Global mobile connectivity depends on international roaming, which enables subscribers to use their service abroad through agreements ensuring traffic exchange between home and visited networks. Poor interconnect performance over the IPX network can degrade user experience and cause revenue loss, yet roaming remains underexplored due to proprietary systems and limited transparency. We present a large-scale study based on nine months of active probing from gateways across seven global regions, yielding nearly four billion latency measurements spanning 173 countries and 399 operators. This dataset enables the analysis of temporal trends, regional disparities, anomalies, and routing inefficiencies in IPX-based roaming. Our findings show how interconnection choices, carrier selection, and routing detours impact performance, advancing our understanding of global roaming trade-offs and allowing future optimization.

Mobile Instant Messaging (MIM) applications such as WhatsApp transformed human communication by enabling global exchange of various message types, such as text, image, video, or voice, at any time. Network providers are facing a substantial user base and network load which is especially high in group chats where each message needs to be distributed to each member. Due to end-to-end encryption, network operators must obtain knowledge about the communication and the resulting load on the network by other means, which makes it necessary to model the network traffic of MIM. In this work, we therefore present an approach to source traffic modeling for MIM. After identifying the building blocks of a Source Traffic Model (STM) for MIM, we address existing gaps through studies on MIM communication networks, contact proximity, media compression and payload size, as well as media file size distribution. Combining existing literature and our work, we present and implement a modular STM approach which can be used for developing STMs for MIM. Using an exemplary STM, we evaluate the daily network traffic per user. With this, we provide a comprehensive description of MIM in the network researching context and enable consideration of MIM in future network design.

Video-based communication is central to today’s digital society. While YouTube and Netflix once dominated video traffic, traditional broadcasters now run their own streaming services, and live-streaming platforms are expanding. Users expect high-resolution video with minimal buffering and latency, especially for live content. Meeting these demands requires infrastructure that balances performance, cost, energy, and resources, supported by comprehensive traffic monitoring and analysis. Artificial Intelligence plays a key role, particularly in encrypted traffic classification and quality prediction, with treebased Machine Learning models like Random Forests (RF) widely used. However, research often emphasizes small accuracy gains while overlooking the raising energy and resource costs of such models, an increasing conflict with sustainability goals. To study this trade-off, we implement RF models with varying feature counts and complexities to predict video re-buffering and buffer health, two key quality indicators. Using a dataset of more than 11,000 YouTube sessions, we analyze how prediction performance scales with model complexity and energy consumption across the full pipeline, revealing several unexpected results.

A key focus of 6G development is ensuring sustainable operation of Information and Communication Technology systems, with the major goal of achieving Net-Zero carbon emissions. Although vendors promote the energy efficiency of their latest products, overall energy consumption in the sector continues to rise due to the increasing number of devices and data traffic. The absence of a standardized energy efficiency metric further complicates meaningful comparisons. Energy intensity-defined as the energy required to process or transmit a bit or byte of data-has recently gained attention as a potential metric. However, a comprehensive analysis of energy intensity across the diverse scenarios, technologies, and devices in the Internet of Things (IoT) ecosystem for 6G networks remains unexplored. This study addresses this gap by evaluating energy intensity as a key metric in a 6G IoT context, comparing measured and simulated access network technologies. It also identifies the limitations of energy intensity and proposes alternative metrics for assessing energy efficiency.

The ongoing and evolving use of communication networks presents two critical challenges for current and future networks that require special attention: (1) managing the enormous and continuously growing data traffic efficiently, and (2) handling the large number of end devices resulting from the advent of the Internet of Things in the control and data plane. In addition to these challenges, there is an urgent need to reduce energy consumption, make more efficient use of resources, and optimize processes without compromising service quality. We are addressing these efforts comprehensively by focusing on the monitoring and quality assessment of streaming applications, which significantly contribute to global Internet traffic, and conduct a general performance analysis for a Long Range Wide Area Network, one of the fastest-growing Low Power Wide Area Network solutions.

