Publications - Pervasive Computing Laboratory

Access control anomaly detection anotacionsemantica authentication Cloud computing coap compromise cryptography cynamon emadrid emrisco home network I-Shaper identity management inrisco inteligenciafuentesabiertas Internet of Things IoT magos Pervasive computing privacy Protection Protocols Qursa raudo2 Security Smart Card Trust management Ubisec VANETs 
Show all
2012
 Rodriguez-Carrion, Alicia;  García-Rubio, Carlos;  Campo-Vázquez, Celeste;  Cortés, Alberto;  García-Lozano, Estrella
Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems Journal Article 
In: Sensors , vol. 12, iss. 6, pp. 7496-7517, 2012, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: active lezi, Ambient intelligence, españavirtual, gsm-based location, lezi update, lz, prediction, recommender system, Ubiquitous computing
@article{campo009b,

title = {Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems},

author = {Alicia Rodriguez-Carrion and Carlos García-Rubio and Celeste Campo-Vázquez and Alberto Cortés and Estrella García-Lozano },

url = {http://hdl.handle.net/10016/27902},

doi = {https://doi.org/10.3390/s120607496},

issn = {1424-8220},

year  = {2012},

date = {2012-06-04},

urldate = {2012-06-04},

journal = {Sensors },

volume = {12},

issue = {6},

pages = {7496-7517},

abstract = {Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.},

keywords = {active lezi, Ambient intelligence, españavirtual, gsm-based location, lezi update, lz, prediction, recommender system, Ubiquitous computing},

pubstate = {published},

tppubtype = {article}

}

Close
Predicting users' next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi) capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.
Close
http://hdl.handle.net/10016/27902
doi:https://doi.org/10.3390/s120607496
Close