Context compression: using principal component analysis for efficient wireless communications

Anagnostopoulos, T., Anagnostopoulos, C. and Hadjiefthymiades, S. (2011) Context compression: using principal component analysis for efficient wireless communications. In: IEEE 12th International Conference on Mobile Data Management, Lulea, Sweden, 6-9 June 2011. IEEE, pp. 27-30. ISBN 9781457705816 (doi: 10.1109/MDM.2011.60)

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Mobile applications are required to operate in highly dynamic pervasive computing environments of dynamic nature and predict the location of mobile users in order to act proactively. We focus on the location prediction and propose a new model/framework. Our model is used for the classification of the spatial trajectories through the adoption of Machine Learning (ML) techniques. Predicting location is treated as a classification problem through supervised learning. We perform the performance assessment of our model through synthetic and real-world data. We monitor the important metrics of prediction accuracy and training sample size.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos
Authors: Anagnostopoulos, T., Anagnostopoulos, C., and Hadjiefthymiades, S.
College/School:College of Science and Engineering > School of Computing Science

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