High-quality communication technology is essential in our globalized, digital world. However, network issues and poor mobile connectivity still impact service quality and user experience across various applications. The roll-out of 5G networks promised improvements in network quality including lower latency and better throughput. But questions whether these improvements have materialized, about regional differences in mobile network quality, and whether the current network infrastructure is prepared to support today's, currently emerging, and future applications in a high quality remain. To address these questions, we analyzed over 225 million network quality measurements from 2022 and 2023 provided by the company OpenSignal to assess mobile network quality as perceived by an end user along all roadways in the German state Bavaria. By examining network generations, throughput, and latency metrics, we provide valuable insights for network planning, helping both engineers and the public in understanding the current state of mobile connectivity on Germany's roads.

Global connectivity demands extensive cellular network coverage, enabling instant data transmission to users worldwide. The growing need for faster, low-latency networks drives the global 5G rollout, aiming to cover one-third of the population by 2025. While 5G promises enhanced Quality of Service, network providers must balance customer expectations with political pressure and the costs of expansion. However, it remains unclear whether current 5G deployments adequately address connectivity challenges and deliver consistently high Quality of Experience. To investigate this, we analyze mobile network quality across Bavaria using over 225 million measurements from Opensignal. By correlating key performance metrics like throughput, latency, jitter, and packet loss with network generation and provider, we evaluate technology and service impacts on network quality. These findings are mapped to Bavaria's population to explore urbanization effects on performance and identify potential digital divides between urban and rural areas.

Video conferencing has become indispensable in both professional and social contexts. From a networking point of view, timely distribution of media to all clients participating in a video conference is required. However, fluctuating network conditions pose a challenge, so that a high Quality of Experience (QoE) might not always be achieved. When assessing how network conditions impact the QoE of video conferencing, previous QoE studies focused on one-to-one settings and suffered from small numbers of participants due to their costly laboratory setups. In this work, we investigate crowdsourcing-based QoE studies for video conferencing in order to obtain a higher number and more globally distributed and diverse participants. For this, we consider multi-party video conferencing and design a web-based study framework which provides a high realism to online study participants by involving themselves in the presented video call. Finally, we conduct a QoE study with our framework to demonstrate the suitability of crowdsourcing for assessing the QoE impact of network-level and application-level degradations of video conferencing applications.

Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.

Continuous advancements in smart solutions are consistently making their way into the market. To address the growing demand for connectivity across the globe, the deployment of large-scale cellular networks is a viable option. However, in many cases, basic monitoring applications or devices do not require high bandwidth or extremely low latency. An excellent alternative to provide coverage to such end devices is LoRaWAN, which significantly increased in popularity in recent years. Yet, LoRaWAN employs a random channel access method for message transmissions, which can lead to sensor interference, message collisions, and, in the worst-case, message loss. Consequently, researchers study alternatives to random channel access, sometimes overlooking LoRaWAN regulations. In this study, we close this gap in literature by conducting a theoretical investigation of a time scheduled channel access approach for LoRaWAN. This approach remains compliant with network regulations for various adjustable network and message parameters. Furthermore, we extend the simple time scheduled approach by introducing variable slot lengths, thereby increasing flexibility and highlighting its potential benefits.

The growing popularity of LoRa-based solutions, such as weather, environmental, and climate monitoring, has resulted in larger LoRaWAN deployments and increased network load. However, the random channel access method used in LoRaWAN leads to higher message collision probabilities and potential data loss, diminishing the networks energy efficiency. This paper addresses this issue by proposing a hardware-independent model that assesses energy consumption and efficiency in different LoRaWAN channel access approaches. Through simulation and analysis of real energy values, we provide insights and recommendations for optimizing the network performance and energy usage. This study fills a research gap and contributes to the development of more robust and energy-efficient LoRaWAN systems for various application areas.

