Evaluating Regression Models for Temporal Prediction of Wi-Fi Device Mobility
| Autor: | Abdessamed Sassi, Abdelmalik Bachir, Walid Bechkit |
|---|---|
| Přispěvatelé: | Université Mohamed Khider de Biskra (BISKRA), Université Larbi-Ben-Mhidi [Oum-El-Bouaghi] (OEB), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria), ALGorithmes et Optimisation pour Réseaux Autonomes (AGORA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Transportation planning
Mobility model Computer science Human mobility models Probabilistic logic 020206 networking & telecommunications Regression analysis 02 engineering and technology computer.software_genre Computer Science Applications [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] 11. Sustainability Linear regression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Residence time prediction Electrical and Electronic Engineering Residence time (statistics) computer WiFi mobility traces |
| Zdroj: | Wireless Personal Communications Wireless Personal Communications, 2021, 116 (1), pp.2169-2186. ⟨10.1007/s11277-020-07785-2⟩ Wireless Personal Communications, Springer Verlag, 2021, 116 (1), pp.2169-2186. ⟨10.1007/s11277-020-07785-2⟩ |
| ISSN: | 0929-6212 1572-834X |
| DOI: | 10.1007/s11277-020-07785-2⟩ |
| Popis: | International audience; The ability to predict the arrival and residence time of mobile users at a particular place is essential for the development of a wealth of new applications and services, such as smart heating control, transportation planning or urban navigation. Previous techniques based on probabilistic models have not been able to perform such prediction accurately. In this paper, we present two linear mobility models, namely Linear Regression, and Auto-Regression, to predict the temporal behavior, particularly the residence time, of individual users. We run performance evaluation experiments on two different WiFi mobility traces datasets made available through the CRAWDAD project. Our results show that using linear regression-based learning algorithms significantly improve the residence time prediction accuracy compared to state-of-the-art methods, and achieve prediction errors in the order of seconds and minutes for a large number of users. |
| Databáze: | OpenAIRE |
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