The Accuracy of Location Prediction Algorithms Based on Markovian Mobility Models

The Accuracy of Location Prediction Algorithms Based on Markovian Mobility Models

Péter Fülöp (Budapest University of Technology and Economics, Hungary), Sándor Imre (Budapest University of Technology and Economics, Hungary), Sándor Szabó (Budapest University of Technology and Economics, Hungary) and Tamás Szálka (Budapest University of Technology and Economics, Hungary)
DOI: 10.4018/978-1-60960-563-6.ch008
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Abstract

The efficient dimensioning of cellular wireless access networks depends highly on the accuracy of the underlying mathematical models of user distribution and traffic estimations. The optimal placement/deployment of e.g. UMTS, IEEE 802.16 WiMAX base stations or IEEE 802.11 WLAN access points is based on user distribution and traffic characteristics in the service area. In this paper we focus on the tradeoff between the accuracy and the complexity of the mathematical models used to describe user movements in the network. We propose a novel Markov chain based model capable of utilizing user’s movement history thus providing more accurate results than other models in the literature. The new model is applicable in real-life scenarios, because it relies on information effectively available in cellular networks (e.g. handover history). The complexity of the proposed model is analyzed, and the accuracy is justified by means of simulation.
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2 Random Walk Model Improvements

Our work is based on the utilization of time-series of mobile users’ movement patterns in cellular mobile networks. In our work we assume that there is a given trace of a mobile service provider’s network history. This dataset consists of all signals that were transferred in the network in the examined time interval. Beside many other network parameters and properties, two main information sets can be recovered:

  • the cell-path that each user visited before

  • the time intervals users have spent in each cell

The series of visited cells is crucial to analyze the similarities in the users’ motion. Based on the motion patterns of the terminals amongst the cells, we can describe some drifts of the users’ motion in a given cell or point. A drift may be caused by geographical or infrastructural objects (like highways, etc.) or some time-dependent circumstances (like mass events, concert, football matches, etc.) (Jardosh, Belding-Royer, Almeroth, & Suri, 2003; McDonald, & Znati, 2000).

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