Sarkar, Rik

Marina, Mahesh

Katsikouli, Panagiota

2018-06-07T13:21:37Z

2018-06-07T13:21:37Z

2018-07-02

Smart-phones, wearables and mobile devices in general are the sensors of our modern world. Their sensing capabilities offer the means to analyze and interpret our behaviour and surroundings. When it comes to human behaviour, perhaps the most informative feature is our location and mobility habits. Insights from human mobility are useful in a number of everyday practical applications, such as the improvement of transportation and road network infrastructure, ride-sharing services, activity recognition, mobile data pre-fetching, analysis of the social behaviour of humans, etc. In this dissertation, we develop algorithms for processing mobility data. The analysis of mobility data is a non trivial task as it involves managing large quantities of location information, usually spread out spatially and temporally across many tracking sensors. An additional challenge in processing mobility information is to publish the data and the results of its analysis without jeopardizing the privacy of the involved individuals or the quality of the data. We look into a series of problems on processing mobility data from individuals and from a population. Our mission is to design algorithms with provable properties that allow for the fast and reliable extraction of insights. We present efficient solutions - in terms of storage and computation requirements - , with a focus on distributed computation, online processing and privacy preservation.

http://hdl.handle.net/1842/31110

en

The University of Edinburgh

Katsikouli, P., Ghosh, A., and Sarkar, R. (2017). Density and privacy preserving publication of location datasets. INFOCOM

Katsikouli, P., Sarkar, R., and Gao, J. (2014). Persistence based online signal and trajectory simpli cation for mobile devices. In Proceedings of the 22Nd ACM SIGSPATIAL International Conference on Advances in Geographic In- formation Systems, SIGSPATIAL '14, pages 371-380, New York, NY, USA. ACM.

Katsikouli, P., Viana, A. C., Fiore, M., and Tarable, A. (2017). On the sampling frequency of human mobility. In Proceedings of the IEEE Global Communications Conference (GLOBECOM), 2017

Radu, V., Katsikouli, P., Sarkar, R., and Marina, M. K. (2014). A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. Proceeding of The 12th ACM Conference on Embedded Networked Sensor Systems (SenSys).

2100-12-31

location data

mobility data

analysis

algorithmic solutions

sampling frequency

distributed algorithms

Distributed and privacy preserving algorithms for mobility information processing

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy

Restricted Access