Publications - Pervasive Computing Laboratory

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2015
 Rodriguez-Carrion, Alicia;  García-Rubio, Carlos;  Campo-Vázquez, Celeste;  Das, Sajal
Analysis of a fast LZ-based entropy estimator for mobility data Conference 
2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), IEEE Publishing Services , 2015, ISBN: 978-1-4799-8425-1.
Abstract | Links | BibTeX | Tags: emrisco, entropy, lz, mobility data
@conference{campo017b,

title = {Analysis of a fast LZ-based entropy estimator for mobility data},

author = { Alicia Rodriguez-Carrion and Carlos García-Rubio and Celeste Campo-Vázquez and Sajal Das },

doi = {https://doi.org/10.1109/percomw.2015.7134080},

isbn = {978-1-4799-8425-1},

year  = {2015},

date = {2015-06-29},

urldate = {2015-06-29},

booktitle = {2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)},

pages = {451-456},

publisher = {IEEE Publishing Services },

abstract = {Randomness in people's movements might serve to detect behavior anomalies. The concept of entropy can be used for this purpose, but its estimation is computational intensive, particularly when processing long movement histories. Moreover, disclosing such histories to third parties may violate user privacy. With a goal to keep the mobility data in the mobile device itself yet being able to measure randomness, we propose three fast entropy estimators based on Lempel-Ziv (LZ) prediction algorithms. We evaluated them with 95 movement histories of real users tracked during 9 months using GSM-based mobility data. The results show that the entropy tendencies of the approaches proposed in this work and those in the literature are the same as time evolves. Therefore, our proposed approach could potentially detect variations in the mobility patterns of the user with a lower computational cost. This allows to unveil shifts in the users mobility behavior without disclosing their sensible location data.},

keywords = {emrisco, entropy, lz, mobility data},

pubstate = {published},

tppubtype = {conference}

}

Close
Randomness in people's movements might serve to detect behavior anomalies. The concept of entropy can be used for this purpose, but its estimation is computational intensive, particularly when processing long movement histories. Moreover, disclosing such histories to third parties may violate user privacy. With a goal to keep the mobility data in the mobile device itself yet being able to measure randomness, we propose three fast entropy estimators based on Lempel-Ziv (LZ) prediction algorithms. We evaluated them with 95 movement histories of real users tracked during 9 months using GSM-based mobility data. The results show that the entropy tendencies of the approaches proposed in this work and those in the literature are the same as time evolves. Therefore, our proposed approach could potentially detect variations in the mobility patterns of the user with a lower computational cost. This allows to unveil shifts in the users mobility behavior without disclosing their sensible location data.
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doi:https://doi.org/10.1109/percomw.2015.7134080
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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
2011
 Rodriguez-Carrion, Alicia;  García-Rubio, Carlos;  Campo-Vázquez, Celeste;  Cortés, Alberto;  García-Lozano, Estrella;  Noriega-Vivas, Patricia
Analysis of Location Prediction Performance of LZ Algorithms using GSM Cell-based Location Data Conference 
5th International Symposium on Ubiquitous Computing and Ambient Intelligence (UCAMI 2011), UCAMI, 2011.
Abstract | Links | BibTeX | Tags: españavirtual, GSM, gsm-based location, lz, LZ Algorithms, prediction, predictions
@conference{campo024,

title = {Analysis of Location Prediction Performance of LZ Algorithms using GSM Cell-based Location Data},

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

url = {https://hdl.handle.net/10016/13103

https://e-archivo.uc3m.es/bitstreams/66c1a251-8ce0-4a0f-b342-86a8b45e60e8/download},

year  = {2011},

date = {2011-12-20},

urldate = {2011-12-20},

booktitle = {5th International Symposium on Ubiquitous Computing and Ambient Intelligence (UCAMI 2011)},

publisher = {UCAMI},

abstract = {Predictions about users' next locations allow bringing forward their future context, thus having additional time to react. To make such predictions, algorithms capable of learning mobility patterns and estimating the next location are needed. This work is focused on making the predictions on mobile terminals, thus resource consumption being an important constraint. Among the predictors with low resource consumption, the family of LZ algorithms has been chosen to study their performance, analyzing the results drawn from processing location records of 95 users. The main contribution is to divide the algorithms into two phases, thus being possible to use the best combination to obtain better prediction accuracy or lower resource consumption.},

keywords = {españavirtual, GSM, gsm-based location, lz, LZ Algorithms, prediction, predictions},

pubstate = {published},

tppubtype = {conference}

}

Close
Predictions about users' next locations allow bringing forward their future context, thus having additional time to react. To make such predictions, algorithms capable of learning mobility patterns and estimating the next location are needed. This work is focused on making the predictions on mobile terminals, thus resource consumption being an important constraint. Among the predictors with low resource consumption, the family of LZ algorithms has been chosen to study their performance, analyzing the results drawn from processing location records of 95 users. The main contribution is to divide the algorithms into two phases, thus being possible to use the best combination to obtain better prediction accuracy or lower resource consumption.
Close
https://hdl.handle.net/10016/13103
https://e-archivo.uc3m.es/bitstreams/66c1a251-8ce0-4a0f-b342-86a8b45e60e8/downlo[...]
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2010
 Rodriguez-Carrion, Alicia;  García-Rubio, Carlos;  Campo-Vázquez, Celeste
Performance Evaluation of LZ-Based Location Prediction Algorithms in Cellular Networks Journal Article 
In: IEEE COMMUNICATIONS LETTERS, vol. 14, iss. 8, pp. 707-709, 2010, ISSN: 1089-7798.
Abstract | Links | BibTeX | Tags: cellular networks, location, lz, prediction
@article{campo011,

title = {Performance Evaluation of LZ-Based Location Prediction Algorithms in Cellular Networks},

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

doi = {https://doi.org/10.1109/lcomm.2010.08.092033},

issn = {1089-7798},

year  = {2010},

date = {2010-08-09},

urldate = {2010-08-09},

journal = {IEEE COMMUNICATIONS LETTERS},

volume = {14},

issue = {8},

pages = {707-709},

abstract = {In mobile phones, it is useful to know the most probable next location to make decisions about future actions. In this letter we compare three LZ based prediction algorithms. The originality of our work is that we make it in a cellular network, we separate the algorithms into two independent phases (tree updating and probability calculation), we have included Active LeZi in the study, and we evaluate hit rate and resource consumption, including processing time.},

keywords = {cellular networks, location, lz, prediction},

pubstate = {published},

tppubtype = {article}

}

Close
In mobile phones, it is useful to know the most probable next location to make decisions about future actions. In this letter we compare three LZ based prediction algorithms. The originality of our work is that we make it in a cellular network, we separate the algorithms into two independent phases (tree updating and probability calculation), we have included Active LeZi in the study, and we evaluate hit rate and resource consumption, including processing time.
Close
doi:https://doi.org/10.1109/lcomm.2010.08.092033
Close