Computer Science and Information Technology Vol. 3(3), pp. 81 - 89
DOI: 10.13189/csit.2015.030305
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Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks

Cristian-Liviu Leca ^*, Ioan Nicolaescu , Cristian-Iulian Rîncu
Department of Communications and Military Electronic Systems, Faculty of Military Electronic and Information Systems, Military Technical Academy, Romania

ABSTRACT

Location services and applications, based on network data or global positioning systems, are greatly influencing and changing the way people use mobile phone networks by improving not only user-applications but also the network management part. These applications and services can be further developed by introducing location prediction. We design a system that logs cell id and timestamp data from the users' mobile device, detects the significance of the location to the user, such as home and workplace, and predicts future locations over a chosen time period using artificial neural networks. A novel method is designed for location detection that automatically determines the significance of the location to the user, by spatial and temporal analysis. In our approach, the neural network is automatically adapted, with the help of the location detection algorithm, to the period of the week for which a prediction is desired, achieving accurate weekday and weekend location prediction.

KEYWORDS
Artificial Neural Network, Location Detection, Prediction, Network Management, Open Cell ID

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Cristian-Liviu Leca , Ioan Nicolaescu , Cristian-Iulian Rîncu , "Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks," Computer Science and Information Technology, Vol. 3, No. 3, pp. 81 - 89, 2015. DOI: 10.13189/csit.2015.030305.

(b). APA Format:
Cristian-Liviu Leca , Ioan Nicolaescu , Cristian-Iulian Rîncu (2015). Significant Location Detection & Prediction in Cellular Networks using Artificial Neural Networks. Computer Science and Information Technology, 3(3), 81 - 89. DOI: 10.13189/csit.2015.030305.