@techreport{NguyenLohHossfeld2023,
  type      = {Working Paper},
  author    = {Nguyen, Kien and Loh, Frank and Hoßfeld, Tobias},
  title     = {Challenges of Serverless Deployment in Edge-MEC-Cloud},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32202},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322025},
  pages     = {4},
  year      = {2023},
  abstract  = {The emerging serverless computing may meet Edge Cloud in a beneficial manner as the two offer flexibility and dynamicity in optimizing finite hardware resources. However, the lack of proper study of a joint platform leaves a gap in literature about consumption and performance of such integration. To this end, this paper identifies the key questions and proposes a methodology to answer them.},
  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}
}
@techreport{LohRaffeckGeissleretal.2023,
  type      = {Working Paper},
  author    = {Loh, Frank and Raffeck, Simon and Geißler, Stefan and Hoßfeld, Tobias},
  title     = {Paving the Way for an Energy Efficient and Sustainable Future Internet of Things},
  series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  journal   = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)},
  doi       = {10.25972/OPUS-32216},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322161},
  pages     = {4},
  year      = {2023},
  abstract  = {In this work, we describe the network from data collection to data processing and storage as a system based on different layers. We outline the different layers and highlight major tasks and dependencies with regard to energy consumption and energy efficiency. With this view, we can outwork challenges and questions a future system architect must answer to provide a more sustainable, green, resource friendly, and energy efficient application or system. Therefore, all system layers must be considered individually but also altogether for future IoT solutions. This requires, in particular, novel sustainability metrics in addition to current Quality of Service and Quality of Experience metrics to provide a high power, user satisfying, and sustainable network.},
  language  = {en}
}
@article{LohPoigneeWamseretal.2021,
  author    = {Loh, Frank and Poign{\´e}e, Fabian and Wamser, Florian and Leidinger, Ferdinand and Hoßfeld, Tobias},
  title     = {Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming},
  series = {Sensors},
  volume    = {21},
  journal   = {Sensors},
  number    = {12},
  issn      = {1424-8220},
  doi       = {10.3390/s21124172},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-241121},
  year      = {2021},
  abstract  = {Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.},
  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}
}
@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}
}