Towards Highly Performant Context Awareness in the Internet of Things

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


Context awareness and adaption are an important requirement for data streaming applications in the Internet of Things (IoT). Concept drift arises under changing context and many solutions for drift detection and system adaption have been developed in the past. Even though the computing resources of these approaches are not negligible, performance aspects, e.g. runtime or memory usage and scalability have not been investigated adequately. The goal of this thesis is to fill this gap and to provide a mean for performance investigations of concept drift handling methods and other approaches that are developed in the research field Data Science. Therefore performance benchmarking of state-of-the-art solutions is conducted. Moreover the thesis discusses performance bottlenecks of the approaches and demonstrates possible improvements based on the knowledge gained by leveraging tools and methods from the performance analysis domain. Approaches are then implemented into an IoT application and deployed with stream processing engines for a final evaluation.


Original languageEnglish
Title of host publicationDistributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference
EditorsJosé Manuel Machado, Pablo Chamoso, Guillermo Hernández, Grzegorz Bocewicz, Roussanka Loukanova, Roussanka Loukanova, Esteban Jove, Angel Martin del Rey, Michela Ricca
PublisherSpringer Link
Number of pages6
Publication statusPublished - Feb 2023

External IDs

Scopus 85149683892
Mendeley 8558def1-e3fa-35ae-9662-92aa8ac4b7db



  • Concept drift detection, Performance analysis, IoT