Contextual location prediction using spatio-temporal clustering. Issue 3 (5th September 2016)

• Record Type: Journal Article
• Authors: Guessoum, Djamel; Miraoui, Moeiz; Tadj, Chakib
• Is Part Of: International journal of pervasive computing and communications. Volume 12:Issue 3(2016)
• Page Start: 290
• Page End: 309
• Publication Date: 2016-09-05
• Subjects: Context-awareness -- Pervasive computing -- Clustering -- DBSCAN -- Location prediction; Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-05-2016-0027
• Languages: English
• ISSNs: 1742-7371

Abstract

Abstract : Purpose: The prediction of a context, especially of a user's location, is a fundamental task in the field of pervasive computing. Such predictions open up a new and rich field of proactive adaptation for context-aware applications. This study/paper aims to propose a methodology that predicts a user's location on the basis of a user's mobility history. Design/methodology/approach: Contextual information is used to find the points of interest that a user visits frequently and to determine the sequence of these visits with the aid of spatial clustering, temporal segmentation and speed filtering. Findings: The proposed method was tested with a real data set using several supervised classification algorithms, which yielded very interesting results. Originality/value: The method uses contextual information (current position, day of the week, time and speed) that can be acquired easily and accurately with the help of common sensors such as GPS.