## 3 Related Work on the Usage of CNMs  
In recent years, CNMs became increasingly relevant in research and practice, as they enable the fast and relatively cheap collection of information on network and application level. Fundamentals on CNMs are specified in [1]. In their white paper, the authors provided definitions of the terms crowdsourced network and QoE measurements, defined use cases for CNMs, and discussed challenges. The three main use cases the authors mention are network planning, network monitoring, and benchmarking. Thus, the following related work is grouped into these three categories. In addition, research which focuses on challenges of CNMs are discussed. One area of application of CNMs is network planning, which includes, for example, the creation of coverage maps for mobile networks. In [10], the authors analyzed different estimation approaches for base station positions using crowdsourced data. They found that a grid-based approach provides the best estimates when compared with that of their real locations. Another approach to estimate base station localization using CNMs was done by [11]. Here, the authors evaluated the applicability of crowdsourced cellular signal measurements in this context and showed that feature clustering leads to good results. For internet service providers (ISPs), network monitoring is essential. With the help of CNMs, they are able to monitor network quality from a user perspective. This can, for example, be done by collecting information during the use of specific smartphone applications. Here, different KPIs can be measured on several layers, from context parameters such as cultural background through network parameters such as signal strength up to application parameters including number of stalling and user-focused parameters, such as browser session time. Especially video streaming applications are well-used options to collect crowd data on the smartphone of the end-users. For example, in [12], the authors designed a smartphone application to analyzing the QoE of YouTube HTTP Adaptive Streaming in mobile networks. Another approach was performed by the authors of [13]. Here, an active measurement framework to collect video streaming KPIs was designed to monitor the quality of mobile networks in Europe [14]. A statistical report of the mobile internet experience for Germany based on CNMs data report can be found in [15]. In addition to some scientific work in this field, the number of commercial CNM service providers increased in the last few years. Examples of such providers are Tutela, Ookla, Umlaut, QoSi, Opensignal, and Rohde & Schwarz, which all use the smartphones of the end users as measurement devices. These companies regularly publish reports on the mobile network experience, for example [16; 17; 18; 19]. In these reports, a comprehensive evaluation of the current state of the mobile networks is given. Furthermore, they compare network operators, coverage, and speed of their networks. Another use case of CNMs is benchmarking, and thus, to measure and compare different ISPs. As previously mentioned, commercial CNMs service providers regularly publish reports that can also include the comparison of the network quality of different ISPs. In research, other benchmarking approaches are presented. For example, in [20], the authors used crowd data collected from peer-to-peer BitTorrent users to compare the performance of ISPs from end-user perspective. Using transfer rates as well as network and geographic location information, they showed that this approach is a feasible way to characterize the service that subscribers can expect from a particular ISP. Another model for evaluating the performance of different ISPs using CNMs was presented by [21]. In their work, they introduce a model which characterizes throughput as a function of signal power. In addition to a wide range of applications, however, CNMs also involve a number of challenges.