## 2 Related Work  
While the support of novel technologies, such as the SDR and GPP platforms mentioned above, would make vUE probes possible, the current literature has not yet explored this topic in depth. However, plenty of previous works on the general concept of network monitoring are associated with the present work. In this way, the use of UE traces is a resource used very frequently, and numerous sources use it in network management literature. However, no systematization of its collection is yet specified, and multiple purposes in different campaigns are explained. This fact confirms the assumption that regularly obtained user data are valuable inputs. The following summarized articles are several examples of its many applications. The work in [10] presents the design of a new measurement tool for Android capable of working with multiple radio technologies: 3G, WiFi (wireless fidelity) and LTE (long term evolution). The results analyzed include the round trip time (RTT), power consumption, throughput and delay under different circumstances and taking into account the different UE states. The paper [11] also used monitoring applications at the UEs for quantifying network quality parameters, such as data rate, web page loading speed, voice quality and network coverage. However, no further assessment of the gathered data was provided. Similarly, the paper [12] focused on the comparison of the accuracy of UEs and sniffers for measuring the data rates of LTE and WiFi networks in burst or isolated transmission conditions. Based on these results, the work proposed an environmental model with some software alternatives for providing these measurement capabilities on mobile phones and base stations. Likewise, the primary purpose in [13] was to improve the visibility and understanding of the complex mobile network performance. Differences across carriers, access technologies, geographic regions and over time were assessed. The associated details of mobile networks were very heterogeneous, making it challenging to detect service degradations properly. Additional parameters such as RTT, throughput or DNS resolutions and traceroutes were studied, allowing them to identify reasons behind some persistent problems. Despite the relevant information collected, the need for more monitoring and diagnosis to improve network performance was noted. Other machine learning (ML) techniques were explored to map key performance indicators (KPI) to key quality indicators (KQIs) in [14]. From a campaign of measurements from the user terminal in a live LTE network, predictive modeling of FTP was performed and later extended to a video service [15]. In a similar way [16], an ML system was proposed to analyze the enormous amounts of data generated to monitor network performance. The work is considered preliminary, emphasizing the challenges and importance of latency prediction on operational mobile networks. Besides, as mentioned in the introduction, virtualized probes as a tool for network monitoring and management as a framework are also scarce. In this way, different approaches have been proposed: In [17] the authors described the need to implement probes based on mobile edge computing technology for QoE monitoring without descriptively going into implementation details. Likewise, the contribution [11] describes the use of UE probes that are compiled in a crowdsourced manner. Although it proposes a possible architecture for their collection and uses in network management, its implementation can be difficult and costly and requires the users' collaboration, which is not always possible. The work in [18] introduced ORCA: An \"operator classifier\" for identifying patterns and disclosing exclusive aspects of mobile network operators in noisy crowdsourced datasets. That study was focused on a set of parameters commonly found on such datasets, contrary to the rest of related works that focused only on one or very few parameters.