Publications - Pervasive Computing Laboratory

Access control anotacionsemantica authentication Cloud computing coap compromise cryptography cynamon emadrid emrisco home network I-Shaper identity management inrisco inteligenciafuentesabiertas Internet of Things intrusion detection system IoT magos middleware Pervasive computing privacy Protocols Qursa raudo2 Security Smart Card Trust management Ubisec VANETs 
Show all
2020
 Diaz-Redondo, Rebeca;  García-Rubio, Carlos;  Campo-Vázquez, Ana Fernandez-Vilas Celeste;  Rodriguez-Carrion, Alicia
A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics Journal Article 
In: Future generation computer systems, vol. 109, pp. 83-94, 2020, ISSN: 0167-739X.
Abstract | Links | BibTeX | Tags: crowd dynamics, density-based clustering, emadrid, entropy analysis, instagram, location-based social network
@article{campo005,

title = {A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics},

author = {Rebeca Diaz-Redondo and Carlos García-Rubio and Ana Fernandez-Vilas Celeste Campo-Vázquez and Alicia Rodriguez-Carrion},

url = {http://hdl.handle.net/10016/33771},

doi = {https://doi.org/10.1016/j.future.2020.03.038},

issn = {0167-739X},

year  = {2020},

date = {2020-08-10},

urldate = {2020-08-10},

journal = {Future generation computer systems},

volume = {109},

pages = {83-94},

abstract = {Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results.},

keywords = {crowd dynamics, density-based clustering, emadrid, entropy analysis, instagram, location-based social network},

pubstate = {published},

tppubtype = {article}

}

Close
Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results.
Close
http://hdl.handle.net/10016/33771
doi:https://doi.org/10.1016/j.future.2020.03.038
Close