@phdthesis{Lange2019,
  author    = {Lange, Stanislav},
  title     = {Optimization of Controller Placement and Information Flow in Softwarized Networks},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-17457},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-174570},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {The Software Defined Networking (SDN) paradigm offers network operators numerous improvements in terms of flexibility, scalability, as well as cost efficiency and vendor independence. However, in order to maximize the benefit from these features, several new challenges in areas such as management and orchestration need to be addressed. This dissertation makes contributions towards three key topics from these areas. Firstly, we design, implement, and evaluate two multi-objective heuristics for the SDN controller placement problem. Secondly, we develop and apply mechanisms for automated decision making based on the Pareto frontiers that are returned by the multi-objective optimizers. Finally, we investigate and quantify the performance benefits for the SDN control plane that can be achieved by integrating information from external entities such as Network Management Systems (NMSs) into the control loop. Our evaluation results demonstrate the impact of optimizing various parameters of softwarized networks at different levels and are used to derive guidelines for an efficient operation.},
  subject      = {Leistungsbewertung},
  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}
}