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Mobile Information Systems
Volume 9, Issue 3, Pages 189-208
http://dx.doi.org/10.3233/MIS-130154

Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra

Najmeh Neysani Samany,1 Mahmoud Reza Delavar,2 Nicholas Chrisman,3 and Mohammad Reza Malek4

1Department of Surveying and Geomatics Engineering, College of Engineering, University of Tehran, Tehran, Iran
2Center of Exellence in Geomatic Engineering in Disaster Management, Department of Serveying and Geomatic Engineering, College of Engineering, University of Tehran, Tehran, Iran
3Department of Geomatic Science, Laval University, Québec, QC, Canada
4Department of GIS, Faculty of Geodesy and Geomatic Engineering, K.N. Toosi University of Technology, Tehran, Iran

Received 11 March 2013; Accepted 11 March 2013

Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Space and time are two dominant factors in context-aware pervasive systems which determine whether an entity is related to the moving user or not. This paper specifically addresses the use of spatio-temporal relations for detecting spatio-temporally relevant contexts to the user. The main contribution of this work is that the proposed model is sensitive to the velocity and direction of the user and applies customized Multi Interval Algebra (MIA) with Voronoi Continuous Range Query (VCRQ) to introduce spatio-temporally relevant contexts according to their arrangement in space. In this implementation the Spatio-Temporal Relevancy Model for Context-Aware Systems (STRMCAS) helps the tourist to find his/her preferred areas that are spatio-temporally relevant. The experimental results in a scenario of tourist navigation are evaluated with respect to the accuracy of the model, performance time and satisfaction of users in 30 iterations of the algorithm. The evaluation process demonstrated the efficiency of the model in real-world applications.