@book{TranGiaHossfeld2021,
  author    = {Tran-Gia, Phuoc and Hoßfeld, Tobias},
  title     = {Performance Modeling and Analysis of Communication Networks},
  edition   = {1st edition},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-152-5},
  doi       = {10.25972/WUP-978-3-95826-153-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-241920},
  publisher      = {W{\"u}rzburg University Press},
  pages     = {xiii, 353},
  year      = {2021},
  abstract  = {This textbook provides an introduction to common methods of performance modeling and analysis of communication systems. These methods form the basis of traffic engineering, teletraffic theory, and analytical system dimensioning. The fundamentals of probability theory, stochastic processes, Markov processes, and embedded Markov chains are presented. Basic queueing models are described with applications in communication networks. Advanced methods are presented that have been frequently used in recent practice, especially discrete-time analysis algorithms, or which go beyond classical performance measures such as Quality of Experience or energy efficiency. Recent examples of modern communication networks include Software Defined Networking and the Internet of Things. Throughout the book, illustrative examples are used to provide practical experience in performance modeling and analysis. Target group: The book is aimed at students and scientists in computer science and technical computer science, operations research, electrical engineering and economics.},
  language  = {en}
}
@article{HirthSeufertLangeetal.2021,
  author    = {Hirth, Matthias and Seufert, Michael and Lange, Stanislav and Meixner, Markus and Tran-Gia, Phuoc},
  title     = {Performance evaluation of hybrid crowdsensing and fixed sensor systems for event detection in urban environments},
  series = {Sensors},
  volume    = {21},
  journal   = {Sensors},
  number    = {17},
  issn      = {1424-8220},
  doi       = {10.3390/s21175880},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245245},
  year      = {2021},
  abstract  = {Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.},
  language  = {en}
}