter indicators, and on the other hand, time-of-day effects on 4\(G\) networks. Another goal is to train a traffic throughput predictor (by machine learning) in a dense urban environment. The work [11] presents a large dataset, belonging to the company \(Tutela\), which contains more than 2.5 million crowdsourced network measurements, collected in Germany between January and July 2019. Compared to the other presented datasets, _upload_, _download_ performance and _latency_ are evaluated. According to the authors, the measured values differ between individual measurements and the mean value for an area. This is why it can be helpful to study in depth the individual measurements and not just take into account the performance of a global area. In [12], the authors use the \"G-NetTrack Pro\" tool in different mobility patterns to provide a \(4G\) dataset for addressing two use cases. The first one is \(HAS\) algorithm evaluation. They compared different optimization algorithms of chunk selection. The second one is the handover analysis. Using the same tool, in [18], the authors produce a \(5G\) dataset, collected from a major Irish mobile operator, and a synthetic \(5G\) large scale multi-cell NS-3 simulation framework. Their goal is to study \(5G\) deployment.