Publications

Video streaming has become an indispensable technology in people’s lives, while its usage keeps constantly increasing. The variability, instability and unpredictability of network conditions pose one of the biggest challenges to video streaming. In this chapter, we analyze HTTP Adaptive Streaming, a technology that relieves these issues by adapting the video reproduction to the current network conditions. Particularly, we study how context awareness can be combined with the adaptive streaming logic to design a proactive client-based video streaming strategy. Our results show that such a context-aware strategy manages to successfully mitigate stallings in light of network connectivity problems, such as an outage. Moreover, we analyze the performance of this strategy by comparing it to the optimal case, as well as by considering situations where the awareness of the context lacks reliability.

Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access network solutions. One major challenge in current LoRaWAN planning is message collision due to the random channel access. To study the collision behavior in LoRaWAN, we present LoRaPlan in this demonstration. LoRaPlan is a software to evaluate gateway placement decisions by studying the collision probability. Therefore, sensor and gateway locations can be imported to create a LoRaWAN. Furthermore, existing networks can be extended by additional gateways. Based on this setup, network coverage, network quality with regard to the number of sensors a single gateway has to manage, and also transmission quality and collision probabilities can be studied. In this demonstration, first the process of network creation with gateway and sensor import and additional manual gateway placement in LoRaPlan is presented. Different adjustable parameters are shown and the influence on network coverage and collision probability is discussed. At the end of this demonstration, it is possible for the audience to place their own gateways and evaluate own placements by means of collision probability and the coverage.

To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.

Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.

Many exciting applications are currently emerging due to the use of technology from the Internet of Things (IoT) in Smart Cities. In recent years, Long Range Wide Area Networks (LoRaWANs) have become one of the main technologies in this field due to long distance coverage, low power consumption sensors, and economic reasons. So far, LoRaWANs have been planned and deployed mainly with the aim of minimizing the number of gateways, while still providing coverage in a geographic area. Packet collisions and future traffic have not been considered. In contrast, we present a gateway placement strategy that is robust against an increase in the number of sensors and in the network load. To this end, we modify an existing local search algorithm for a geometric set cover problem such that it takes the capacities of gateways into account. If we set these capacities below the maximum, the network can accommodate additional sensors in the future. Moreover, our algorithm can extend the initial placement once the capacities are exhausted.

The adaptation of Internet of Things in our everyday life shows different new demands and challenges. One of the fastest growing technologies in this context are Low Power Wide Area Networks with LoRaWAN as one of their most prominent representatives. The promise of this technology is to transmit sensor data over large distances with very little energy consumption. But transmission behavior, and especially overhead and collision probability, must be studied in detail to improve transmission quality and energy consumption due to the random access nature of LoRaWAN. Because of that, this work investigates the LoRaWAN channel capacity by analyzing the transmission overhead, the collision probability, and the data loss. At the end, an energy consumption comparison is done. The contribution is a novel approach based on aggregation and retransmission that shows a decreased data loss of up to 20% compared to the currently used random channel access.

The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements.