@techreport{LohGeisslerHossfeld2022,
  type      = {Working Paper},
  author    = {Loh, Frank and Geißler, Stefan and Hoßfeld, Tobias},
  title     = {LoRaWAN Network Planning in Smart Environments: Towards Reliability, Scalability, and Cost Reduction},
  series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  journal   = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)},
  doi       = {10.25972/OPUS-28082},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280829},
  pages     = {4},
  year      = {2022},
  abstract  = {The goal in this work is to present a guidance for LoRaWAN planning to improve overall reliability for message transmissions and scalability. At the end, the cost component is discussed. Therefore, a five step approach is presented that helps to plan a LoRaWAN deployment step by step: Based on the device locations, an initial gateway placement is suggested followed by in-depth frequency and channel access planning. After an initial planning phase, updates for channel access and the initial gateway planning is suggested that should also be done periodically during network operation. Since current gateway placement approaches are only studied with random channel access, there is a lot of potential in the cell planning phase. Furthermore, the performance of different channel access approaches is highly related on network load, and thus cell size and sensor density. Last, the influence of different cell planning ideas on expected costs are discussed.},
  subject      = {Datennetz},
  language  = {en}
}
@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}
}