TY - GEN
T1 - Combining Context-Awareness and Data Analytics in Support of Drone Technology
AU - Shishkov, Boris
AU - Ivanova, Krassimira
AU - Verbraeck, Alexander
AU - van Sinderen, Marten
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2022
Y1 - 2022
N2 - Drones performing an autonomous mission need to adapt to frequent changes in their environment. In other words, they have to be context-aware. Most current context-aware systems are designed to distinguish between situations that have been pre-defined in terms of anticipated situation types and corresponding desired behavior types. This only partially benefits drone technology because many types of drone missions can be characterized by situations that are hard to predict at design time. We suggest combining context-awareness and data analytics for a better situation coverage. This could be achieved by using performance data (generated at real-time) as training data for supervised machine learning – it would allow relating situations to appropriate behaviors that a drone could follow. The conceptual ideas are presented in this position paper while validation is left for future work.
AB - Drones performing an autonomous mission need to adapt to frequent changes in their environment. In other words, they have to be context-aware. Most current context-aware systems are designed to distinguish between situations that have been pre-defined in terms of anticipated situation types and corresponding desired behavior types. This only partially benefits drone technology because many types of drone missions can be characterized by situations that are hard to predict at design time. We suggest combining context-awareness and data analytics for a better situation coverage. This could be achieved by using performance data (generated at real-time) as training data for supervised machine learning – it would allow relating situations to appropriate behaviors that a drone could follow. The conceptual ideas are presented in this position paper while validation is left for future work.
KW - Context-awareness
KW - Data analytics
KW - Drone technology
UR - http://www.scopus.com/inward/record.url?scp=85145183814&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-23226-8_4
DO - 10.1007/978-3-031-23226-8_4
M3 - Conference contribution
AN - SCOPUS:85145183814
SN - 9783031232251
T3 - Communications in Computer and Information Science
SP - 51
EP - 60
BT - Telecommunications and Remote Sensing - 11th International Conference, ICTRS 2022, Proceedings
A2 - Shishkov, Boris
A2 - Lazarov, Andon
PB - Springer
T2 - 11th International Conference on Telecommunications and Remote Sensing, ICTRS 2022
Y2 - 21 November 2022 through 22 November 2022
ER -