In daily life, we use numerous real-time services, such as health or fitness apps. These services provide valuable information based on data collected, e.g. using sensors of smartwatches or smartphones (mobile devices). An example of such a sensor is the accelerometer, which obtains information such as acceleration, speed, and distance. However, the sensor can also be used to determine information, such as the weight, height, and gender of the person wearing the device [Hajihassnai et al., 2021].
- This Master Thesis aims to create an ontology of the sensors built into mobile
- devices and the information that can be derived from them. Therefore: You will familiarise yourself with the topic of sensors,
- You will use literature, source code repositories, etc., to determine which information can be derived from the sensor,
- You should try to prototypically implement examples of deriving information from these sensors,
- And finally, you will create an ontology from this information.
Required/Recommended Qualification:
The candidate:
• Should have some knowledge about ontologies,
• Should have experience with the Android platform,
• Has interest or experience with Sensors,
• Has interests in data science.
Contact persons:
• Maik Benndorf (maikb@ifi.uio.no)
• Thomas Plagemann (plagemann@ifi.uio.no)
Resources
Parrot: This research project is associated with the Parrot project (Privacy Engineering for Real-Time Analytics in Human-Centered Internet of Things).
• Project Site: [UiO - Institutt for informatikk. (2020, June 24). Parrot: Privacy Engineering for Real-Time Analytics in Human-Centered Internet of Things. https://www.mn.uio.no/ifi/english/research/projects/parrot/index.html (Retrieved 7 September 2023)]
• Vision of Parrot: [Plagemann, T., Goebel, V., Hollick, M., & Koldehofe, B. (2022). Towards Privacy Engineering for Real-Time Analytics in the Human-Centered Internet of Things (arXiv:2210.16352). arXiv. http://arxiv.org/abs/2210.16352 (Retrieved 7 September 2023)]
Ontology:
• [Hofweber, T. (2004). Logic and Ontology. https://plato.stanford.edu/entries/ logic-ontology/ (Retrieved 7 September 2023)]
• [Jones, D., Bench-Capon, T., & Visser, P. (1998). Methodologies for Ontology Development.]
• [Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods, and applications. The Knowledge Engineering Review, 11(2), 93–136. https://doi.org/10.1017/S0269888900007797]
Sensor:
• Android Sensor Overview: [Google LLC. (n.d.). Sensors Overview. Android Developers. https://developer.android.com/guide/topics/sensors/ sensors_overview (Retrieved 7 September 2023)]
• Sensor Inference:
– [Kröger, J. (2019). Unexpected inferences from sensor data: a hidden privacy threat in the internet of things. In Internet of Things. Information Processing in an Increasingly Connected World: First IFIP International Cross-Domain Conference, IFIPIoT 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 18-19, 2018, Revised Selected Papers 1 (pp. 147-159). Springer International Publishing.]
– [Hajihassnai, O., Ardakanian, O., & Khazaei, H. (2021). Obscurenet: Learning attribute-invariant latent representation for anonymizing sensor data. Proceedings of the International Conference on Internet of Things Design and Implementation, 40–52.]
– [Moqurrab, S. A., Naeem, T., Shoaib Malik, M., Fayyaz, A. A., Jamal, A., & Srivastava, G. (2023). UtilityAware: A framework for data privacy protection in e-health. Information Sciences, 643, 119247. https://doi.org/10.1016/j.ins.2023.119247]