The possibility of predicting Quality of Service, and particularly data rates, in mobile networks will enable new applications for future automated and connected mobility, such as teleoperated driving. Since network data is difficult to acquire and usually of low granularity, robust prediction approaches are required that need to be trained with data sets and measurements generated by end devices. In this paper, we present uplink and downlink data sets, measured in extensive drive tests, that are made available for the evaluation of machine learning methods. Based on this data, we compare the data rate prediction performance in uplink and downlink for neural network, random forest and gradient boosting approaches. Our results show a significantly higher achievable accuracy in uplink than in downlink, and that, even with reduced feature sets, gradient boosting is particularly suited for the prediction task. Moreover, we investigate the use of quantile estimation methods for predicting bounds on the achievable data rate. We show that conformalized approaches, both based on neural networks and random forests, can predict quantiles with very high accuracy.

Many wearable applications utilize wireless communication to provide the user with comfort features. With a typically rather small consumer market using technologies in the ISM band is beneficial in order to be cost effective. Safety relevant wearable applications, such as a wireless airbag jacket, face the challenge of providing real-time communication in noisy and volatile environments. In this evaluation we analyze different Wireless Body Area Network (WBAN) technologies with respect to their performance for real-time applications in mobile scenarios. The well established approach of using frequency diversity for increased reliability in noisy environments is evaluated for mobile scenarios.

Selecting the best path in multi-path heterogeneous networks is challenging. Multi-path TCP uses by default a scheduler that selects the path with the minimum round trip time (minRTT). A well-known problem is head-of-line blocking at the receiver when packets arrive out of order on different paths. We shed light on another issue that occurs if scheduling have to deal with deep queues in the network. First, we highlight the relevance by a real-world experiment in cellular networks that often deploy deep queues. Second, we elaborate on the issues with minRTT scheduling and deep queues in a simplified network to illustrate the root causes; namely the interaction of the minRTT scheduler and loss-based congestion control that causes extensive bufferbloat at network elements and distorts RTT measurement. This results in extraordinary large buffer sizes for full utilization. Finally, we discuss mitigation techniques and show how alternative congestion control algorithms mitigate the effect.

In this demo we present IBR-DTN for Android: IBR-DTN is a fully featured RFC5050 compliant Bundle Protocol implementation that can run on un-rooted Android devices starting from Android Version 2.3 (Gingerbread). IBR-DTN for Android supports all features of the IBR-DTN version for PCs and embedded systems. It is available in the Google Play Store for free.