## 6 Discussion  
The system realized optimized data transfers over multi-access networking technologies, which should enable better performance (lower latency/higher throughput) at the control center regarding monitored vehicles. The system should enable development, execution, and evaluation of real applications in an emulation environment, which is more difficult in simulations (e.g., NS-2/NS-3 [3]). Development of ML-based solutions in an emulation environment lowers the cost of early experimentation. Existing research on multipath routing can be compared to our proposed architecture. Multipath routing for QUIC is optimized with reinforcement learning in an emulation environment (NetEm, Mininet) [8], whereas we developed a custom emulation environment. They used session related parameters (SRTT, congestion window, and bytes in flight) for prediction of throughput, but we focused on prediction of OWD. Multipath TCP decision making was optimized with random forest trees in 4G/Wi-Fi networks with mobile devices [9]. Particularly, access point-related parameters (signal strength from the associated Wi-Fi AP, data rate, TCP throughput, gateway RTT, end-to-end RTT and interference degree) were used in the training and prediction of path quality. We also used signal strength and signal-to-noise ratio (interference degree in [9]) in model training, but instead used OWD for estimation of path quality. Additionally, their performance measurements focused on throughput improvement, whereas we focused on OWD. A recent proposal on the development of ML-based models and applications with emulators focuses on RAN scheduling and slicing [10]. Instead, our focus was to enable ML-based decision making for moving vehicles. They experimented with online reinforcement learning and learned that incremental online learning enabled better performance (vs. a model trained offline) by adapting to the changes of the environment. This evidence suggests that incremental online learning should also be integrated to our emulation environment for supporting decision making in the future. We compared offline performance of RandomForestRegressor, River (with AdaptiveRandomForestRegressor) and multi-layer perceptron (MLP) algorithms (Table 1). We chose random forest-based algorithms for experimentation, because of their utilization and performance in an earlier study [8], and suitability for our regression problem (OWD prediction). However, in the future, deeper neural network models (besides MLP) may also be experimented with, which may require utilization of other (besides sklearn) ML libraries (e.g., Tensorflow). Another possible ML approach to be experimented with in the future might be swarm intelligence [17]. Our current approach is to collect measurement data from multiple moving vehicles for training purposes. Thus, the data may also be suited for offline training of models based on swarm intelligence methods. Online distributed training with swarm intelligence would require network connectivity between learning agents, which may become challenging in a mining environment. Additionally, ML aspects of our architecture can be compared to earlier work. A few studies exist [15; 16] on the application of River [13] for solving incremental learning problems. River was used for online analysis of encrypted traffic for identifying malware based on AdaptiveRandomForestClassifier [16]. Additionally, performance of River and Creme was analyzed with logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naive Bayes classification, where several datasets were used for evaluation [15]. In general, River achieved good performance in terms of classification accuracy (except for bananas dataset in [15]). However, neither of the studies experimented River for usage in a real time system (such as our emulator). Offline experiments indicated the potential of River in the research context (Figure 13), although online experiments were left for future work.