Video streaming generates most network traffic in today’s Internet. For that reason, video on demand streaming is researched heavily in recent years, and many traffic monitoring mechanisms, flow and stream models, and models to predict the user perceived quality are well established. However, the quickly growing live streaming sector is not considered in these Quality of Experience models and not even the relation between network traffic and playback quality has been studied so far, forming a gap in literature. For that reason, we investigate Twitch.tv streaming as one of the largest live streaming platforms based on a large dataset and investigate the possibility to predict live streaming quality based on uplink request information. We apply approaches that are well studied for on demand streaming to predict quality changes, playback quality, and video interruption events as the most important metrics impairing user perceived quality. In this context, we answer whether these models are suitable for live streaming, if small changes are sufficient for satisfactory prediction results, or if fundamental changes and new models are required.

Today, video streaming is responsible for about 50 % of all Internet traffic worldwide. To cope with this massive amount of video streaming data, a major concern of network providers is the development of efficient traffic monitoring and management techniques. However, fast and efficient monitoring which leads to intelligent management decisions is becoming highly resource intense and complex, due to the steady increase of the number of streamed videos and the quality of the streamed content. Considering HTTP adaptive streaming applications, we present a simple machine learning free, uplink request based approach to estimate drops in the video playback butter. These drops are the first indicator leading to quality impairment events like downwards quality changes or stalling. With our approach, instead of analyzing thousands of encrypted packets in the network, we only need to consider one single packet every 5 s-10s on average, depending on the video chunk size and independently of the played resolution. Nevertheless, we are able to detect nearly all stalling events or consider a trade-off between stalling detection recall and false positives. Our approach can be implemented completely moving average based, thus not requiring any parameter setup or other expert knowledge. Due to its simplicity, it can be deployed on any access point to collect streaming quality information that is useful for active network management and intelligent resource provisioning but also in a data center to analyze a massive number of parallel video flows.

The recent rise of the Internet of Things (IoT) has given way to numerous challenges and research questions. One of the most critical issues in the area of low powered devices is the question of energy efficiency. Here, technologies like LoRa or Zigbee emerged, promising low power consumption while maintaining adequate performance. However, even when using these tailor made technologies, several configuration aspects need to be taken into account to provide high performance, energy efficient operation. To this end, we propose a generic model to compute the energy efficiency of wireless sensors under the assumption of perfect CSMA/CA channel access. We present numerical results for a typical LoRa device and highlight extensions towards other channel access mechanisms. Finally, we apply Kleinrock’s power metric to obtain ideal system configurations for varying load parameters.

Future Internet of Things deployments generate a multitude of new challenges and opportunities. While 5G networks promise massive throughput rates with ultra low delays, many verticals in the areas of Industry 4.0, Smart City, or Smart Agriculture only transmit tiny amounts of data and instead demand high energy efficiency. This is addressed by access technologies like Low Power Wide Area Networks being complementary to high performance 5G networks. Especially Long Range Wide Area Network (LoRaWAN) promises transmissions across long distances with low energy requirements with the drawback of unreliable transmission due to potential message collisions through random channel access. For that reason, a broad parameter study simulation for LoRaWAN channel access with slotted Aloha is presented for real world scenarios and influences like clock drifts or cross traffic. The key impact factors of this channel access approach are studied with focus on the collision probability in various scenarios and simulation results are compared to the current state of the art. The contribution of this work are guidelines for parameter settings in a LoRaWAN with slotted Aloha channel access and performance comparisons for different settings. This information is crucial to scale and operate future LoRaWANs.

The development and adoption speed of new Internet of Things (IoT) devices and applications is constantly increasing. In particular in mobile networks, however, constant connectivity for all newly developed, globally spread, and occasionally geographically mobile devices is challenging. While the main business of current network operators is in particular, not to roam these new device generation between different networks, whereas new so called Mobile Virtual Network Operators (MVNOs) arise with exactly this business case. They use already available Radio Access Networks and deploy SIM cards that are able to establish data connection everywhere on the basis of international roaming agreements. However, because of the complex interconnection of different systems, additional challenges arise. Outages in one network component might impact the whole system.In this work, we study these signaling anomalies called incidents based on a dataset received from a global MVNO. The goal of this work is to determine traffic characteristics during these incidents towards a better understanding of signaling traffic in IoT networks. Thus, this work can be seen as a fundamental basis to help operators in a characterization, and in particular prediction and avoidance of signaling incidents.

