@article{LohMehlingHossfeld2022,
  author    = {Loh, Frank and Mehling, Noah and Hoßfeld, Tobias},
  title     = {Towards LoRaWAN without data loss: studying the performance of different channel access approaches},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {2},
  issn      = {1424-8220},
  doi       = {10.3390/s22020691},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-302418},
  year      = {2022},
  abstract  = {The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN.},
  language  = {en}
}
@article{LohWamserPoigneeetal.2022,
  author    = {Loh, Frank and Wamser, Florian and Poign{\´e}e, Fabian and Geißler, Stefan and Hoßfeld, Tobias},
  title     = {YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis},
  series = {Scientific Data},
  volume    = {9},
  journal   = {Scientific Data},
  number    = {1},
  doi       = {10.1038/s41597-022-01418-y},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300240},
  year      = {2022},
  abstract  = {Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.},
  language  = {en}
}
@article{SeufertPoigneeHossfeldetal.2022,
  author    = {Seufert, Anika and Poign{\´e}e, Fabian and Hoßfeld, Tobias and Seufert, Michael},
  title     = {Pandemic in the digital age: analyzing WhatsApp communication behavior before, during, and after the COVID-19 lockdown},
  series = {Humanities and Social Sciences Communications},
  volume    = {9},
  journal   = {Humanities and Social Sciences Communications},
  doi       = {10.1057/s41599-022-01161-0},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300261},
  year      = {2022},
  abstract  = {The strict restrictions introduced by the COVID-19 lockdowns, which started from March 2020, changed people's daily lives and habits on many different levels. In this work, we investigate the impact of the lockdown on the communication behavior in the mobile instant messaging application WhatsApp. Our evaluations are based on a large dataset of 2577 private chat histories with 25,378,093 messages from 51,973 users. The analysis of the one-to-one and group conversations confirms that the lockdown severely altered the communication in WhatsApp chats compared to pre-pandemic time ranges. In particular, we observe short-term effects, which caused an increased message frequency in the first lockdown months and a shifted communication activity during the day in March and April 2020. Moreover, we also see long-term effects of the ongoing pandemic situation until February 2021, which indicate a change of communication behavior towards more regular messaging, as well as a persisting change in activity during the day. The results of our work show that even anonymized chat histories can tell us a lot about people's behavior and especially behavioral changes during the COVID-19 pandemic and thus are of great relevance for behavioral researchers. Furthermore, looking at the pandemic from an Internet provider perspective, these insights can be used during the next pandemic, or if the current COVID-19 situation worsens, to adapt communication networks to the changed usage behavior early on and thus avoid network congestion.},
  language  = {en}
}