By Jordi Nin, Daniel Villatoro
This e-book constitutes the completely refereed post-conference lawsuits of the 1st foreign Workshop on Citizen Sensor Networks, CitiSens 2012, in Montpellier, France, on August 27, 2012.
The 7 revised complete papers offered including 1 keynote lectures have been conscientiously reviewed and chosen from sixteen submissions. The permitted papers care for themes like crowdsourcing, shrewdpermanent towns, multi-agent platforms, privateness in social networks, facts anonymity or shrewdpermanent sensors.
Read Online or Download Citizen in Sensor Networks: First International Workshop, CitiSens 2012, Montpellier, France, August 27, 2012, Revised Selected Papers PDF
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Additional info for Citizen in Sensor Networks: First International Workshop, CitiSens 2012, Montpellier, France, August 27, 2012, Revised Selected Papers
Del C. Delgado-Roman, M. Pujol-Gonzalez, and C. 1 0 0 50 100 150 Time (weeks) Fig. 2. Network remaining energy ratio Finally, to completely deﬁne the experimental setup, Table 2 presents the values assigned to the COSA algorithm parameters as well as the sampling frequency demanded to the system. The number of nodes considered is set to 50 and their deployment along the river course is assumed to follow a regular chain distribution. Every node is situated in the middle of the river section considered and evenly spaced.
Among this information, the spatial and temporal The TweetBeat of the City 45 coordinates of the users have been a key aspect. The temporal description of microblogging posts or social media in general, together with a sentiment analysis can be used to anticipate events of any sort, like the commercial success of a movie  or even the stock market . The spatial description of social media can be used to detect points of interest, like geolocated flickr photos for the tourist case in , which actually reaveal trajectories when it combines this information with the time stamps.
Torra Table 1. 55 dataset obtained, as expected, a very bad results with a 99% of disclosure risk and a 0% of information loss. This is very bad because it means that almost all the users are exposed to the disclosure of their sensible information. However, if we take a look at the different protections results we can see that measures are more reduced and balanced. Fig. 3. Dispersion plot of the protected and original microdata evaluation results Figure 3 shows the obtained results graphically.