LoRaWAN is one of the most promising Internet of Things technologies with regard to low energy consumption. However, the currently used random channel access has much potential for improvement. Thus, current literature studies alter-native channel access approaches, but the energy consumption is often not taken into consideration. For that reason, we present a generic model to quantify energy consumption in LoRaWAN for different channel access mechanisms based on a state machine. With our model, we can describe the energy consumption for specific access mechanisms or for the complete network. Our model shows that random access only performs best if no additional receive windows are opened. A simple improvement is Listen before Talk. For networks with high load, improvements are achieved by a more complex scheduled MAC. Our model serves as a basis for future energy consumption studies, conducted through measurements or simulations.

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.

Continuously monitoring the network activity to proactively recognise possible problems and prevent users QoE degradation is a major concern for network operators, for both mobile radio and home networks. Considering video streaming applications, which generate the majority of overall Internet traffic, monitoring the chunk requests from the video client to the video server is of particular interest, as they not only indicate that a download burst is imminent, but their type (e.g., request of an audio or video chunk) and frequency also allow to estimate which and how much data will be downloaded to the client. In this work, we propose a machine-learning based video streaming traffic monitoring architecture able to i) predict when next uplink request will be issued by the video client and ii) classify the type of next uplink request. We evaluate the system performance on a dataset of more than 900 HTTP adaptive streaming sessions and 15,000 request-response exchanges, where both the predictor of the next request arrival and the request type classifier are fed with lightweight features extracted from encrypted traffic in an online fashion, both in the uplink and downlink directions of the traffic. Results show that i) the system is able to classify the type of a HAS uplink requests with an accuracy greater than 95 % and ii) pipe-lining request type classification and prediction of next request arrival time improves the final prediction performance.

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.

Today’s traffic projections speak of almost 58% video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50% encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.

In recent years, an increasing number of Internet-based applications have been released that use virtual reality for education, training, gaming, and various forms of entertainment. When transmitting an omnidirectional 360° video over the Inter-net, it consumes considerably more data compared to traditional video streaming. To overcome the high network requirements and still reach a high Quality of Experience, HTTP adaptive streaming technology is considered for 360° video streaming. In contrast to traditional streaming, the adaptation logic considers not only the current network conditions, but also the viewport of the user, i.e., which part of the 360° sphere the user is currently focusing on. However, as the viewport of the user might change anytime, the adaptation logic is required to accurately predict the viewport in the next seconds of the playback to allow for an efficient and smooth streaming with a high visual quality.In this paper, we present novel linear programs that determine the optimal visual quality, which is reachable in a given network scenario using different approaches for viewport prediction. Our results are an important contribution for designing adaptation logics for 360° video streaming, which allow for efficient data transmission in the network while reaching a high QoE in VR applications.

Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available at [8].

This demo introduces a wrapper used for automated measurements of mobile video streaming in the Android YouTube app. The difference to traditional measurement techniques is that the measurement is done with the native YouTube app as it is provided in the Google Play Store. In addition to bandwidth or packet loss detection, the QoE of the video stream can be measured and quantified. For this, the amount of quality changes, the current playtime, the buffer level, and statistics like video and audio format are captured. Thus, detailed relationships between network parameters and streaming behavior based on many factors can be detected within the native app available in the Play Store.

In this demonstration we will show the prospects of dynamic cloud service placement. A cloud service is implemented that manages real-time video streaming and real-time control commands for a remote controlled drone. The requirements for this cloud service are groundbreaking because the image transfer and the control commands should be transmitted in real time that the drone can be controlled smoothly by a user. The goal is to improve user QoE for streaming services and real-time control by cloud service migrations, respecting the concept of dynamic Edge Computing by means of moving computing and monitoring applications on demand close to the user and the end device.